Tag Archives: AWS Key Management Service*

Securing Amazon ECS workloads on AWS Fargate with customer managed keys

Post Syndicated from Maish Saidel-Keesing original https://aws.amazon.com/blogs/compute/securing-amazon-ecs-workloads-on-aws-fargate-with-customer-managed-keys/

As Amazon CTO Werner Vogels said, “Encryption is the tool we have to make sure that nobody else has access to your data. Amazon Web Services (AWS) built encryption into nearly all of its 165 cloud services. Make use of it. Dance like nobody is watching. Encrypt like everyone is.”

Security is the top priority at AWS, underpinning everything we do. With AWS Fargate, every Amazon Elastic Container Service (Amazon ECS) task is launched on to a new single use, single tenant unit of compute. The ephemeral storage for this compute is always encrypted, and the AWS Key Management Service (AWS KMS) encryption key used for this encryption is managed by AWS Fargate.

Today, AWS is announcing that you can bring your own customer managed keys (CMKs). Once added to AWS KMS, you can use these to encrypt the underlying ephemeral storage of an Amazon ECS task on AWS Fargate. With this new capability, customers operating in heavily regulated environments can now have more control and visibility into their task’s ephemeral storage encryption.

This post dives into AWS Fargate task ephemeral storage and shows how the new customer managed key (CMK) feature can be enabled and audited.


AWS Fargate is a serverless compute engine for containerized workloads running on Amazon ECS and Amazon Elastic Kubernetes Service (Amazon EKS). Each time a new piece of work is scheduled on to AWS Fargate, as an Amazon ECS task or an Amazon EKS Pod, this workload is placed on a single use, single-tenant instance of compute.

For Amazon ECS tasks, that unit of compute has 20GiBs of ephemeral storage attached. This can be increased up to 200GiB by specifying the ephemeralStorage parameter in your task definition. This ephemeral storage is bound to the lifecycle of the Amazon ECS task, and once the Amazon ECS task has stopped, along with the underlying compute, this ephemeral storage is deleted.

If you are using AWS Fargate platform version 1.4.0 or higher, this ephemeral storage volume is encrypted by default. It is encrypted using an AWS Key Management Service (KMS) key with the AES-256 encryption algorithm. The key, and its lifecycle, is owned by the AWS Fargate service. You can learn more about Fargate-managed ephemeral storage encryption in the AWS Fargate Security Whitepaper.

With today’s launch, as an alternative to the Fargate-managed encryption, you can choose to encrypt the ephemeral storage with customer managed keys (CMKs). This helps regulation-sensitive customers meet their internal security policies and regulatory requirements.

Customers can import their own existing keys into AWS KMS or create a new CMK to encrypt the ephemeral storage. CMKs used by AWS Fargate can be managed through the normal AWS KMS lifecycle actions such as being rotated, disabled, and deleted. See the Amazon ECS documentation for more details on managing the KMS key. Additionally, all access from AWS Fargate to the KMS key can be audited in AWS CloudTrail Logs.

In January 2024, AWS announced that additional Amazon Elastic Block Store (Amazon EBS) volumes can now be attached to Amazon ECS tasks running on AWS Fargate. These EBS volumes unlock additional use cases for AWS Fargate customers, using higher capacity and high-performance volumes for use in their tasks alongside the ephemeral storage. These additional EBS volumes are managed differently to the ephemeral storage, and these volumes can already be encrypted with customer managed KMS keys (CMKs).

AWS Fargate falls under the scope of the following compliance programs regarding AWS’s side of the shared responsibility model. The compliance programs covered by AWS Fargate include:

You can download third-party audit reports using AWS Artifact. For more information, see Downloading Reports in AWS Artifact. Many of these compliance programs require customers to encrypt their data at rest within their Amazon ECS on AWS Fargate resources.

Customers also have additional internal risk management policies for key handling, where they must generate their own keys, have backups for these keys off-cloud, and manage the lifecycle of these keys. Until today, these customers could not use AWS Fargate’s default encryption solution for the workloads subject to their internal security policies.

Enabling CMK for ephemeral storage on an Amazon ECS Cluster

Following today’s launch a single KMS key can now be attached to a new or existing Amazon ECS Cluster. Once a key has been attached, all new tasks launched on to AWS Fargate use this KMS key. If you have existing tasks running in the Amazon ECS cluster, they must be redeployed to use the new encryption key. If these tasks are part of an Amazon ECS service, passing the –force-new-deployment flag to an amazon ecs update-service command forces all tasks to be redeployed with the new KMS key (while respecting the minimumHealthyPercent of the service).

To attach a KMS key to a new or existing cluster, specify the KeyId to the new managedStorageConfiguration field:

aws ecs create-cluster \
  --cluster clusterName \
  --configuration '{"managedStorageConfiguration":{"fargateEphemeralStorageKmsKeyId":"arn:aws:kms:us-west-2:012345678901:key/a1b2c3d4-5678-90ab-cdef-EXAMPLE11111"}}'

Here is an example of the output of a DescribeClusters API request to an Amazon ECS cluster with a customer managed key:

aws ecs describe-clusters --clusters ecs-fargate-self-managed-key-cluster --region us-west-2 --include CONFIGURATIONS

Result of describe-clusters query

Aside from auditing CloudTrail Logs for encryption events, you can also verify that an ECS task is using the KMS key by using the DescribeTask API on an existing task:

    "tasks": [
            "clusterArn": "arn:aws:ecs:us-west-2:1234567890:cluster/mycluster",
            "taskArn": "arn:aws:ecs:us-west-2:1234567890:task/11223342-1111-4fde-b6ca-273c5cfc00a1]",
            "fargateEphemeralStorage": {
                "sizeInGiB": 20,
                "kmsKeyId": "arn:aws:kms:us-west-2:1234567890:key/082222a1-1111-4fde-b6ca-273c5cfc00a1"

Enforcing encryption with customer managed keys

The new AWS Identity and Access Management (IAM) condition key ensures that your Amazon ECS clusters are created with a customer managed key. This can be applied as Service Control Policy in your AWS Organization or as part of your IAM permissions.

Here is an IAM policy example snippet that ensures a cluster can only be created when a specific AWS KMS key is used:

  "Version": "2012-10-17",
  "Statement": [
      "Effect": "Allow",
      "Action": [
      "Resource": "*",
      "Condition": {
        "StringEquals": {
          "ecs:fargate-ephemeral-storage-kms-key": "arn:aws:kms:us-east-1:123456789012:key/1234abcd-12ab-34cd-56ef-1234567890ab"

Audit encryption events

Encryption events are logged in AWS CloudTrail. The following is an example of a CloudTrail event that includes the volume ID, cluster name, and AWS Account ID of the operation. You can find more details about the type of events that are logged in Managing AWS KMS keys for Fargate ephemeral storage.

    "eventVersion": "1.08",
    "userIdentity": {
        "type": "AWSService",
        "invokedBy": "ec2-frontend-api.amazonaws.com"
    "eventTime": "2024-04-23T18:08:13Z",
    "eventSource": "kms.amazonaws.com",
    "eventName": "CreateGrant",
    "awsRegion": "us-west-2",
    "sourceIPAddress": "ec2-frontend-api.amazonaws.com",
    "userAgent": "ec2-frontend-api.amazonaws.com",
    "requestParameters": {
        "keyId": "arn:aws:kms:us-west-2:123456789012:key/9b52b885-3f4d-40af-9843-d6b24b735559",
        "granteePrincipal": "fargate.us-west-2.amazonaws.com",
        "operations": [
        "constraints": {
            "encryptionContextSubset": {
                "aws:ecs:clusterAccount": "123456789012",
                "aws:ebs:id": "vol-01234567890abcdef",
                "aws:ecs:clusterName": "ecs-fargate-self-managed-key-cluster"
        "retiringPrincipal": "ec2.us-west-2.amazonaws.com"
    "responseElements": {
        "grantId": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
        "keyId": "arn:aws:kms:us-west-2:123456789012:key/9b52b885-3f4d-40af-9843-d6b24b735559"
    "requestID": "be4d1a4e4730e0dceca51f87ee7454d5db76400d80e22bfbf3c4ca01e893b60c",
    "eventID": "bf36027c-86bd-40f2-a561-960cbe148c4c",
    "readOnly": false,
    "resources": [
            "accountId": "AWS Internal",
            "type": "AWS::KMS::Key",
            "ARN": "arn:aws:kms:us-west-2:123456789012:key/9b52b885-3f4d-40af-9843-d6b24b735559"
    "eventType": "AwsApiCall",
    "managementEvent": true,
    "recipientAccountId": "123456789012",
    "sharedEventID": "bf36027c-86bd-40f2-a561-960cbe148c4c",
    "eventCategory": "Management"


With the use of AWS KMS customer managed keys, you can now meet your security requirements for your data inside your Amazon ECS workloads running on AWS Fargate.

To learn more about compliance on your Amazon ECS workloads you can reference the FSI Services Spotlight: Amazon Elastic Container Service (ECS) with AWS Fargate blog post or the security overview of AWS Fargate whitepaper. To learn more about the use of customer managed keys in AWS Fargate, refer to the AWS documentation. This feature was requested by our customers on the AWS Containers roadmap.

AWS Weekly Roundup: Anthropic’s Claude 3 Opus in Amazon Bedrock, Meta Llama 3 in Amazon SageMaker JumpStart, and more (April 22, 2024)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-anthropics-claude-3-opus-in-amazon-bedrock-meta-llama-3-in-amazon-sagemaker-jumpstart-and-more-april-22-2024/

AWS Summits continue to rock the world, with events taking place in various locations around the globe. AWS Summit London (April 24) is the last one in April, and there are nine more in May, including AWS Summit Berlin (May 15–16), AWS Summit Los Angeles (May 22), and AWS Summit Dubai (May 29). Join us to connect, collaborate, and learn about AWS!

While you decide which summit to attend, let’s look at the last week’s new announcements.

Last week’s launches
Last week was another busy one in the world of artificial intelligence (AI). Here are some launches that got my attention.

Anthropic’s Claude 3 Opus now available in Amazon Bedrock – After Claude 3 Sonnet and Claude 3 Haiku, two of the three state-of-the-art models of Anthropic’s Claude 3, Opus is now available in Amazon Bedrock. Cluade 3 Opus is at the forefront of generative AI, demonstrating comprehension and fluency on complicated tasks at nearly human levels. Like the rest of the Claude 3 family, Opus can process images and return text outputs. Claude 3 Opus shows an estimated twofold gain in accuracy over Claude 2.1 on difficult open-ended questions, reducing the likelihood of faulty responses.

Meta Llama 3 now available in Amazon SageMaker JumpStart – Meta Llama 3 is now available in Amazon SageMaker JumpStart, a machine learning (ML) hub that can help you accelerate your ML journey. You can deploy and use Llama 3 foundation models (FMs) with a few steps in Amazon SageMaker Studio or programmatically through the Amazon SageMaker Python SDK. Llama is available in two parameter sizes, 8B and 70B, and can be used to support a broad range of use cases, with improvements in reasoning, code generation, and instruction following. The model will be deployed in an AWS secure environment under your VPC controls, helping ensure data security.

Built-in SQL extension with Amazon SageMaker Studio Notebooks – SageMaker Studio’s JupyterLab now includes a built-in SQL extension to discover, explore, and transform data from various sources using SQL and Python directly within the notebooks. You can now seamlessly connect to popular data services and easily browse and search databases, schemas, tables, and views. You can also preview data within the notebook interface. New features such as SQL command completion, code formatting assistance, and syntax highlighting improve developer productivity. To learn more, visit Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks and the SageMaker Developer Guide.

AWS Split Cost Allocation Data for Amazon EKS – You can now receive granular cost visibility for Amazon Elastic Kubernetes Service (Amazon EKS) in the AWS Cost and Usage Reports (CUR) to analyze, optimize, and chargeback cost and usage for your Kubernetes applications. You can allocate application costs to individual business units and teams based on how Kubernetes applications consume shared Amazon EC2 CPU and memory resources. You can aggregate these costs by cluster, namespace, and other Kubernetes primitives to allocate costs to individual business units or teams. These cost details will be accessible in the CUR 24 hours after opt-in. You can use the Containers Cost Allocation dashboard to visualize the costs in Amazon QuickSight and the CUR query library to query the costs using Amazon Athena.

AWS KMS automatic key rotation enhancementsAWS Key Management Service (AWS KMS) introduces faster options for automatic symmetric key rotation. You can now customize rotation frequency between 90 days to 7 years, invoke key rotation on demand for customer-managed AWS KMS keys, and view the rotation history for any rotated AWS KMS key. There is a nice post on the Security Blog you can visit to learn more about this feature, including a little bit of history about cryptography.

Amazon Personalize automatic solution trainingAmazon Personalize now offers automatic training for solutions. With automatic training, you can set a cadence for your Amazon Personalize solutions to automatically retrain using the latest data from your dataset group. This process creates a newly trained machine learning (ML) model, also known as a solution version, and maintains the relevance of Amazon Personalize recommendations for end users. Automatic training mitigates model drift and makes sure recommendations align with users’ evolving behaviors and preferences. With Amazon Personalize, you can personalize your website, app, ads, emails, and more, using the same machine learning technology used by Amazon, without requiring any prior ML experience. To get started with Amazon Personalize, visit our documentation.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

We launched existing services and instance types in additional Regions:

Other AWS news
Here are some additional news that you might find interesting:

The PartyRock Generative AI Hackathon winners – The PartyRock Generative AI Hackathon concluded with over 7,650 registrants submitting 1,200 projects across four challenge categories, featuring top winners like Parable Rhythm – The Interactive Crime Thriller, Faith – Manga Creation Tools, and Arghhhh! Zombie. Participants showed remarkable creativity and technical prowess, with prizes totaling $60,000 in AWS credits.

I tried the Faith – Manga Creation Tools app using my daughter Arya’s made-up stories and ideas and the result was quite impressive.

Visit Jeff Barr’s post to learn more about how to try the apps for yourself.

AWS open source news and updates – My colleague Ricardo writes about open source projects, tools, and events from the AWS Community. Check out Ricardo’s page for the latest updates.

Upcoming AWS events
Check your calendars and sign up for upcoming AWS events:

AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Singapore (May 7), Seoul (May 16–17), Hong Kong (May 22), Milan (May 23), Stockholm (June 4), and Madrid (June 5).

AWS re:Inforce – Explore cloud security in the age of generative AI at AWS re:Inforce, June 10–12 in Pennsylvania for 2.5 days of immersive cloud security learning designed to help drive your business initiatives.

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Turkey (May 18), Midwest | Columbus (June 13), Sri Lanka (June 27), Cameroon (July 13), Nigeria (August 24), and New York (August 28).

You can browse all upcoming AWS led in-person and virtual events and developer-focused events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The curious case of faster AWS KMS symmetric key rotation

Post Syndicated from Jeremy Stieglitz original https://aws.amazon.com/blogs/security/the-curious-case-of-faster-aws-kms-symmetric-key-rotation/

Today, AWS Key Management Service (AWS KMS) is introducing faster options for automatic symmetric key rotation. We’re also introducing rotate on-demand, rotation visibility improvements, and a new limit on the price of all symmetric keys that have had two or more rotations (including existing keys). In this post, I discuss all those capabilities and changes. I also present a broader overview of how symmetric cryptographic key rotation came to be, and cover our recommendations on when you might need rotation and how often to rotate your keys. If you’ve ever been curious about AWS KMS automatic key rotation—why it exists, when to enable it, and when to use it on-demand—read on.

How we got here

There are longstanding reasons for cryptographic key rotation. If you were Caesar in Roman times and you needed to send messages with sensitive information to your regional commanders, you might use keys and ciphers to encrypt and protect your communications. There are well-documented examples of using cryptography to protect communications during this time, so much so that the standard substitution cipher, where you swap each letter for a different letter that is a set number of letters away in the alphabet, is referred to as Caesar’s cipher. The cipher is the substitution mechanism, and the key is the number of letters away from the intended letter you go to find the substituted letter for the ciphertext.

The challenge for Caesar in relying on this kind of symmetric key cipher is that both sides (Caesar and his field generals) needed to share keys and keep those keys safe from prying eyes. What happens to Caesar’s secret invasion plans if the key used to encipher his attack plan was secretly intercepted in transmission down the Appian Way? Caesar had no way to know. But if he rotated keys, he could limit the scope of which messages could be read, thus limiting his risk. Messages sent under a key created in the year 52 BCE wouldn’t automatically work for messages sent the following year, provided that Caesar rotated his keys yearly and the newer keys weren’t accessible to the adversary. Key rotation can reduce the scope of data exposure (what a threat actor can see) when some but not all keys are compromised. Of course, every time the key changed, Caesar had to send messengers to his field generals to communicate the new key. Those messengers had to ensure that no enemies intercepted the new keys without their knowledge – a daunting task.

Illustration of Roman solider on horseback riding through countryside on cobblestone trail.

Figure 1: The state of the art for secure key rotation and key distribution in 52 BC.

Fast forward to the 1970s–2000s

In modern times, cryptographic algorithms designed for digital computer systems mean that keys no longer travel down the Appian Way. Instead, they move around digital systems, are stored in unprotected memory, and sometimes are printed for convenience. The risk of key leakage still exists, therefore there is a need for key rotation. During this period, more significant security protections were developed that use both software and hardware technology to protect digital cryptographic keys and reduce the need for rotation. The highest-level protections offered by these techniques can limit keys to specific devices where they can never leave as plaintext. In fact, the US National Institute of Standards and Technologies (NIST) has published a specific security standard, FIPS 140, that addresses the security requirements for these cryptographic modules.

Modern cryptography also has the risk of cryptographic key wear-out

Besides addressing risks from key leakage, key rotation has a second important benefit that becomes more pronounced in the digital era of modern cryptography—cryptographic key wear-out. A key can become weaker, or “wear out,” over time just by being used too many times. If you encrypt enough data under one symmetric key, and if a threat actor acquires enough of the resulting ciphertext, they can perform analysis against your ciphertext that will leak information about the key. Current cryptographic recommendations to protect against key wear-out can vary depending on how you’re encrypting data, the cipher used, and the size of your key. However, even a well-designed AES-GCM implementation with robust initialization vectors (IVs) and large key size (256 bits) should be limited to encrypting no more than 4.3 billion messages (232), where each message is limited to about 64 GiB under a single key.

Figure 2: Used enough times, keys can wear out.

Figure 2: Used enough times, keys can wear out.

During the early 2000s, to help federal agencies and commercial enterprises navigate key rotation best practices, NIST formalized several of the best practices for cryptographic key rotation in the NIST SP 800-57 Recommendation for Key Management standard. It’s an excellent read overall and I encourage you to examine Section 5.3 in particular, which outlines ways to determine the appropriate length of time (the cryptoperiod) that a specific key should be relied on for the protection of data in various environments. According to the guidelines, the following are some of the benefits of setting cryptoperiods (and rotating keys within these periods):

5.3 Cryptoperiods

A cryptoperiod is the time span during which a specific key is authorized for use by legitimate entities or the keys for a given system will remain in effect. A suitably defined cryptoperiod:

  1. Limits the amount of information that is available for cryptanalysis to reveal the key (e.g. the number of plaintext and ciphertext pairs encrypted with the key);
  2. Limits the amount of exposure if a single key is compromised;
  3. Limits the use of a particular algorithm (e.g., to its estimated effective lifetime);
  4. Limits the time available for attempts to penetrate physical, procedural, and logical access mechanisms that protect a key from unauthorized disclosure;
  5. Limits the period within which information may be compromised by inadvertent disclosure of a cryptographic key to unauthorized entities; and
  6. Limits the time available for computationally intensive cryptanalysis.

Sometimes, cryptoperiods are defined by an arbitrary time period or maximum amount of data protected by the key. However, trade-offs associated with the determination of cryptoperiods involve the risk and consequences of exposure, which should be carefully considered when selecting the cryptoperiod (see Section 5.6.4).

(Source: NIST SP 800-57 Recommendation for Key Management, page 34).

One of the challenges in applying this guidance to your own use of cryptographic keys is that you need to understand the likelihood of each risk occurring in your key management system. This can be even harder to evaluate when you’re using a managed service to protect and use your keys.

Fast forward to the 2010s: Envisioning a key management system where you might not need automatic key rotation

When we set out to build a managed service in AWS in 2014 for cryptographic key management and help customers protect their AWS encryption workloads, we were mindful that our keys needed to be as hardened, resilient, and protected against external and internal threat actors as possible. We were also mindful that our keys needed to have long-term viability and use built-in protections to prevent key wear-out. These two design constructs—that our keys are strongly protected to minimize the risk of leakage and that our keys are safe from wear out—are the primary reasons we recommend you limit key rotation or consider disabling rotation if you don’t have compliance requirements to do so. Scheduled key rotation in AWS KMS offers limited security benefits to your workloads.

Specific to key leakage, AWS KMS keys in their unencrypted, plaintext form cannot be accessed by anyone, even AWS operators. Unlike Caesar’s keys, or even cryptographic keys in modern software applications, keys generated by AWS KMS never exist in plaintext outside of the NIST FIPS 140-2 Security Level 3 fleet of hardware security modules (HSMs) in which they are used. See the related post AWS KMS is now FIPS 140-2 Security Level 3. What does this mean for you? for more information about how AWS KMS HSMs help you prevent unauthorized use of your keys. Unlike many commercial HSM solutions, AWS KMS doesn’t even allow keys to be exported from the service in encrypted form. Why? Because an external actor with the proper decryption key could then expose the KMS key in plaintext outside the service.

This hardened protection of your key material is salient to the principal security reason customers want key rotation. Customers typically envision rotation as a way to mitigate a key leaking outside the system in which it was intended to be used. However, since KMS keys can be used only in our HSMs and cannot be exported, the possibility of key exposure becomes harder to envision. This means that rotating a key as protection against key exposure is of limited security value. The HSMs are still the boundary that protects your keys from unauthorized access, no matter how many times the keys are rotated.

If we decide the risk of plaintext keys leaking from AWS KMS is sufficiently low, don’t we still need to be concerned with key wear-out? AWS KMS mitigates the risk of key wear-out by using a key derivation function (KDF) that generates a unique, derived AES 256-bit key for each individual request to encrypt or decrypt under a 256-bit symmetric KMS key. Those derived encryption keys are different every time, even if you make an identical call for encrypt with the same message data under the same KMS key. The cryptographic details for our key derivation method are provided in the AWS KMS Cryptographic Details documentation, and KDF operations use the KDF in counter mode, using HMAC with SHA256. These KDF operations make cryptographic wear-out substantially different for KMS keys than for keys you would call and use directly for encrypt operations. A detailed analysis of KMS key protections for cryptographic wear-out is provided in the Key Management at the Cloud Scale whitepaper, but the important take-away is that a single KMS key can be used for more than a quadrillion (250) encryption requests without wear-out risk.

In fact, within the NIST 800-57 guidelines is consideration that when the KMS key (key-wrapping key in NIST language) is used with unique data keys, KMS keys can have longer cryptoperiods:

“In the case of these very short-term key-wrapping keys, an appropriate cryptoperiod (i.e., which includes both the originator and recipient-usage periods) is a single communication session. It is assumed that the wrapped keys will not be retained in their wrapped form, so the originator-usage period and recipient-usage period of a key-wrapping key is the same. In other cases, a key-wrapping key may be retained so that the files or messages encrypted by the wrapped keys may be recovered later. In such cases, the recipient-usage period may be significantly longer than the originator-usage period of the key-wrapping key, and cryptoperiods lasting for years may be employed.

Source: NIST 800-57 Recommendations for Key Management, section

So why did we build key rotation in AWS KMS in the first place?

Although we advise that key rotation for KMS keys is generally not necessary to improve the security of your keys, you must consider that guidance in the context of your own unique circumstances. You might be required by internal auditors, external compliance assessors, or even your own customers to provide evidence of regular rotation of all keys. A short list of regulatory and standards groups that recommend key rotation includes the aforementioned NIST 800-57, Center for Internet Security (CIS) benchmarks, ISO 27001, System and Organization Controls (SOC) 2, the Payment Card Industry Data Security Standard (PCI DSS), COBIT 5, HIPAA, and the Federal Financial Institutions Examination Council (FFIEC) Handbook, just to name a few.

Customers in regulated industries must consider the entirety of all the cryptographic systems used across their organizations. Taking inventory of which systems incorporate HSM protections, which systems do or don’t provide additional security against cryptographic wear-out, or which programs implement encryption in a robust and reliable way can be difficult for any organization. If a customer doesn’t have sufficient cryptographic expertise in the design and operation of each system, it becomes a safer choice to mandate a uniform scheduled key rotation.

That is why we offer an automatic, convenient method to rotate symmetric KMS keys. Rotation allows customers to demonstrate this key management best practice to their stakeholders instead of having to explain why they chose not to.

Figure 3 details how KMS appends new key material within an existing KMS key during each key rotation.

Figure 3: KMS key rotation process

Figure 3: KMS key rotation process

We designed the rotation of symmetric KMS keys to have low operational impact to both key administrators and builders using those keys. As shown in Figure 3, a keyID configured to rotate will append new key material on each rotation while still retaining and keeping the existing key material of previous versions. This append method achieves rotation without having to decrypt and re-encrypt existing data that used a previous version of a key. New encryption requests under a given keyID will use the latest key version, while decrypt requests under that keyID will use the appropriate version. Callers don’t have to name the version of the key they want to use for encrypt/decrypt, AWS KMS manages this transparently.

Some customers assume that a key rotation event should forcibly re-encrypt any data that was ever encrypted under the previous key version. This is not necessary when AWS KMS automatically rotates to use a new key version for encrypt operations. The previous versions of keys required for decrypt operations are still safe within the service.

We’ve offered the ability to automatically schedule an annual key rotation event for many years now. Lately, we’ve heard from some of our customers that they need to rotate keys more frequently than the fixed period of one year. We will address our newly launched capabilities to help meet these needs in the final section of this blog post.

More options for key rotation in AWS KMS (with a price reduction)

After learning how we think about key rotation in AWS KMS, let’s get to the new options we’ve launched in this space:

  • Configurable rotation periods: Previously, when using automatic key rotation, your only option was a fixed annual rotation period. You can now set a rotation period from 90 days to 2,560 days (just over seven years). You can adjust this period at any point to reset the time in the future when rotation will take effect. Existing keys set for rotation will continue to rotate every year.
  • On-demand rotation for KMS keys: In addition to more flexible automatic key rotation, you can now invoke on-demand rotation through the AWS Management Console for AWS KMS, the AWS Command Line Interface (AWS CLI), or the AWS KMS API using the new RotateKeyOnDemand API. You might occasionally need to use on-demand rotation to test workloads, or to verify and prove key rotation events to internal or external stakeholders. Invoking an on-demand rotation won’t affect the timeline of any upcoming rotation scheduled for this key.

    Note: We’ve set a default quota of 10 on-demand rotations for a KMS key. Although the need for on-demand key rotation should be infrequent, you can ask to have this quota raised. If you have a repeated need for testing or validating instant key rotation, consider deleting the test keys and repeating this operation for RotateKeyOnDemand on new keys.

  • Improved visibility: You can now use the AWS KMS console or the new ListKeyRotations API to view previous key rotation events. One of the challenges in the past is that it’s been hard to validate that your KMS keys have rotated. Now, every previous rotation for a KMS key that has had a scheduled or on-demand rotation is listed in the console and available via API.
    Figure 4: Key rotation history showing date and type of rotation

    Figure 4: Key rotation history showing date and type of rotation

  • Price cap for keys with more than two rotations: We’re also introducing a price cap for automatic key rotation. Previously, each annual rotation of a KMS key added $1 per month to the price of the key. Now, for KMS keys that you rotate automatically or on-demand, the first and second rotation of the key adds $1 per month in cost (prorated hourly), but this price increase is capped at the second rotation. Rotations after your second rotation aren’t billed. Existing customers that have keys with three or more annual rotations will see a price reduction for those keys to $3 per month (prorated) per key starting in the month of May, 2024.


In this post, I highlighted the more flexible options that are now available for key rotation in AWS KMS and took a broader look into why key rotation exists. We know that many customers have compliance needs to demonstrate key rotation everywhere, and increasingly, to demonstrate faster or immediate key rotation. With the new reduced pricing and more convenient ways to verify key rotation events, we hope these new capabilities make your job easier.

Flexible key rotation capabilities are now available in all AWS Regions, including the AWS GovCloud (US) Regions. To learn more about this new capability, see the Rotating AWS KMS keys topic in the AWS KMS Developer Guide.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.


Jeremy Stieglitz

Jeremy is the Principal Product Manager for AWS KMS, where he drives global product strategy and roadmap. Jeremy has more than 25 years of experience defining security products and platforms across large companies (RSA, Entrust, Cisco, and Imperva) and start-up environments (Dataguise, Voltage, and Centrify). Jeremy is the author or co-author of 23 patents in network security, user authentication, and network automation and control.

Strengthen the DevOps pipeline and protect data with AWS Secrets Manager, AWS KMS, and AWS Certificate Manager

Post Syndicated from Magesh Dhanasekaran original https://aws.amazon.com/blogs/security/strengthen-the-devops-pipeline-and-protect-data-with-aws-secrets-manager-aws-kms-and-aws-certificate-manager/

In this blog post, we delve into using Amazon Web Services (AWS) data protection services such as Amazon Secrets Manager, AWS Key Management Service (AWS KMS), and AWS Certificate Manager (ACM) to help fortify both the security of the pipeline and security in the pipeline. We explore how these services contribute to the overall security of the DevOps pipeline infrastructure while enabling seamless integration of data protection measures. We also provide practical insights by demonstrating the implementation of these services within a DevOps pipeline for a three-tier WordPress web application deployed using Amazon Elastic Kubernetes Service (Amazon EKS).

DevOps pipelines involve the continuous integration, delivery, and deployment of cloud infrastructure and applications, which can store and process sensitive data. The increasing adoption of DevOps pipelines for cloud infrastructure and application deployments has made the protection of sensitive data a critical priority for organizations.

Some examples of the types of sensitive data that must be protected in DevOps pipelines are:

  • Credentials: Usernames and passwords used to access cloud resources, databases, and applications.
  • Configuration files: Files that contain settings and configuration data for applications, databases, and other systems.
  • Certificates: TLS certificates used to encrypt communication between systems.
  • Secrets: Any other sensitive data used to access or authenticate with cloud resources, such as private keys, security tokens, or passwords for third-party services.

Unintended access or data disclosure can have serious consequences such as loss of productivity, legal liabilities, financial losses, and reputational damage. It’s crucial to prioritize data protection to help mitigate these risks effectively.

The concept of security of the pipeline encompasses implementing security measures to protect the entire DevOps pipeline—the infrastructure, tools, and processes—from potential security issues. While the concept of security in the pipeline focuses on incorporating security practices and controls directly into the development and deployment processes within the pipeline.

By using Secrets Manager, AWS KMS, and ACM, you can strengthen the security of your DevOps pipelines, safeguard sensitive data, and facilitate secure and compliant application deployments. Our goal is to equip you with the knowledge and tools to establish a secure DevOps environment, providing the integrity of your pipeline infrastructure and protecting your organization’s sensitive data throughout the software delivery process.

Sample application architecture overview

WordPress was chosen as the use case for this DevOps pipeline implementation due to its popularity, open source nature, containerization support, and integration with AWS services. The sample architecture for the WordPress application in the AWS cloud uses the following services:

  • Amazon Route 53: A DNS web service that routes traffic to the correct AWS resource.
  • Amazon CloudFront: A global content delivery network (CDN) service that securely delivers data and videos to users with low latency and high transfer speeds.
  • AWS WAF: A web application firewall that protects web applications from common web exploits.
  • AWS Certificate Manager (ACM): A service that provides SSL/TLS certificates to enable secure connections.
  • Application Load Balancer (ALB): Routes traffic to the appropriate container in Amazon EKS.
  • Amazon Elastic Kubernetes Service (Amazon EKS): A scalable and highly available Kubernetes cluster to deploy containerized applications.
  • Amazon Relational Database Service (Amazon RDS): A managed relational database service that provides scalable and secure databases for applications.
  • AWS Key Management Service (AWS KMS): A key management service that allows you to create and manage the encryption keys used to protect your data at rest.
  • AWS Secrets Manager: A service that provides the ability to rotate, manage, and retrieve database credentials.
  • AWS CodePipeline: A fully managed continuous delivery service that helps to automate release pipelines for fast and reliable application and infrastructure updates.
  • AWS CodeBuild: A fully managed continuous integration service that compiles source code, runs tests, and produces ready-to-deploy software packages.
  • AWS CodeCommit: A secure, highly scalable, fully managed source-control service that hosts private Git repositories.

Before we explore the specifics of the sample application architecture in Figure 1, it’s important to clarify a few aspects of the diagram. While it displays only a single Availability Zone (AZ), please note that the application and infrastructure can be developed to be highly available across multiple AZs to improve fault tolerance. This means that even if one AZ is unavailable, the application remains operational in other AZs, providing uninterrupted service to users.

Figure 1: Sample application architecture

Figure 1: Sample application architecture

The flow of the data protection services in the post and depicted in Figure 1 can be summarized as follows:

First, we discuss securing your pipeline. You can use Secrets Manager to securely store sensitive information such as Amazon RDS credentials. We show you how to retrieve these secrets from Secrets Manager in your DevOps pipeline to access the database. By using Secrets Manager, you can protect critical credentials and help prevent unauthorized access, strengthening the security of your pipeline.

Next, we cover data encryption. With AWS KMS, you can encrypt sensitive data at rest. We explain how to encrypt data stored in Amazon RDS using AWS KMS encryption, making sure that it remains secure and protected from unauthorized access. By implementing KMS encryption, you add an extra layer of protection to your data and bolster the overall security of your pipeline.

Lastly, we discuss securing connections (data in transit) in your WordPress application. ACM is used to manage SSL/TLS certificates. We show you how to provision and manage SSL/TLS certificates using ACM and configure your Amazon EKS cluster to use these certificates for secure communication between users and the WordPress application. By using ACM, you can establish secure communication channels, providing data privacy and enhancing the security of your pipeline.

Note: The code samples in this post are only to demonstrate the key concepts. The actual code can be found on GitHub.

Securing sensitive data with Secrets Manager

In this sample application architecture, Secrets Manager is used to store and manage sensitive data. The AWS CloudFormation template provided sets up an Amazon RDS for MySQL instance and securely sets the master user password by retrieving it from Secrets Manager using KMS encryption.

Here’s how Secrets Manager is implemented in this sample application architecture:

  1. Creating a Secrets Manager secret.
    1. Create a Secrets Manager secret that includes the Amazon RDS database credentials using CloudFormation.
    2. The secret is encrypted using an AWS KMS customer managed key.
    3. Sample code:
          Type: AWS::RDS::DBInstance
      		ManageMasterUserPassword: true
              		KmsKeyId: !Ref RDSMySqlSecretEncryption

    The ManageMasterUserPassword: true line in the CloudFormation template indicates that the stack will manage the master user password for the Amazon RDS instance. To securely retrieve the password for the master user, the CloudFormation template uses the MasterUserSecret parameter, which retrieves the password from Secrets Manager. The KmsKeyId: !Ref RDSMySqlSecretEncryption line specifies the KMS key ID that will be used to encrypt the secret in Secrets Manager.

    By setting the MasterUserSecret parameter to retrieve the password from Secrets Manager, the CloudFormation stack can securely retrieve and set the master user password for the Amazon RDS MySQL instance without exposing it in plain text. Additionally, specifying the KMS key ID for encryption adds another layer of security to the secret stored in Secrets Manager.

  2. Retrieving secrets from Secrets Manager.
    1. The secrets store CSI driver is a Kubernetes-native driver that provides a common interface for Secrets Store integration with Amazon EKS. The secrets-store-csi-driver-provider-aws is a specific provider that provides integration with the Secrets Manager.
    2. To set up Amazon EKS, the first step is to create a SecretProviderClass, which specifies the secret ID of the Amazon RDS database. This SecretProviderClass is then used in the Kubernetes deployment object to deploy the WordPress application and dynamically retrieve the secrets from the secret manager during deployment. This process is entirely dynamic and verifies that no secrets are recorded anywhere. The SecretProviderClass is created on a specific app namespace, such as the wp namespace.
    3. Sample code:
      apiVersion: secrets-store.csi.x-k8s.io/v1
      kind: SecretProviderClass
        provider: aws
          objects: |
              - objectName: 'rds!db-0x0000-0x0000-0x0000-0x0000-0x0000'

When using Secrets manager, be aware of the following best practices for managing and securing Secrets Manager secrets:

  • Use AWS Identity and Access Management (IAM) identity policies to define who can perform specific actions on Secrets Manager secrets, such as reading, writing, or deleting them.
  • Secrets Manager resource policies can be used to manage access to secrets at a more granular level. This includes defining who has access to specific secrets based on attributes such as IP address, time of day, or authentication status.
  • Encrypt the Secrets Manager secret using an AWS KMS key.
  • Using CloudFormation templates to automate the creation and management of Secrets Manager secrets including rotation.
  • Use AWS CloudTrail to monitor access and changes to Secrets Manager secrets.
  • Use CloudFormation hooks to validate the Secrets Manager secret before and after deployment. If the secret fails validation, the deployment is rolled back.

Encrypting data with AWS KMS

Data encryption involves converting sensitive information into a coded form that can only be accessed with the appropriate decryption key. By implementing encryption measures throughout your pipeline, you make sure that even if unauthorized individuals gain access to the data, they won’t be able to understand its contents.

Here’s how data at rest encryption using AWS KMS is implemented in this sample application architecture:

  1. Amazon RDS secret encryption
    1. Encrypting secrets: An AWS KMS customer managed key is used to encrypt the secrets stored in Secrets Manager to ensure their confidentiality during the DevOps build process.
    2. Sample code:
          Type: AWS::RDS::DBInstance
            ManageMasterUserPassword: true
              KmsKeyId: !Ref RDSMySqlSecretEncryption
          Type: "AWS::KMS::Key"
              Id: rds-mysql-secret-encryption
                - Sid: Allow administration of the key
                  Effect: Allow
                  "Action": [
                - Sid: Allow use of the key
                  Effect: Allow
                  "Action": [

  2. Amazon RDS data encryption
    1. Enable encryption for an Amazon RDS instance using CloudFormation. Specify the KMS key ARN in the CloudFormation stack and RDS will use the specified KMS key to encrypt data at rest.
    2. Sample code:
          Type: AWS::RDS::DBInstance
        KmsKeyId: !Ref RDSMySqlDataEncryption
              StorageEncrypted: true
          Type: "AWS::KMS::Key"
              Id: rds-mysql-data-encryption
                - Sid: Allow administration of the key
                  Effect: Allow
                  "Action": [
                - Sid: Allow use of the key
                  Effect: Allow
                  "Action": [

  3. Kubernetes Pods storage
    1. Use encrypted Amazon Elastic Block Store (Amazon EBS) volumes to store configuration data. Create a managed encrypted Amazon EBS volume using the following code snippet, and then deploy a Kubernetes pod with the persistent volume claim (PVC) mounted as a volume.
    2. Sample code:
      kind: StorageClass
      provisioner: ebs.csi.aws.com
        csi.storage.k8s.io/fstype: xfs
        encrypted: "true"
      kind: Deployment
            - name: persistent-storage
                claimName: ebs-claim

  4. Amazon ECR
    1. To secure data at rest in Amazon Elastic Container Registry (Amazon ECR), enable encryption at rest for Amazon ECR repositories using the AWS Management Console or AWS Command Line Interface (AWS CLI). ECR uses AWS KMS to encrypt the data at rest.
    2. Create a KMS key for Amazon ECR and use that key to encrypt the data at rest.
    3. Automate the creation of encrypted ECR repositories and enable encryption at rest using a DevOps pipeline, use CodePipeline to automate the deployment of the CloudFormation stack.
    4. Define the creation of encrypted Amazon ECR repositories as part of the pipeline.
    5. Sample code:
          Type: AWS::ECR::Repository
              EncryptionType: KMS
              KmsKey: !Ref ECREncryption
          Type: AWS::KMS::Key
              Id: ecr-encryption-key
                - Sid: Allow administration of the key
                  Effect: Allow
                  "Action": [
                - Sid: Allow use of the key
                  Effect: Allow
                  "Action": [

AWS best practices for managing encryption keys in an AWS environment

To effectively manage encryption keys and verify the security of data at rest in an AWS environment, we recommend the following best practices:

  • Use separate AWS KMS customer managed KMS keys for data classifications to provide better control and management of keys.
  • Enforce separation of duties by assigning different roles and responsibilities for key management tasks, such as creating and rotating keys, setting key policies, or granting permissions. By segregating key management duties, you can reduce the risk of accidental or intentional key compromise and improve overall security.
  • Use CloudTrail to monitor AWS KMS API activity and detect potential security incidents.
  • Rotate KMS keys as required by your regulatory requirements.
  • Use CloudFormation hooks to validate KMS key policies to verify that they align with organizational and regulatory requirements.

Following these best practices and implementing encryption at rest for different services such as Amazon RDS, Kubernetes Pods storage, and Amazon ECR, will help ensure that data is encrypted at rest.

Securing communication with ACM

Secure communication is a critical requirement for modern environments and implementing it in a DevOps pipeline is crucial for verifying that the infrastructure is secure, consistent, and repeatable across different environments. In this WordPress application running on Amazon EKS, ACM is used to secure communication end-to-end. Here’s how to achieve this:

  1. Provision TLS certificates with ACM using a DevOps pipeline
    1. To provision TLS certificates with ACM in a DevOps pipeline, automate the creation and deployment of TLS certificates using ACM. Use AWS CloudFormation templates to create the certificates and deploy them as part of infrastructure as code. This verifies that the certificates are created and deployed consistently and securely across multiple environments.
    2. Sample code:
          Type: AWS::CertificateManager::Certificate
            DomainName: !Ref DNSDomainName
            ValidationMethod: 'DNS'
          Description: dns domain name 
          TypeM: String
          Default: "example.com"

  2. Provisioning of ALB and integration of TLS certificate using AWS ALB Ingress Controller for Kubernetes
    1. Use a DevOps pipeline to create and configure the TLS certificates and ALB. This verifies that the infrastructure is created consistently and securely across multiple environments.
    2. Sample code:
      kind: Ingress
          alb.ingress.kubernetes.io/scheme: internet-facing
          alb.ingress.kubernetes.io/certificate-arn: arn:aws:acm:us-east-1:000000000000:certificate/0x0000-0x0000-0x0000-0x0000-0x0000
          alb.ingress.kubernetes.io/listen-ports: '[{"HTTPS":443}]'
          alb.ingress.kubernetes.io/security-groups:  sg-0x00000x0000,sg-0x00000x0000
        ingressClassName: alb

  3. CloudFront and ALB
    1. To secure communication between CloudFront and the ALB, verify that the traffic from the client to CloudFront and from CloudFront to the ALB is encrypted using the TLS certificate.
    2. Sample code:
          Type: AWS::CloudFront::Distribution
                - DomainName: !Ref ALBDNSName
                  Id: !Ref ALBDNSName
                    HTTPSPort: '443'
                    OriginProtocolPolicy: 'https-only'
                      - LSv1
      AcmCertificateArn: !Sub 'arn:aws:acm:${AWS::Region}:${AWS::AccountId}:certificate/${ACMCertificateIdentifier}'
                  SslSupportMethod:  'sni-only'
                  MinimumProtocolVersion: 'TLSv1.2_2021'
          Description: alb dns name
          Type: String
          Default: "k8s-wp-ingressw-x0x0000x000-x0x0000x000.us-east-1.elb.amazonaws.com"

  4. ALB to Kubernetes Pods
    1. To secure communication between the ALB and the Kubernetes Pods, use the Kubernetes ingress resource to terminate SSL/TLS connections at the ALB. The ALB sends the PROTO metadata http connection header to the WordPress web server. The web server checks the incoming traffic type (http or https) and enables the HTTPS connection only hereafter. This verifies that pod responses are sent back to ALB only over HTTPS.
    2. Additionally, using the X-Forwarded-Proto header can help pass the original protocol information and help avoid issues with the $_SERVER[‘HTTPS’] variable in WordPress.
    3. Sample code:
      define('FORCE_SSL_ADMIN', true);
      if (isset($_SERVER['HTTP_X_FORWARDED_PROTO']) && strpos($_SERVER['HTTP_X_FORWARDED_PROTO'], 'https') !== false) {
          $_SERVER['HTTPS'] = 'on';

  5. Kubernetes Pods to Amazon RDS
    1. To secure communication between the Kubernetes Pods in Amazon EKS and the Amazon RDS database, use SSL/TLS encryption on the database connection.
    2. Configure an Amazon RDS MySQL instance with enhanced security settings to verify that only TLS-encrypted connections are allowed to the database. This is achieved by creating a DB parameter group with a parameter called require_secure_transport set to ‘1‘. The WordPress configuration file is also updated to enable SSL/TLS communication with the MySQL database. Then enable the TLS flag on the MySQL client and the Amazon RDS public certificate is passed to ensure that the connection is encrypted using the TLS_AES_256_GCM_SHA384 protocol. The sample code that follows focuses on enhancing the security of the RDS MySQL instance by enforcing encrypted connections and configuring WordPress to use SSL/TLS for communication with the database.
    3. Sample code:
          Type: 'AWS::RDS::DBParameterGroup'
            DBParameterGroupName: 'rds-tls-custom-mysql'
              require_secure_transport: '1'
          Type: AWS::RDS::DBInstance
            DBName: 'wordpress'
            DBParameterGroupName: !Ref RDSDBParameterGroup
      // Enable SSL/TLS between WordPress and MYSQL database
      define('MYSQL_CLIENT_FLAGS', MYSQLI_CLIENT_SSL);//This activates SSL mode
      define('MYSQL_SSL_CA', '/usr/src/wordpress/amazon-global-bundle-rds.pem');

In this architecture, AWS WAF is enabled at CloudFront to protect the WordPress application from common web exploits. AWS WAF for CloudFront is recommended and use AWS managed WAF rules to verify that web applications are protected from common and the latest threats.

Here are some AWS best practices for securing communication with ACM:

  • Use SSL/TLS certificates: Encrypt data in transit between clients and servers. ACM makes it simple to create, manage, and deploy SSL/TLS certificates across your infrastructure.
  • Use ACM-issued certificates: This verifies that your certificates are trusted by major browsers and that they are regularly renewed and replaced as needed.
  • Implement certificate revocation: Implement certificate revocation for SSL/TLS certificates that have been compromised or are no longer in use.
  • Implement strict transport security (HSTS): This helps protect against protocol downgrade attacks and verifies that SSL/TLS is used consistently across sessions.
  • Configure proper cipher suites: Configure your SSL/TLS connections to use only the strongest and most secure cipher suites.

Monitoring and auditing with CloudTrail

In this section, we discuss the significance of monitoring and auditing actions in your AWS account using CloudTrail. CloudTrail is a logging and tracking service that records the API activity in your AWS account, which is crucial for troubleshooting, compliance, and security purposes. Enabling CloudTrail in your AWS account and securely storing the logs in a durable location such as Amazon Simple Storage Service (Amazon S3) with encryption is highly recommended to help prevent unauthorized access. Monitoring and analyzing CloudTrail logs in real-time using CloudWatch Logs can help you quickly detect and respond to security incidents.

In a DevOps pipeline, you can use infrastructure-as-code tools such as CloudFormation, CodePipeline, and CodeBuild to create and manage CloudTrail consistently across different environments. You can create a CloudFormation stack with the CloudTrail configuration and use CodePipeline and CodeBuild to build and deploy the stack to different environments. CloudFormation hooks can validate the CloudTrail configuration to verify it aligns with your security requirements and policies.

It’s worth noting that the aspects discussed in the preceding paragraph might not apply if you’re using AWS Organizations and the CloudTrail Organization Trail feature. When using those services, the management of CloudTrail configurations across multiple accounts and environments is streamlined. This centralized approach simplifies the process of enforcing security policies and standards uniformly throughout the organization.

By following these best practices, you can effectively audit actions in your AWS environment, troubleshoot issues, and detect and respond to security incidents proactively.

Complete code for sample architecture for deployment

The complete code repository for the sample WordPress application architecture demonstrates how to implement data protection in a DevOps pipeline using various AWS services. The repository includes both infrastructure code and application code that covers all aspects of the sample architecture and implementation steps.

The infrastructure code consists of a set of CloudFormation templates that define the resources required to deploy the WordPress application in an AWS environment. This includes the Amazon Virtual Private Cloud (Amazon VPC), subnets, security groups, Amazon EKS cluster, Amazon RDS instance, AWS KMS key, and Secrets Manager secret. It also defines the necessary security configurations such as encryption at rest for the RDS instance and encryption in transit for the EKS cluster.

The application code is a sample WordPress application that is containerized using Docker and deployed to the Amazon EKS cluster. It shows how to use the Application Load Balancer (ALB) to route traffic to the appropriate container in the EKS cluster, and how to use the Amazon RDS instance to store the application data. The code also demonstrates how to use AWS KMS to encrypt and decrypt data in the application, and how to use Secrets Manager to store and retrieve secrets. Additionally, the code showcases the use of ACM to provision SSL/TLS certificates for secure communication between the CloudFront and the ALB, thereby ensuring data in transit is encrypted, which is critical for data protection in a DevOps pipeline.


Strengthening the security and compliance of your application in the cloud environment requires automating data protection measures in your DevOps pipeline. This involves using AWS services such as Secrets Manager, AWS KMS, ACM, and AWS CloudFormation, along with following best practices.

By automating data protection mechanisms with AWS CloudFormation, you can efficiently create a secure pipeline that is reproducible, controlled, and audited. This helps maintain a consistent and reliable infrastructure.

Monitoring and auditing your DevOps pipeline with AWS CloudTrail is crucial for maintaining compliance and security. It allows you to track and analyze API activity, detect any potential security incidents, and respond promptly.

By implementing these best practices and using data protection mechanisms, you can establish a secure pipeline in the AWS cloud environment. This enhances the overall security and compliance of your application, providing a reliable and protected environment for your deployments.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Magesh Dhanasekaran

Magesh Dhanasekaran

Magesh has significant experience in the cloud security space especially in data protection, threat detection and security governance, risk & compliance domain. Magesh has a track record in providing Information Security consulting service to financial industry and government agencies in Australia. He is using his extensive experience in cloud security architecture, digital transformation, and secure application development practice to provide security advisory on AWS products and services to WWPS Federal Financial Customers. Magesh currently holds cybersecurity industry certifications such as ISC2’s CISSP, ISACA’s CISM, CompTIA Security+ and AWS Solution Architect / Security Specialty Certification.

Karna Thandapani

Karna Thandapani

Karna is a Cloud Consultant with extensive experience in DevOps/DevSecOps and application development activities as a Developer. Karna has in-depth knowledge and hands-on experience in the major AWS services (Cloudformation, EC2, Lambda, Serverless, Step Functions, Glue, API Gateway, ECS, EKS, LB, AutoScaling, Route53, etc.,)and holding Developer Associate, Solutions Architect Associate, and DevOps Engineer Professional.

AWS KMS is now FIPS 140-2 Security Level 3. What does this mean for you?

Post Syndicated from Rushir Patel original https://aws.amazon.com/blogs/security/aws-kms-now-fips-140-2-level-3-what-does-this-mean-for-you/

AWS Key Management Service (AWS KMS) recently announced that its hardware security modules (HSMs) were given Federal Information Processing Standards (FIPS) 140-2 Security Level 3 certification from the U.S. National Institute of Standards and Technology (NIST). For organizations that rely on AWS cryptographic services, this higher security level validation has several benefits, including simpler set up and operation. In this post, we will share more details about the recent change in FIPS validation status for AWS KMS and explain the benefits to customers using AWS cryptographic services as a result of this change.

Background on NIST FIPS 140

The FIPS 140 framework provides guidelines and requirements for cryptographic modules that protect sensitive information. FIPS 140 is the industry standard in the US and Canada and is recognized around the world as providing authoritative certification and validation for the way that cryptographic modules are designed, implemented, and tested against NIST cryptographic security guidelines.

Organizations follow FIPS 140 to help ensure that their cryptographic security is aligned with government standards. FIPS 140 validation is also required in certain fields such as manufacturing, healthcare, and finance and is included in several industry and regulatory compliance frameworks, such as the Payment Card Industry Data Security Standard (PCI DSS), the Federal Risk and Authorization Management Program (FedRAMP), and the Health Information Trust Alliance (HITRUST) framework. FIPS 140 validation is recognized in many jurisdictions around the world, so organizations that operate globally can use FIPS 140 certification internationally.

For more information on FIPS Security Levels and requirements, see FIPS Pub 140-2: Security Requirements for Cryptographic Modules.

What FIPS 140-2 Security Level 3 means for AWS KMS and you

Until recently, AWS KMS had been validated at Security Level 2 overall and at Security Level 3 in the following four sub-categories:

  • Cryptographic module specification
  • Roles, services, and authentication
  • Physical security
  • Design assurance

The latest certification from NIST means that AWS KMS is now validated at Security Level 3 overall in each sub-category. As a result, AWS assumes more of the shared responsibility model, which will benefit customers for certain use cases. Security Level 3 certification can assist organizations seeking compliance with several industry and regulatory standards. Even though FIPS 140 validation is not expressly required in a number of regulatory regimes, maintaining stronger, easier-to-use encryption can be a powerful tool for complying with FedRAMP, U.S. Department of Defense (DOD) Approved Product List (APL), HIPAA, PCI, the European Union’s General Data Protection Regulation (GDPR), and the ISO 27001 standard for security management best practices and comprehensive security controls.

Customers who previously needed to meet compliance requirements for FIPS 140-2 Level 3 on AWS were required to use AWS CloudHSM, a single-tenant HSM solution that provides dedicated HSMs instead of managed service HSMs. Now, customers who were using CloudHSM to help meet their compliance obligations for Level 3 validation can use AWS KMS by itself for key generation and usage. Compared to CloudHSM, AWS KMS is typically lower cost and easier to set up and operate as a managed service, and using AWS KMS shifts the responsibility for creating and controlling encryption keys and operating HSMs from the customer to AWS. This allows you to focus resources on your core business instead of on undifferentiated HSM infrastructure management tasks.

AWS KMS uses FIPS 140-2 Level 3 validated HSMs to help protect your keys when you request the service to create keys on your behalf or when you import them. The HSMs in AWS KMS are designed so that no one, not even AWS employees, can retrieve your plaintext keys. Your plaintext keys are never written to disk and are only used in volatile memory of the HSMs while performing your requested cryptographic operation.

The FIPS 140-2 Level 3 certified HSMs in AWS KMS are deployed in all AWS Regions, including the AWS GovCloud (US) Regions. The China (Beijing) and China (Ningxia) Regions do not support the FIPS 140-2 Cryptographic Module Validation Program. AWS KMS uses Office of the State Commercial Cryptography Administration (OSCCA) certified HSMs to protect KMS keys in China Regions. The certificate for the AWS KMS FIPS 140-2 Security Level 3 validation is available on the NIST Cryptographic Module Validation Program website.

As with many industry and regulatory frameworks, FIPS 140 is evolving. NIST approved and published a new updated version of the 140 standard, FIPS 140-3, which supersedes FIPS 140-2. The U.S. government has begun transitioning to the FIPS 140-3 cryptography standard, with NIST announcing that they will retire all FIPS 140-2 certificates on September 22, 2026. NIST recently validated AWS-LC under FIPS 140-3 and is currently in the process of evaluating AWS KMS and certain instance types of AWS CloudHSM under the FIPS 140-3 standard. To check the status of these evaluations, see the NIST Modules In Process List.

For more information on FIPS 140-3, see FIPS Pub 140-3: Security Requirements for Cryptographic Modules.

Legal Disclaimer

This document is provided for the purposes of information only; it is not legal advice, and should not be relied on as legal advice. Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers.

AWS encourages its customers to obtain appropriate advice on their implementation of privacy and data protection environments, and more generally, applicable laws and other obligations relevant to their business.

AWS encourages its customers to obtain appropriate advice on their implementation of privacy and data protection environments, and more generally, applicable laws and other obligations relevant to their business.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Rushir Patel

Rushir Patel

Rushir is a Senior Security Specialist at AWS, focused on data protection and cryptography services. His goal is to make complex topics simple for customers and help them adopt better security practices. Before joining AWS, he worked in security product management at IBM and Bank of America.

Rohit Panjala

Rohit Panjala

Rohit is a Worldwide Security GTM Specialist at AWS, focused on data protection and cryptography services. He is responsible for developing and implementing go-to-market (GTM) strategies and sales plays and driving customer and partner engagements for AWS data protection services on a global scale. Before joining AWS, Rohit worked in security product management and electrical engineering roles.

New – Amazon S3 Dual-Layer Server-Side Encryption with Keys Stored in AWS Key Management Service (DSSE-KMS)

Post Syndicated from Irshad Buchh original https://aws.amazon.com/blogs/aws/new-amazon-s3-dual-layer-server-side-encryption-with-keys-stored-in-aws-key-management-service-dsse-kms/

Today, we are launching Amazon S3 dual-layer server-side encryption with keys stored in AWS Key Management Service (DSSE-KMS), a new encryption option in Amazon S3 that applies two layers of encryption to objects when they are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket. DSSE-KMS is designed to meet National Security Agency CNSSP 15 for FIPS compliance and Data-at-Rest Capability Package (DAR CP) Version 5.0 guidance for two layers of CNSA encryption. Using DSSE-KMS, you can fulfill regulatory requirements to apply multiple layers of encryption to your data.

Amazon S3 is the only cloud object storage service where customers can apply two layers of encryption at the object level and control the data keys used for both layers. DSSE-KMS makes it easier for highly regulated customers to fulfill rigorous security standards, such as US Department of Defense (DoD) customers.

With DSSE-KMS, you can specify dual-layer server-side encryption (DSSE) in the PUT or COPY request for an object or configure your S3 bucket to apply DSSE to all new objects by default. You can also enforce DSSE-KMS using IAM and bucket policies. Each layer of encryption uses a separate cryptographic implementation library with individual data encryption keys. DSSE-KMS helps protect sensitive data against the low probability of a vulnerability in a single layer of cryptographic implementation.

DSSE-KMS simplifies the process of applying two layers of encryption to your data, without having to invest in infrastructure required for client-side encryption. Each layer of encryption uses a different implementation of the 256-bit Advanced Encryption Standard with Galois Counter Mode (AES-GCM) algorithm. DSSE-KMS uses the AWS Key Management Service (AWS KMS) to generate data keys, allowing you to control your customer managed keys by setting permissions per key and specifying key rotation schedules. With DSSE-KMS, you can now query and analyze your dual-encrypted data with AWS services such as Amazon Athena, Amazon SageMaker, and more.

With this launch, Amazon S3 now offers four options for server-side encryption:

  1. Server-side encryption with Amazon S3 managed keys (SSE-S3)
  2. Server-side encryption with AWS KMS (SSE-KMS)
  3. Server-side encryption with customer-provided encryption keys (SSE-C)
  4. Dual-layer server-side encryption with keys stored in KMS (DSSE-KMS)

Let’s see how DSSE-KMS works in practice.

Create an S3 Bucket and Turn on DSSE-KMS
To create a new bucket in the Amazon S3 console, I choose Buckets in the navigation pane. I choose Create bucket, and I select a unique and meaningful name for the bucket. Under Default encryption section, I choose DSSE-KMS as the encryption option. From the available AWS KMS keys, I select a key for my requirements. Finally, I choose Create bucket to complete the creation of the S3 bucket, encrypted by DSSE-KMS encryption settings.


Upload an Object to the DSSE-SSE enabled S3 Bucket
In the Buckets list, I choose the name of the bucket that I want to upload an object to. On the Objects tab for the bucket, I choose Upload. Under Files and folders, I choose Add files. I then choose a file to upload, and then choose Open. Under Server-side encryption, I choose Do not specify an encryption key. I then choose Upload.

Server Side Encryption

Once the object is uploaded to the S3 bucket, I notice that the uploaded object inherits the Server-side encryption settings from the bucket.

Server Side Encryption Setting

Download a DSSE-KMS Encrypted Object from an S3 Bucket
I select the object that I previously uploaded and choose Download or choose Download as from the Object actions menu. Once the object is downloaded, I open it locally, and the object is decrypted automatically, requiring no change to client applications.

Now Available
Amazon S3 dual-layer server-side encryption with keys stored in AWS KMS (DSSE-KMS) is available today in all AWS Regions. You can get started with DSSE-KMS via the AWS CLI or AWS Management Console. To learn more about all available encryption options on Amazon S3, visit the Amazon S3 User Guide. For pricing information on DSSE-KMS, visit the Amazon S3 pricing page (Storage tab) and the AWS KMS pricing page.

— Irshad

Announcing AWS KMS External Key Store (XKS)

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/announcing-aws-kms-external-key-store-xks/

I am excited to announce the availability of AWS Key Management Service (AWS KMS) External Key Store. Customers who have a regulatory need to store and use their encryption keys on premises or outside of the AWS Cloud can now do so. This new capability allows you to store AWS KMS customer managed keys on a hardware security module (HSM) that you operate on premises or at any location of your choice.

At a high level, AWS KMS forwards API calls to securely communicate with your HSM. Your key material never leaves your HSM. This solution allows you to encrypt data with external keys for the vast majority of AWS services that support AWS KMS customer managed keys, such as Amazon EBS, AWS Lambda, Amazon S3, Amazon DynamoDB, and over 100 more services. There is no change required to your existing AWS services’ configuration parameters or code.

This helps you unblock use cases for a small portion of regulated workloads where encryption keys should be stored and used outside of an AWS data center. But this is a major change in the way you operate cloud-based infrastructure and a significant shift in the shared responsibility model. We expect only a small percentage of our customers to enable this capability. The additional operational burden and greater risks to availability, performance, and low latency operations on protected data will exceed—for most cases—the perceived security benefits from AWS KMS External Key Store.

Let me dive into the details.

A Brief Recap on Key Management and Encryption
When an AWS service is configured to encrypt data at rest, the service requests a unique encryption key from AWS KMS. We call this the data encryption key. To protect data encryption keys, the service also requests that AWS KMS encrypts that key with a specific KMS customer managed key, also known as a root key. Once encrypted, data keys can be safely stored alongside the data they protect. This pattern is called envelope encryption. Imagine an envelope that contains both the encrypted data and the encrypted key that was used to encrypt these data.

But how do we protect the root key? Protecting the root key is essential as it allows the decryption of all data keys it encrypted.

The root key material is securely generated and stored in a hardware security module, a piece of hardware designed to store secrets. It is tamper-resistant and designed so that the key material never leaves the secured hardware in plain text. AWS KMS uses HSMs that are certified under the NIST 140-2 Cryptographic Module certification program.

You can choose to create root keys tied to data classification, or create unique root keys to protect different AWS services, or by project tag, or associated to each data owner, and each root key is unique to each AWS Region.

AWS KMS calls the root keys customer managed keys when you create and manage the keys yourself. They are called AWS managed keys when they are created on behalf of an AWS service that encrypts data, such as Amazon Elastic Block Store (Amazon EBS), Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (RDS), or Amazon DynamoDB. For simplicity, let’s call them KMS keys. These are the root keys, the ones that never leave the secured HSM environment. All KMS encryption and decryption operations happen in the secured environment of the HSM.

The XKS Proxy Solution
When configuring AWS KMS External Key Store (XKS), you are replacing the KMS key hierarchy with a new, external root of trust. The root keys are now all generated and stored inside an HSM you provide and operate. When AWS KMS needs to encrypt or decrypt a data key, it forwards the request to your vendor-specific HSM.

All AWS KMS interactions with the external HSM are mediated by an external key store proxy (XKS proxy), a proxy that you provide, and you manage. The proxy translates generic AWS KMS requests into a format that the vendor-specific HSMs can understand.

The HSMs that XKS communicates with are not located in AWS data centers.

XKS architecture

To provide customers with a broad range of external key manager options, AWS KMS developed the XKS specification with feedback from several HSM, key management, and integration service providers, including Atos, Entrust, Fortanix, HashiCorp, Salesforce, Thales, and T-Systems. For information about availability, pricing, and how to use XKS with solutions from these vendors, consult the vendor directly.

In addition, we will provide a reference implementation of an XKS proxy that can be used with SoftHSM or any HSM that supports a PKCS #11 interface. This reference implementation XKS proxy can be run as a container, is built in Rust, and will be available via GitHub in the coming weeks.

Once you have completed the setup of your XKS proxy and HSM, you can create a corresponding external key store resource in KMS. You create keys in your HSM and map these keys to the external key store resource in KMS. Then you can use these keys with AWS services that support customer keys or your own applications to encrypt your data.

Each request from AWS KMS to the XKS proxy includes meta-data such as the AWS principal that called the KMS API and the KMS key ARN. This allows you to create an additional layer of authorization controls at the XKS proxy level, beyond those already provided by IAM policies in your AWS accounts.

The XKS proxy is effectively a kill switch you control. When you turn off the XKS proxy, all new encrypt and decrypt operations using XKS keys will cease to function. AWS services that have already provisioned a data key into memory for one of your resources will continue to work until either you deactivate the resource or the service key cache expires. For example, Amazon S3 caches data keys for a few minutes when bucket keys are enabled.

The Shift in Shared Responsibility
Under standard cloud operating procedures, AWS is responsible for maintaining the cloud infrastructure in operational condition. This includes, but is not limited to, patching the systems, monitoring the network, designing systems for high availability, and more.

When you elect to use XKS, there is a fundamental shift in the shared responsibility model. Under this model, you are responsible for maintaining the XKS proxy and your HSM in operational condition. Not only do they have to be secured and highly available, but also sized to sustain the expected number of AWS KMS requests. This applies to all components involved: the physical facilities, the power supplies, the cooling system, the network, the server, the operating system, and more.

Depending on your workload, AWS KMS operations may be critical to operating services that require encryption for your data at rest in the cloud. Typical services relying on AWS KMS for normal operation include Amazon Elastic Block Store (Amazon EBS), Lambda, Amazon S3, Amazon RDS, DynamoDB, and more. In other words, it means that when the part of the infrastructure under your responsibility is not available or has high latencies (typically over 250 ms), AWS KMS will not be able to operate, cascading the failure to requests that you make to other AWS services. You will not be able to start an EC2 instance, invoke a Lambda function, store or retrieve objects from S3, connect to your RDS or DynamoDB databases, or any other service that relies on AWS KMS XKS keys stored in the infrastructure you manage.

As one of the product managers involved in XKS told me while preparing this blog post, “you are running your own tunnel to oxygen through a very fragile path.”

We recommend only using this capability if you have a regulatory or compliance need that requires you to maintain your encryption keys outside of an AWS data center. Only enable XKS for the root keys that support your most critical workloads. Not all your data classification categories will require external storage of root keys. Keep the data set protected by XKS to the minimum to meet your regulatory requirements, and continue to use AWS KMS customer managed keys—fully under your control—for the rest.

Some customers for which external key storage is not a compliance requirement have also asked for this feature in the past, but they all ended up accepting one of the existing AWS KMS options for cloud-based key storage and usage once they realized that the perceived security benefits of an XKS-like solution didn’t outweigh the operational cost.

What Changes and What Stays the Same?
I tried to summarize the changes for you.

What is identical
to standard AWS KMS keys
What is changing

The supported AWS KMS APIs and key identifiers (ARN) are identical. AWS services that support customer managed keys will work with XKS.

The way to protect access and monitor access from the AWS side is unchanged. XKS uses the same IAM policies and the same key policies. API calls are logged in AWS CloudTrail, and AWS CloudWatch has the usage metrics.

The pricing is the same as other AWS KMS keys and API operations.

XKS does not support asymmetric or HMAC keys managed in the HSM you provide.

You now own the concerns of availability, durability, performance, and latency boundaries of your encryption key operations.

You can implement another layer of authorization, auditing, and monitoring at XKS proxy level. XKS resides in your network.

While the KMS price stays the same, your expenses are likely to go up substantially to procure an HSM and maintain your side of the XKS-related infrastructure in operational condition.

An Open Specification
For those strictly regulated workloads, we are developing XKS as an open interoperability specification. Not only have we collaborated with the major vendors I mentioned already, but we also opened a GitHub repository with the following materials:

  • The XKS proxy API specification. This describes the format of the generic requests KMS sends to an XKS proxy and the responses it expects. Any HSM vendor can use the specification to create an XKS proxy for their HSM.
  • A reference implementation of an XKS proxy that implements the specification. This code can be adapted by HSM vendors to create a proxy for their HSM.
  • An XKS proxy test client that can be used to check if an XKS proxy complies with the requirements of the XKS proxy API specification.

Other vendors, such as SalesForce, announced their own XKS solution allowing their customers to choose their own key management solution and plug it into their solution of choice, including SalesForce.

Pricing and Availability
External Key Store is provided at no additional cost on top of AWS KMS. AWS KMS charges $1 per root key per month, no matter where the key material is stored, on KMS, on CloudHSM, or on your own on-premises HSM.

For a full list of Regions where AWS KMS XKS is currently available, visit our technical documentation.

If you think XKS will help you to meet your regulatory requirements, have a look at the technical documentation and the XKS FAQ.

— seb

Securely retrieving secrets with AWS Lambda

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/securely-retrieving-secrets-with-aws-lambda/

AWS Lambda functions often need to access secrets, such as certificates, API keys, or database passwords. Storing secrets outside the function code in an external secrets manager helps to avoid exposing secrets in application source code. Using a secrets manager also allows you to audit and control access, and can help with secret rotation. Do not store secrets in Lambda environment variables, as these are visible to anyone who has access to view function configuration.

This post highlights some solutions to store secrets securely and retrieve them from within your Lambda functions.

AWS Partner Network (APN) member Hashicorp provides Vault to secure secrets and application data. Vault allows you to control access to your secrets centrally, across applications, systems, and infrastructure. You can store secrets in Vault and access them from a Lambda function to access a database, for example. The Vault Agent for AWS helps you authenticate with Vault, retrieve the database credentials, and then perform the queries. You can also use the Vault AWS Lambda extension to manage connectivity to Vault.

AWS Systems Manager Parameter Store enables you to store configuration data securely, including secrets, as parameter values. For information on Parameter Store pricing, see the documentation.

AWS Secrets Manager allows you to replace hardcoded credentials in your code with an API call to Secrets Manager to retrieve the secret programmatically. You can generate, protect, rotate, manage, and retrieve secrets throughout their lifecycle. By default, Secrets Manager does not write or cache the secret to persistent storage. Secrets Manager supports cross-account access to secrets. For information on Secrets Manager pricing, see the documentation.

Parameter Store integrates directly with Secrets Manager as a pass-through service for references to Secrets Manager secrets. Use this integration if you prefer using Parameter Store as a consistent solution for calling and referencing secrets across your applications. For more information, see “Referencing AWS Secrets Manager secrets from Parameter Store parameters.”

For an example application to show Secrets Manager functionality, deploy the example detailed in “How to securely provide database credentials to Lambda functions by using AWS Secrets Manager”.

When to retrieve secrets

When Lambda first invokes your function, it creates a runtime environment. It runs the function’s initialization (init) code, which is the code outside the main handler. Lambda then runs the function handler code as the invocation. This receives the event payload and processes your business logic. Subsequent invocations can use the same runtime environment.

You can retrieve secrets during each function invocation from within your handler code. This ensures that the secret value is always up to date but can lead to increased function duration and cost, as the function calls the secret manager during each invocation. There may also be additional retrieval costs from Secret Manager.

Retrieving secret during each invocation

Retrieving secret during each invocation

You can reduce costs and improve performance by retrieving the secret during the function init process. During subsequent invocations using the same runtime environment, your handler code can use the same secret.

Retrieving secret during function initialization.

Retrieving secret during function initialization.

The Serverless Land pattern example shows how to retrieve a secret during the init phase using Node.js and top-level await.

If a secret may change between subsequent invocations, ensure that your handler can check for the secret validity and, if necessary, retrieve the secret again.

Retrieve changed secret during subsequent invocation.

Retrieve changed secret during subsequent invocation.

You can also use Lambda extensions to retrieve secrets from Secrets Manager, cache them, and automatically refresh the cache based on a time value. The extension retrieves the secret from Secrets Manager before the init process and makes it available via a local HTTP endpoint. The function then retrieves the secret from the local HTTP endpoint, rather than directly from Secrets Manager, increasing performance. You can also share the extension with multiple functions, which can reduce function code. The extension handles refreshing the cache based on a configurable timeout value. This ensures that the function has the updated value, without handling the refresh in your function code, which increases reliability.

Using Lambda extensions to cache and refresh secret.

Using Lambda extensions to cache and refresh secret.

You can deploy the solution using the steps in Cache secrets using AWS Lambda extensions.

Lambda Powertools

Lambda Powertools provides a suite of utilities for Lambda functions to simplify the adoption of serverless best practices. AWS Lambda Powertools for Python and AWS Lambda Powertools for Java both provide a parameters utility that integrates with Secrets Manager.

from aws_lambda_powertools.utilities import parameters
def handler(event, context):
    # Retrieve a single secret
    value = parameters.get_secret("my-secret")
import software.amazon.lambda.powertools.parameters.SecretsProvider;
import software.amazon.lambda.powertools.parameters.ParamManager;

public class AppWithSecrets implements RequestHandler<APIGatewayProxyRequestEvent, APIGatewayProxyResponseEvent> {
    // Get an instance of the Secrets Provider
    SecretsProvider secretsProvider = ParamManager.getSecretsProvider();

    // Retrieve a single secret
    String value = secretsProvider.get("/my/secret");

Rotating secrets

You should rotate secrets to prevent the misuse of your secrets. This helps you to replace long-term secrets with short-term ones, which reduces the risk of compromise.

Secrets Manager has built-in functionality to rotate secrets on demand or according to a schedule. Secrets Manager has native integrations with Amazon RDS, Amazon DocumentDB, and Amazon Redshift, using a Lambda function to manage the rotation process for you. It deploys an AWS CloudFormation stack and populates the function with the Amazon Resource Name (ARN) of the secret. You specify the permissions to rotate the credentials, and how often you want to rotate the secret. You can view and edit Secrets Manager rotation settings in the Secrets Manager console.

Secrets Manager rotation settings

Secrets Manager rotation settings

You can also create your own rotation Lambda function for other services.

Auditing secrets access

You should continually review how applications are using your secrets to ensure that the usage is as you expect. You should also log any changes to them so you can investigate any potential issues, and roll back changes if necessary.

When using Hashicorp Vault, use Audit devices to log all requests and responses to Vault. Audit devices can append logs to a file, write to syslog, or write to a socket.

Secrets Manager supports logging API calls using AWS CloudTrail. CloudTrail monitors and records all API calls for Secrets Manager as events. This includes calls from code calling the Secrets Manager APIs and access via the Secrets Manager console. CloudTrail data is considered sensitive, so you should use AWS KMS encryption to protect it.

The CloudTrail event history shows the requests to secretsmanager.amazonaws.com.

Viewing CloudTrail access to Secrets Manager

Viewing CloudTrail access to Secrets Manager

You can use Amazon EventBridge to respond to alerts based on specific operations recorded in CloudTrail. These include secret rotation or deleted secrets. You can also generate an alert if someone tries to use a version of a secret version while it is pending deletion. This may help identify and alert you when an outdated certificate is used.

Securing secrets

You must tightly control access to secrets because of their sensitive nature. Create AWS Identity and Access Management (IAM) policies and resource policies to enable minimal access to secrets. You can use role-based, as well as attribute-based, access control. This can prevent credentials from being accidentally used or compromised. For more information, see “Authentication and access control for AWS Secrets Manager”.

Secrets Manager supports encryption at rest using AWS Key Management Service (AWS KMS) using keys that you manage. Secrets are encrypted in transit using TLS by default, which requires request signing.

You can access secrets from inside an Amazon Virtual Private Cloud (Amazon VPC) without requiring internet access. Use AWS PrivateLink and configure a Secrets Manager specific VPC endpoint.

Do not store plaintext secrets in Lambda environment variables. Ensure that you do not embed secrets directly in function code, commit these secrets to code repositories, or log the secret to CloudWatch.


Using a secrets manager to store secrets such as certificates, API keys or database passwords helps to avoid exposing secrets in application source code. This post highlights some AWS and third-party solutions, such as Hashicorp Vault, to store secrets securely and retrieve them from within your Lambda functions.

Secrets Manager is the preferred AWS solution for storing and managing secrets. I explain when to retrieve secrets, including using Lambda extensions to cache secrets, which can reduce cost and improve performance.

You can use the Lambda Powertools parameters utility, which integrates with Secrets Manager. Rotating secrets reduces the risk of compromise and you can audit secrets using CloudTrail and respond to alerts using EventBridge. I also cover security considerations for controlling access to your secrets.

For more serverless learning resources, visit Serverless Land.

How to tune TLS for hybrid post-quantum cryptography with Kyber

Post Syndicated from Brian Jarvis original https://aws.amazon.com/blogs/security/how-to-tune-tls-for-hybrid-post-quantum-cryptography-with-kyber/

We are excited to offer hybrid post-quantum TLS with Kyber for AWS Key Management Service (AWS KMS) and AWS Certificate Manager (ACM). In this blog post, we share the performance characteristics of our hybrid post-quantum Kyber implementation, show you how to configure a Maven project to use it, and discuss how to prepare your connection settings for Kyber post-quantum cryptography (PQC).

After five years of intensive research and cryptanalysis among partners from academia, the cryptographic community, and the National Institute of Standards and Technology (NIST), NIST has selected Kyber for post-quantum key encapsulation mechanism (KEM) standardization. This marks the beginning of the next generation of public key encryption. In time, the classical key establishment algorithms we use today, like RSA and elliptic curve cryptography (ECC), will be replaced by quantum-secure alternatives. At AWS Cryptography, we’ve been researching and analyzing the candidate KEMs through each round of the NIST selection process. We began supporting Kyber in round 2 and continue that support today.

A cryptographically relevant quantum computer that is capable of breaking RSA and ECC does not yet exist. However, we are offering hybrid post-quantum TLS with Kyber today so that customers can see how the performance differences of PQC affect their workloads. We also believe that the use of PQC raises the already-high security bar for connecting to AWS KMS and ACM, making this feature attractive for customers with long-term confidentiality needs.

Performance of hybrid post-quantum TLS with Kyber

Hybrid post-quantum TLS incurs a latency and bandwidth overhead compared to classical crypto alone. To quantify this overhead, we measured how long S2N-TLS takes to negotiate hybrid post-quantum (ECDHE + Kyber) key establishment compared to ECDHE alone. We performed the tests with the Linux perf subsystem on an Amazon Elastic Compute Cloud (Amazon EC2) c6i.4xlarge instance in the US East (Northern Virginia) AWS Region, and we initiated 2,000 TLS connections to a test server running in the US West (Oregon) Region, to include typical internet latencies.

Figure 1 shows the latencies of a TLS handshake that uses classical ECDHE and hybrid post-quantum (ECDHE + Kyber) key establishment. The columns are separated to illustrate the CPU time spent by the client and server compared to the time spent sending data over the network.

Figure 1: Latency of classical compared to hybrid post-quantum TLS handshake

Figure 1: Latency of classical compared to hybrid post-quantum TLS handshake

Figure 2 shows the bytes sent and received during the TLS handshake, as measured by the client, for both classical ECDHE and hybrid post-quantum (ECDHE + Kyber) key establishment.

Figure 2: Bandwidth of classical compared to hybrid post-quantum TLS handshake

Figure 2: Bandwidth of classical compared to hybrid post-quantum TLS handshake

This data shows that the overhead for using hybrid post-quantum key establishment is 0.25 ms on the client, 0.23 ms on the server, and an additional 2,356 bytes on the wire. Intra-Region tests would result in lower network latency. Your latencies also might vary depending on network conditions, CPU performance, server load, and other variables.

The results show that the performance of Kyber is strong; the additional latency is one of the top contenders among the NIST PQC candidates that we analyzed in a previous blog post. In fact, the performance of these ciphers has improved during our latest test, because x86-64 assembly-optimized versions of these ciphers are now available for use.

Configure a Maven project for hybrid post-quantum TLS

In this section, we provide a Maven configuration and code example that will show you how to get started using our assembly-optimized, hybrid post-quantum TLS configuration with Kyber.

To configure a Maven project for hybrid post-quantum TLS

  1. Get the preview release of the AWS Common Runtime HTTP client for the AWS SDK for Java 2.x. Your Maven dependency configuration should specify version 2.17.69-PREVIEW or newer, as shown in the following code sample.

  2. Configure the desired cipher suite in your code’s initialization. The following code sample configures an AWS KMS client to use the latest hybrid post-quantum cipher suite.
    // Check platform support
        throw new RuntimeException(“Hybrid post-quantum cipher suites are not supported.”);
    // Configure HTTP client   
    SdkAsyncHttpClient awsCrtHttpClient = AwsCrtAsyncHttpClient.builder()
    // Create the AWS KMS async client
    KmsAsyncClient kmsAsync = KmsAsyncClient.builder()

With that, all calls made with your AWS KMS client will use hybrid post-quantum TLS. You can use the latest hybrid post-quantum cipher suite with ACM by following the preceding example but using an AcmAsyncClient instead.

Tune connection settings for hybrid post-quantum TLS

Although hybrid post-quantum TLS has some latency and bandwidth overhead on the initial handshake, that cost is amortized over the duration of the TLS session, and you can fine-tune your connection settings to help further reduce the cost. In this section, you learn three ways to reduce the impact of hybrid PQC on your TLS connections: connection pooling, connection timeouts, and TLS session resumption.

Connection pooling

Connection pools manage the number of active connections to a server. They allow a connection to be reused without closing and reopening it, which amortizes the cost of connection establishment over time. Part of a connection’s setup time is the TLS handshake, so you can use connection pools to help reduce the impact of an increase in handshake latency.

To illustrate this, we wrote a test application that generates approximately 200 transactions per second to a test server. We varied the maximum concurrency setting of the HTTP client and measured the latency of the test request. In the AWS CRT HTTP client, this is the maxConcurrency setting. If the connection pool doesn’t have an idle connection available, the request latency includes establishing a new connection. Using Wireshark, we captured the network traffic to observe the number of TLS handshakes that took place over the duration of the application. Figure 3 shows the request latency and number of TLS handshakes as the maxConcurrency setting is increased.

Figure 3: Median request latency and number of TLS handshakes as concurrency pool size increases

Figure 3: Median request latency and number of TLS handshakes as concurrency pool size increases

The biggest latency benefit occurred with a maxConcurrency value greater than 1. Beyond that, the latencies were past the point of diminishing returns. For all maxConcurrency values of 10 and below, additional TLS handshakes took place within the connections, but they didn’t have much impact on median latency. These inflection points will depend on your application’s request volume. The takeaway is that connection pooling allows connections to be reused, thereby spreading the cost of any increased TLS negotiation time over many requests.

More detail about using the maxConcurrency option can be found in the AWS SDK for Java API Reference.

Connection timeouts

Connection timeouts work in conjunction with connection pooling. Even if you use a connection pool, there is a limit to how long idle connections stay open before the pool closes them. You can adjust this time limit to save on connection establishment overhead.

A nice way to visualize this setting is to imagine bursty traffic patterns. Despite tuning the connection pool concurrency, your connections keep closing because the burst period is longer than the idle time limit. By increasing the maximum idle time, you can reuse these connections despite bursty behavior.

To simulate the impact of connection timeouts, we wrote a test application that starts 10 threads, each of which activate at the same time on a periodic schedule every 5 seconds for a minute. We set maxConcurrency to 10 to allow each thread to have its own connection. We set connectionMaxIdleTime of the AWS CRT HTTP client to 1 second for the first test; and to 10 seconds for the second test.

When the maximum idle time was 1 second, the connections for all 10 threads closed during the time between each burst. As a result, 100 total connections were formed over the life of the test, causing a median request latency of 20.3 ms. When we changed the maximum idle time to 10 seconds, the 10 initial connections were reused by each subsequent burst, reducing the median request latency to 5.9 ms.

By setting the connectionMaxIdleTime appropriately for your application, you can reduce connection establishment overhead, including TLS negotiation time, to help achieve time savings throughout the life of your application.

More detail about using the connectionMaxIdleTime option can be found in the AWS SDK for Java API Reference.

TLS session resumption

TLS session resumption allows a client and server to bypass the key agreement that is normally performed to arrive at a new shared secret. Instead, communication quickly resumes by using a shared secret that was previously negotiated, or one that was derived from a previous secret (the implementation details depend on the version of TLS in use). This feature requires that both the client and server support it, but if available, TLS session resumption allows the TLS handshake time and bandwidth increases associated with hybrid PQ to be amortized over the life of multiple connections.


As you learned in this post, hybrid post-quantum TLS with Kyber is available for AWS KMS and ACM. This new cipher suite raises the security bar and allows you to prepare your workloads for post-quantum cryptography. Hybrid key agreement has some additional overhead compared to classical ECDHE, but you can mitigate these increases by tuning your connection settings, including connection pooling, connection timeouts, and TLS session resumption. Begin using hybrid key agreement today with AWS KMS and ACM.

If you have feedback about this post, submit comments in the Comments section below.

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Brian Jarvis

Brian Jarvis

Brian is a Senior Software Engineer at AWS Cryptography. His interests are in post-quantum cryptography and cryptographic hardware. Previously, Brian worked in AWS Security, developing internal services used throughout the company. Brian holds a Bachelor’s degree from Vanderbilt University and a Master’s degree from George Mason University in Computer Engineering. He plans to finish his PhD “some day”.

Top 2021 AWS Security service launches security professionals should review – Part 1

Post Syndicated from Ryan Holland original https://aws.amazon.com/blogs/security/top-2021-aws-security-service-launches-part-1/

Given the speed of Amazon Web Services (AWS) innovation, it can sometimes be challenging to keep up with AWS Security service and feature launches. To help you stay current, here’s an overview of some of the most important 2021 AWS Security launches that security professionals should be aware of. This is the first of two related posts; Part 2 will highlight some of the important 2021 launches that security professionals should be aware of across all AWS services.

Amazon GuardDuty

In 2021, the threat detection service Amazon GuardDuty expanded the internal AWS security intelligence it consumes to use more of the intel that AWS internal threat detection teams collect, including additional nation-state threat intelligence. Sharing more of the important intel that internal AWS teams collect lets you quickly improve your protection. GuardDuty also launched domain reputation modeling. These machine learning models take all the domain requests from across all of AWS, and feed them into a model that allows AWS to categorize previously unseen domains as highly likely to be malicious or benign based on their behavioral characteristics. In practice, AWS is seeing that these models often deliver high-fidelity threat detections, identifying malicious domains 7–14 days before they are identified and available on commercial threat feeds.

AWS also launched second generation anomaly detection for GuardDuty. Shortly after the original GuardDuty launch in 2017, AWS added additional anomaly detection for user behavior analytics and monitoring for unusual activity of AWS Identity and Access Management (IAM) users. After receiving customer feedback that the original feature was a little too noisy, and that it was difficult to understand why some findings were generated, the GuardDuty analytics team rebuilt this functionality on an entirely new machine learning model, considerably reducing the number of detections and generating a more accurate positive-detection rate. The new model also added additional context that security professionals (such as analysts) can use to understand why the model shows findings as suspicious or unusual.

Since its introduction, GuardDuty has detected when AWS EC2 Role credentials are used to call AWS APIs from IP addresses outside of AWS. Beginning in early 2022, GuardDuty now supports detection when credentials are used from other AWS accounts, inside the AWS network. This is a complex problem for customers to solve on their own, which is why the GuardDuty team added this enhancement. The solution considers that there are legitimate reasons why a source IP address that is communicating with AWS services APIs might be different than the Amazon Elastic Compute Cloud (Amazon EC2) instance IP address, or a NAT gateway associated with the instance’s VPC. The enhancement also considers complex network topologies that route traffic to one or multiple VPCs—for example, AWS Transit Gateway or AWS Direct Connect.

Our customers are increasingly running container workloads in production; helping to raise the security posture of these workloads became an AWS development priority in 2021. GuardDuty for EKS Protection is one recent feature that has resulted from this investment. This new GuardDuty feature monitors Amazon Elastic Kubernetes Service (Amazon EKS) cluster control plane activity by analyzing Kubernetes audit logs. GuardDuty is integrated with Amazon EKS, giving it direct access to the Kubernetes audit logs without requiring you to turn on or store these logs. Once a threat is detected, GuardDuty generates a security finding that includes container details such as pod ID, container image ID, and associated tags. See below for details on how the new Amazon Inspector is also helping to protect containers.

Amazon Inspector

At AWS re:Invent 2021, we launched the new Amazon Inspector, a vulnerability management service that continually scans AWS workloads for software vulnerabilities and unintended network exposure. The original Amazon Inspector was completely re-architected in this release to automate vulnerability management and to deliver near real-time findings to minimize the time needed to discover new vulnerabilities. This new Amazon Inspector has simple one-click enablement and multi-account support using AWS Organizations, similar to our other AWS Security services. This launch also introduces a more accurate vulnerability risk score, called the Inspector score. The Inspector score is a highly contextualized risk score that is generated for each finding by correlating Common Vulnerability and Exposures (CVE) metadata with environmental factors for resources such as network accessibility. This makes it easier for you to identify and prioritize your most critical vulnerabilities for immediate remediation. One of the most important new capabilities is that Amazon Inspector automatically discovers running EC2 instances and container images residing in Amazon Elastic Container Registry (Amazon ECR), at any scale, and immediately starts assessing them for known vulnerabilities. Now you can consolidate your vulnerability management solutions for both Amazon EC2 and Amazon ECR into one fully managed service.

AWS Security Hub

In addition to a significant number of smaller enhancements throughout 2021, in October AWS Security Hub, an AWS cloud security posture management service, addressed a top customer enhancement request by adding support for cross-Region finding aggregation. You can now view all your findings from all accounts and all selected Regions in a single console view, and act on them from an Amazon EventBridge feed in a single account and Region. Looking back at 2021, Security Hub added 72 additional best practice checks, four new AWS service integrations, and 13 new external partner integrations. A few of these integrations are Atlassian Jira Service Management, Forcepoint Cloud Security Gateway (CSG), and Amazon Macie. Security Hub also achieved FedRAMP High authorization to enable security posture management for high-impact workloads.

Amazon Macie

Based on customer feedback, data discovery tool Amazon Macie launched a number of enhancements in 2021. One new feature, which made it easier to manage Amazon Simple Storage Service (Amazon S3) buckets for sensitive data, was criteria-based bucket selection. This Macie feature allows you to define runtime criteria to determine which S3 buckets should be included in a sensitive data-discovery job. When a job runs, Macie identifies the S3 buckets that match your criteria, and automatically adds or removes them from the job’s scope. Before this feature, once a job was configured, it was immutable. Now, for example, you can create a policy where if a bucket becomes public in the future, it’s automatically added to the scan, and similarly, if a bucket is no longer public, it will no longer be included in the daily scan.

Originally Macie included all managed data identifiers available for all scans. However, customers wanted more surgical search criteria. For example, they didn’t want to be informed if there were exposed data types in a particular environment. In September 2021, Macie launched the ability to enable/disable managed data identifiers. This allows you to customize the data types you deem sensitive and would like Macie to alert on, in accordance with your organization’s data governance and privacy needs.

Amazon Detective

Amazon Detective is a service to analyze and visualize security findings and related data to rapidly get to the root cause of potential security issues. In January 2021, Amazon Detective added a convenient, time-saving integration that allows you to start security incident investigation workflows directly from the GuardDuty console. This new hyperlink pivot in the GuardDuty console takes findings directly from the GuardDuty console into the Detective console. Another time-saving capability added was the IP address drill down functionality. This new capability can be useful to security forensic teams performing incident investigations, because it helps quickly determine the communications that took place from an EC2 instance under investigation before, during, and after an event.

In December 2021, Detective added support for AWS Organizations to simplify management for security operations and investigations across all existing and future accounts in an organization. This launch allows new and existing Detective customers to onboard and centrally manage the Detective graph database for up to 1,200 AWS accounts.

AWS Key Management Service

In June 2021, AWS Key Management Service (AWS KMS) introduced multi-Region keys, a capability that lets you replicate keys from one AWS Region into another. With multi-Region keys, you can more easily move encrypted data between Regions without having to decrypt and re-encrypt with different keys for each Region. Multi-Region keys are supported for client-side encryption using direct AWS KMS API calls, or in a simplified manner with the AWS Encryption SDK and Amazon DynamoDB Encryption Client.

AWS Secrets Manager

Last year was a busy year for AWS Secrets Manager, with four feature launches to make it easier to manage secrets at scale, not just for client applications, but also for platforms. In March 2021, Secrets Manager launched multi-Region secrets to automatically replicate secrets for multi-Region workloads. Also in March, Secrets Manager added three new rules to AWS Config, to help administrators verify that secrets in Secrets Manager are configured according to organizational requirements. Then in April 2021, Secrets Manager added a CSI driver plug-in, to make it easy to consume secrets from Amazon EKS by using Kubernetes’s standard Secrets Store interface. In November, Secrets Manager introduced a higher secret limit of 500,000 per account to simplify secrets management for independent software vendors (ISVs) that rely on unique secrets for a large number of end customers. Although launched in January 2022, it’s also worth mentioning Secrets Manager’s release of rotation windows to align automatic rotation of secrets with application maintenance windows.

Amazon CodeGuru and Secrets Manager

In November 2021, AWS announced a new secrets detector feature in Amazon CodeGuru that searches your codebase for hardcoded secrets. Amazon CodeGuru is a developer tool powered by machine learning that provides intelligent recommendations to detect security vulnerabilities, improve code quality, and identify an application’s most expensive lines of code.

This new feature can pinpoint locations in your code with usernames and passwords; database connection strings, tokens, and API keys from AWS; and other service providers. When a secret is found in your code, CodeGuru Reviewer provides an actionable recommendation that links to AWS Secrets Manager, where developers can secure the secret with a point-and-click experience.

Looking ahead for 2022

AWS will continue to deliver experiences in 2022 that meet administrators where they govern, developers where they code, and applications where they run. A lot of customers are moving to container and serverless workloads; you can expect to see more work on this in 2022. You can also expect to see more work around integrations, like CodeGuru Secrets Detector identifying plaintext secrets in code (as noted previously).

To stay up-to-date in the year ahead on the latest product and feature launches and security use cases, be sure to read the Security service launch announcements. Additionally, stay tuned to the AWS Security Blog for Part 2 of this blog series, which will provide an overview of some of the important 2021 launches that security professionals should be aware of across all AWS services.

If you’re looking for more opportunities to learn about AWS security services, check out AWS re:Inforce, the AWS conference focused on cloud security, identity, privacy, and compliance, which will take place June 28-29 in Houston, Texas.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Want more AWS Security news? Follow us on Twitter.


Ryan Holland

Ryan is a Senior Manager with GuardDuty Security Response. His team is responsible for ensuring GuardDuty provides the best security value to customers, including threat intelligence, behavioral analytics, and finding quality.


Marta Taggart

Marta is a Seattle-native and Senior Product Marketing Manager in AWS Security Product Marketing, where she focuses on data protection services. Outside of work you’ll find her trying to convince Jack, her rescue dog, not to chase squirrels and crows (with limited success).

How Ribbon Communications Built a Scalable, Resilient Robocall Mitigation Platform

Post Syndicated from Siva Rajamani original https://aws.amazon.com/blogs/architecture/how-ribbon-communications-built-a-scalable-resilient-robocall-mitigation-platform/

Ribbon Communications provides communications software, and IP and optical networking end-to-end solutions that deliver innovation, unparalleled scale, performance, and agility to service providers and enterprise.

Ribbon Communications is helping customers modernize their networks. In today’s data-hungry, 24/7 world, this equates to improved competitive positioning and business outcomes. Companies are migrating from on-premises equipment for telephony services and looking for equivalent as a service (aaS) offerings. But these solutions must still meet the stringent resiliency, availability, performance, and regulatory requirements of a telephony service.

The telephony world is inundated with robocalls. In the United States alone, there were an estimated 50.5 billion robocalls in 2021! In this blog post, we describe the Ribbon Identity Hub – a holistic solution for robocall mitigation. The Ribbon Identity Hub enables services that sign and verify caller identity, which is compliant to the ATIS standards under the STIR/SHAKEN framework. It also evaluates and scores calls for the probability of nuisance and fraud.

Ribbon Identity Hub is implemented in Amazon Web Services (AWS). It is a fully managed service for telephony service providers and enterprises. The solution is secure, multi-tenant, automatic scaling, and multi-Region, and enables Ribbon to offer managed services to a wide range of telephony customers. Ribbon ensures resiliency and performance with efficient use of resources in the telephony environment, where load ratios between busy and idle time can exceed 10:1.

Ribbon Identity Hub

The Ribbon Identity Hub services are separated into a data (call-transaction) plane, and a control plane.

Data plane (call-transaction)

The call-transaction processing is typically invoked on a per-call-setup basis where availability, resilience, and performance predictability are paramount. Additionally, due to high variability in load, automatic scaling is a prerequisite.

Figure 1. Data plane architecture

Figure 1. Data plane architecture

Several AWS services come together in a solution that meets all these important objectives:

  1. Amazon Elastic Container Service (ECS): The ECS services are set up for automatic scaling and span two Availability Zones. This provides the horizontal scaling capability, the self-healing capacity, and the resiliency across Availability Zones.
  2. Elastic Load Balancing – Application Load Balancer (ALB): This provides the ability to distribute incoming traffic to ECS services as the target. In addition, it also offers:
    • Seamless integration with the ECS Auto Scaling group. As the group grows, traffic is directed to the new instances only when they are ready. As traffic drops, traffic is drained from the target instances for graceful scale down.
    • Full support for canary and linear upgrades with zero downtime. Maintains full-service availability without any changes or even perception for the client devices.
  3. Amazon Simple Storage Service (S3): Transaction detail records associated with call-related requests must be securely and reliably maintained for over a year due to billing and other contractual obligations. Amazon S3 simplifies this task with high durability, lifecycle rules, and varied controls for retention.
  4. Amazon DynamoDB: Building resilient services is significantly easier when the compute processing can be stateless. Amazon DynamoDB facilitates such stateless architectures without compromise. Coupled with the availability of the Amazon DynamoDB Accelerator (DAX) caching layer, the solution can meet the extreme low latency operation requirements.
  5. AWS Key Management Service (KMS): Certain tenant configuration is highly confidential and requires elevated protection. Furthermore, the data is part of the state that must be recovered across Regions in disaster recovery scenarios. To meet the security requirements, the KMS is used for envelope encryption using per-tenant keys. Multi-Region KMS keys facilitates the secure availability of this state across Regions without the need for application-level intervention when replicating encrypted data.
  6. Amazon Route 53: For telephony services, any non-transient service failure is unacceptable. In addition to providing high degree of resiliency through Multi-AZ architecture, Identity Hub also provides Regional level high availability through its multi-Region active-active architecture. Route 53 with health checks provides for dynamic rerouting of requests within minutes to alternate Regions.

Control plane

The Identity Hub control plane is used for customer configuration, status, and monitoring. The API is REST-based. Since this is not used on a call-by-call basis, the requirements around latency and performance are less stringent, though the requirements around high resiliency and dynamic scaling still apply. In this area, ease of implementation and maintainability are key.

Figure 2. Control plane architecture

Figure 2. Control plane architecture

The following AWS services implement our control plane:

  1. Amazon API Gateway: Coupled with a custom authenticator, the API Gateway handles all the REST API credential verification and routing. Implementation of an API is transformed into implementing handlers for each resource, which is the application core of the API.
  2. AWS Lambda: All the REST API handlers are written as Lambda functions. By using the Lambda’s serverless and concurrency features, the application automatically gains self-healing and auto-scaling capabilities. There is also a significant cost advantage as billing is per millisecond of actual compute time used. This is significant for a control plane where usage is typically sparse and unpredictable.
  3. Amazon DynamoDB: A stateless architecture with Lambda and API Gateway, all persistent state must be stored in an external database. The database must match the resilience and auto-scaling characteristics of the rest of the control plane. DynamoDB easily fits the requirements here.

The customer portal, in addition to providing the user interface for control plane REST APIs, also delivers a rich set of user-customizable dashboards and reporting capability. Here again, the availability of various AWS services simplifies the implementation, and remains non-intrusive to the central call-transaction processing.

Services used here include:

  1. AWS Glue: Enables extraction and transformation of raw transaction data into a format useful for reporting and dashboarding. AWS Glue is particularly useful here as the data available is regularly expanding, and the use cases for the reporting and dashboarding increase.
  2. Amazon QuickSight: Provides all the business intelligence (BI) functionality, including the ability for Ribbon to offer separate author and reader access to their users, and implements tenant-based access separation.


Ribbon has successfully deployed Identity Hub to enable cloud hosted telephony services to mitigate robocalls. Telephony requirements around resiliency, performance, and capacity were not compromised. Identity Hub offers the benefits of a 24/7 fully managed service requiring no additional customer on-premises equipment.

Choosing AWS services for Identity Hub gives Ribbon the ability to scale and meet future growth. The ability to dynamically scale the service in and out also brings significant cost advantages in telephony applications where busy hour traffic is significantly higher than idle time traffic. In addition, the availability of global AWS services facilitates the deployment of services in customer-local geographic locations to meet performance requirements or local regulatory compliance.

Managing permissions with grants in AWS Key Management Service

Post Syndicated from Rick Yin original https://aws.amazon.com/blogs/security/managing-permissions-with-grants-in-aws-key-management-service/

AWS Key Management Service (AWS KMS) helps customers to use encryption to secure their data. When creating a new encrypted Amazon Web Services (AWS) resource, such as an Amazon Relational Database Service (Amazon RDS) database or an Amazon Simple Storage Service (Amazon S3) bucket, all you have to do is provide an AWS KMS key ID that you control and the data will be encrypted and the complexity of protecting and making encryption keys highly available is reduced.

If you’re considering delegating encryption to an AWS service to use a key under your control when it encrypts your data in that service, you might wonder how to ensure the AWS service can only use your key when you want it to and not have full access to decrypt any of your resources at any time. The answer is to use scoped-down dynamic permissions in AWS KMS. Specifically, a combination of permissions that you define in the KMS key policy document along with additional permissions that are created dynamically using KMS grants define the conditions under which one or more AWS services can use your KMS keys to encrypt and decrypt your data.

In this blog post, I discuss:

  • An example of how an AWS service uses your KMS key policy and grants to securely manage access to your encryption keys. The example uses Amazon RDS and demonstrates how the block storage volume behind your database instance is encrypted.
  • Best practices for using grants from AWS KMS in your own workloads.
  • Recent performance improvements when using grants in AWS KMS.

Case study: How RDS uses grants from AWS KMS to encrypt your database volume

Many Amazon RDS instance types are hosted on an Amazon Elastic Compute Cloud (Amazon EC2) instance where the underlying storage layer is an Amazon Elastic Block Store (Amazon EBS) volume. The blocks of the EBS volume that stores the database content are encrypted under a randomly generated 256-bit symmetric data key that is itself encrypted under a KMS key that you configure RDS to use when you create your database instance. Let’s look at how RDS interacts with EBS, EC2, and AWS KMS to securely create an RDS instance using an KMS key.

When you send a request to RDS to create your database, there are several asynchronous requests being made among the RDS, EC2, EBS, and KMS services to:

  1. Create the underlying storage volume with a unique encryption key.
  2. Create the compute instance in EC2.
  3. Load the database engine into the EC2 instance.
  4. Give the EC2 instance permissions to use the encryption key to read and write data to the database storage volume.

The initial authenticated request that you make to RDS to create a new database is made by an AWS Identity and Access Management (IAM) principal in your account (e.g. a user or role). Once the request is received, a series of things has to happen:

  1. RDS needs to request EBS to create an encrypted volume to store your future data.
  2. EBS needs to request AWS KMS generate a unique 256-bit data key for the volume and encrypt it under the KMS key you told RDS to use.
  3. RDS then needs to request that EC2 launch an instance, attach that encrypted volume, and make the data key available to EC2 for use in reads and writes to the volume.

From your perspective, the IAM principal used to create the database also must have permissions in the KMS key policy for the GenerateDataKeyWithoutPlaintext and Decrypt actions. This enables the unique 256-bit data key to be created and encrypted under the desired KMS key as well as allowing the user or role to have the data key decrypted and provisioned to the Nitro card managing your EC2 instance so that reads/writes can happen from/to the database. Given the asynchronous nature of the process of creating the database vs. launching the database volume in the future, how do the RDS, EBS, and EC2 services all get the necessary least privileged permissions to create and provision the data key for use with your database? The answer starts with your IAM principal having permission for the AWS KMS CreateGrant action in the key policy.

RDS uses the identity from your IAM principal to create a grant in AWS KMS that allows it to create other grants for EC2 and EBS with very limited permissions that are further scoped down compared to the original permissions your IAM principal has on the AWS KMS key. A total of three grants are created:

  • The initial RDS grant.
  • A subsequent EBS grant that allows EBS to call AWS KMS and generate a 256-bit data key that is encrypted under the KMS key you defined when creating your database.
  • The attachment grant, which allows the specific EC2 instance hosting your database volume to decrypt the encrypted data key for and provision it for use during I/O between the instance and the EBS volume.

RDS grant

In this example, let’s say you’ve created an RDS instance with an ID of db-1234 and specified a KMS key for encryption. The following grant is created on the KMS key, allowing RDS to create more grants for EC2 and EBS to use in the asynchronous processes required to launch your database instance. The RDS grant is as follows:

{Grantee Principal: '<Regional RDS Service Account>', Encryption Context: '"aws:rds:db-id": "db-1234"', Operations: ['CreateGrant', 'Decrypt', 'GenerateDataKeyWithoutPlaintext']}

In plain English, this grant gives RDS permissions to use the KMS key for three specific operations (API actions) only when the call specifies the RDS instance ID db-1234 in the Encryption Context parameter. The grant provides access for the the grantee principal, which in this case is the value shown for the <Regional RDS service account>. This grant is created in AWS KMS and associated with your KMS key. Because the EC2 instance hasn’t yet been created and launched, the grantee principal cannot include the EC2 instance ID and must instead be the regional RDS service account.

EBS grant

With the RDS instance and initial AWS KMS grant created, RDS requests EC2 to launch an instance for the RDS database. EC2 creates an instance with a unique ID (e.g. i-1234567890abcdefg) using EC2 permissions you gave to the original IAM principal. In addition to the EC2 instance being created, RDS requests that Amazon EBS create an encrypted volume dedicated to the database. As a part of volume creation, EBS needs permission to call AWS KMS to generate a unique 256-bit data key for the volume and encrypt that data key under the KMS key you defined.

The EC2 instance ID is used as the name of the identity for future calls to AWS KMS, so RDS inserts it as the grantee principal in the EBS grant it creates. The EBS grant is as follows:

{Grantee Principal: '<RDS-Host-Role>:i-1234567890abcdefg', Encryption Context: '"aws:rds:db-id": "db-1234"', Operations: ['CreateGrant', 'Decrypt', 'GenerateDataKeyWithoutPlaintext']}}

You’ll notice that this grant uses the same encryption context as the initial RDS grant. However, now that we have the EC2 instance ID associated with the database ID, the permissions that EBS gets to use your key as the grantee principal can be scoped down to require both values. Once this grant is created, EBS can create the EBS volume (e.g. vol-0987654321gfedcba) and call AWS KMS to generate and encrypt a 256-bit data key that can only be used for that volume. This encrypted data key is stored by EBS in preparation for the volume attachment process.

Attachment grant

The final step in creating the RDS instance is to attach the EBS volume to the EC2 instance hosting your database. EC2 now uses the previously created EBS grant to create the attachment grant with the i-1234567890abcdefg instance identity. This grant allows EC2 to decrypt the encrypted data key, provision it to the Nitro card that manages the instance, and begin encrypting I/O to the EBS volume of the RDS database. The attachment grant in this example will be as follows:

{Grantee Principal: 'EC2 Instance Role:i-1234567890abcdefg', Encryption Context: '"aws:rds:db-id": "db-1234", "aws:ebs:id":"vol-0987654321gfedcba"', Operations: ['Decrypt']}

The attachment grant is the most restrictive of the three grants. It requires the caller to know the IDs of all the AWS entities involved: EC2 instance ID, EBS volume ID, and RDS database ID. This design ensures that your KMS key can only be used for decryption by these AWS services in order to launch the specific RDS database you want.

The encrypted EBS volume is now active and attached to the EC2 instance. Should you terminate the RDS instance, the services retire all the relevant KMS grants so they no longer have any permission to use your KMS key to decrypt the 256-bit data key required to decrypt data in your database. If you need to launch your encrypted database again, a similar set of three grants will be dynamically created with the RDS database, EC2 instance, and EBS volume IDs used to scope down permissions on the AWS KMS key.

The process described in the previous paragraphs is graphically shown in Figure 1:
Figure 1: How Amazon RDS uses Amazon EC2, Amazon EBS, and AWS KMS to create an encrypted RDS instance

Considering all the AWS KMS key permissions that are added and removed as a part of launching a database, you might ask why not just use the key policy document to make these changes? A KMS key allows only one key policy with a maximum document size of 32 KB. Because one key could be used to encrypt any number of AWS resources, trying to dynamically add and remove scoped-down permissions related to each resource to the key policy document creates two risks. First, the maximum allowable size of the key policy document (32KB) might be exceeded. Second, depending on how many resources are being accessed concurrently, you may exceed the request rate quota for the PutKeyPolicy API action in AWS KMS.

In contrast, there can be any number of grants on a given AWS KMS key, each grant specifying a scoped-down permission for the use of a KMS key with any AWS service that integrated with AWS KMS. Grant creation and deletion is also designed for much higher-volume request rates than modifications to the key policy document. Finally, permission to call PutKeyPolicy is a highly privileged permission, as it lets the caller make unrestricted changes to the permissions on the key, including changes to administrative permissions to disable or schedule the key for deletion. Grants on a key can only allow permissions to use the key, not administer the key. Also, grants that allow the creation of other grants by other IAM principals prohibit the escalation of privilege. In the RDS example above, the permissions RDS receives from the IAM principal in your account during the first CreateGrant request cannot be more permissive than what you defined for the IAM principal in the KMS key policy. The permissions RDS gives to EC2 and EBS during the database creation process cannot be more permissive than the original permission RDS has from the initial grant. This design ensures that AWS services cannot escalate their privileges and use your KMS key for purposes different than what you intend.

Best practices for using AWS KMS grants

AWS KMS grants are a powerful tool to dynamically define permissions to use keys. They are automatically created when you use server-side encryption features in various AWS services. You can also use grants to control permission in your own applications that perform client-side encryption. Here are some best practices to consider:

  • Design the permissions to be as scoped down as possible. Use a specific grantee principal, such as an IAM role, and give the principal access only to the AWS KMS API actions that are needed. You can further limit the scope of grants with the Encryption Context parameter by using any element you want to ensure callers are using the AWS KMS key only for the intended purpose. Below is a specific example that grants AWS account 123456789012 permission to call the GenerateDataKey or Decrypt APIs, but only if the supplied encryption context for customerID is 5678.
    {Actions: 'GenerateDataKey, Decrypt', Grantee Principal: '123456789012', Encryption Context: '"customerID": "5678"'}

    This grant could prevent your application from decrypting data belonging to customer “5678” without explicitly passing the expected customerID in the request to AWS KMS. This may be a useful defense-in-depth mechanism to prevent unauthorized access to your customers’ data if your application’s AWS credentials were compromised and used from a different caller who doesn’t know that encryption context is a required parameter for all reads and writes in order to encrypt and decrypt data.

    For more information on how you can use encryption context in AWS KMS permissions, requests, and AWS CloudTrail logs, see How to Protect the Integrity of Your Encrypted Data by Using AWS Key Management Service and EncryptionContext.

  • Remember that grants don’t automatically expire. Your code needs to retire or revoke them once you know the permission is no longer needed on the KMS key. Grants that aren’t retired are leftover permissions that might create a security risk for encrypted resources. See retiring and revoking grants in the AWS KMS developer guide for more detail.
  • Avoid creating duplicate grants. A duplicate grant is a grant that shares the same AWS KMS key ID, API actions, grantee principal, encryption context, and name. If you retire the original grant after use and not the duplicates, then the leftover duplicate grants can lead to unintended access to encrypt or decrypt data.

Recent performance improvements to AWS KMS grants: Removing a resource quota

For customers who use AWS KMS to encrypt resources in AWS services that use grants, there used to be cases where AWS KMS had to enforce a quota on the number of concurrently active resources that could be encrypted under the same KMS key. For example, customers of Amazon RDS, Amazon WorkSpaces, or Amazon EBS would run into this quota at very large scale. This was the Grants for a given principal per key quota and was previously set to 500. You might have seen the error message “Keys only support 500 grants per grantee principal in this region” when trying to create a resource in one of these services.

We recently made a change to AWS KMS to remove this quota entirely and this error message no longer exists. With this quota removed, you can now attach unlimited grants to any KMS key when using any AWS service.


In this blog post, you’ve seen how services such as Amazon RDS use AWS KMS grants to pass scoped-down permissions through the Amazon EC2 and Amazon EBS instances. You also saw some best practices for using AWS KMS grants in your own applications. Finally, you learned about how AWS KMS has improved grants by removing one of the resource quotas.

Below are some additional resources for AWS KMS and grants.

If you have feedback about this post, submit comments in the Comments section below.

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Rick Yin

Rick is a software development engineer on the AWS KMS team. His current focus is helping to scale AWS KMS to meet increasing customer demand by making sure we can serve our requests at ultra-low latency and ultra-high availability. In his free time, Rick enjoys learning about history and trying to stay in shape. He has recently taken up rowing.

Field Notes: Analyze Cross-Account AWS KMS Call Usage with AWS CloudTrail and Amazon Athena

Post Syndicated from Abhijit Rajeshirke original https://aws.amazon.com/blogs/architecture/field-notes-analyze-cross-account-aws-kms-call-usage-with-aws-cloudtrail-and-amazon-athena/

Businesses are expanding their footprint on Amazon Web Services (AWS) and are adopting a multi-account strategy to help isolate and manage business applications and data. In the multi-account strategy, it is common to have business applications deployed in one account accessing an Amazon Simple Storage Service (Amazon S3) encrypted bucket from another AWS account.

When an application in an AWS account uses a AWS Key Management Service (AWS KMS) key owned by a different account, it’s known as a cross-account call. For cross-account requests, AWS KMS throttles the account that makes the requests, not the account that owns the AWS KMS key. These requests count toward the request quota of the caller account. Sometimes it’s essential to identify or track cross-account AWS KMS API usage. In this blog, you will learn about use cases to track these requests and steps to identify cross-account AWS KMS calls.

To understand the problem better, consider a scenario where you have multiple AWS accounts set up in a hub and spoke configuration as shown in the following diagram.  Each account is administered by a different administrator. Amazon S3 data lake is located in the centralized hub account. The data lake bucket is encrypted using server-side encryption with AWS KMS (SSE-KMS) with customer-managed keys. Multiple spoke accounts access datasets from this data lake bucket. When a spoke account uploads or downloads objects from the data lake, Amazon S3 makes a GenerateDataKey (for uploads) or Decrypt (for downloads) API request to AWS KMS on behalf of the spoke account. These API requests get applied toward AWS KMS quota of the spoke account.

In the following diagram (figure 1), spoke accounts B, C, and D are uploading/downloading files from the encrypted data lake located in hub account A. Related AWS KMS API quotas will get applied to spoke accounts even though encryption/decryption is happening at the data lake S3 bucket. For example, the centralized Amazon S3 data lake is located in hub account A with an account ID 111111111111. Amazon S3 data lake bucket is encrypted using AWS KMS key ARN ending in 3aa3c82a2174.

Spoke account B with account ID 222222222222 is downloading 1,811 files and uploading 749 files from the centralized data lake. A total of 2,560 AWS KMS API calls will be counted against the request quota for account B.

Spoke account C with account ID 33333333333 is downloading 997 files and uploading 271 files from centralized data lake. A total of 1,268 AWS KMS API calls will be counted against the request quota for account C.

Spoke account D with account ID 444444444444 is downloading 638 files and uploading 306 files from centralized data lake from centralized data lake. The total 944 AWS KMS API quotas will get applied to account D.

Spoke and hub accounts are owned by separate business units and owned by different account administrators.

Note: when you configure your bucket to use an S3 Bucket Key for SSE-KMS, you may not see separate Decrypt or GenerateDataKey for each file upload or download.

Figure 1: Architecture outlining the hub and spoke accounts

Figure 1: Architecture outlining the hub and spoke accounts

This architecture design works for the following three use cases.

Use case #1:

A spoke account administrator wants to track the individual AWS KMS key-wise encryption/decryption costs using AWS Cost Explorer and cost allocation tags. Tracking costs this way works well for the AWS KMS API calls made within the same spoke account and related costs will be displayed under appropriate cost allocation tags. However, for the cross account AWS KMS API calls, cost allocation tags will not be visible outside of the hub account and will be displayed under cost allocation tag “None.” Analyzing cross-account AWS KMS API calls will help administrator determine approximate percentage usage by each cross account KMS key.

Use case # 2:

The spoke account has multiple applications, and each application has a unique AWS Identity and Access Management (IAM) principal. The spoke account administrator would like to track encryption/decryption usage. Identifying IAM principal-wise cross account calls will help the administrator determine approximate percent usage by each IAM principal /each application.

Use case # 3:

The spoke account administrator wants to understand how much AWS KMS quota is used for the cross-account specific KMS keys.

Solution Overview

Let’s discuss how we can track cross-account AWS KMS calls using AWS CloudTrail and Amazon Athena. For this solution, we will reuse your existing CloudTrail or create new CloudTrail in a Region where the hub account Amazon S3 data lake is located. As shown in the following diagram, we will use Athena to query the CloudTrail data to identify cross account AWS KMS calls used for S3 encryption/decryption.

Figure 2- Spoke and hub architecture

Figure 2: Architecture outlining the CloudTrail and Athena Solution


For this walkthrough, you should have the following prerequisites:

  • AWS accounts (one for hub and at least one for spoke)
  • AWS KMS (SSE-KMS) with customer managed keys encrypted S3 bucket


Step 1: Activate AWS CloudTrail for the hub account

CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of your AWS account. With CloudTrail, you can log, continuously monitor, and retain account activity across your AWS accounts. If you have already activated CloudTrail, you can reuse the same. If you haven’t, you can activate it using the steps in this tutorial. For the proposed solution, you must enable CloudTrail for management events only. You don’t require CloudTrail for data events or insight events. Also, be aware that you need only single CloudTrail and creating duplicate cloud trails can increase the service cost.

Note:  you can analyze the data in Athena only when the CloudTrail data is available. Any access requests made prior to enabling CloudTrail cannot be analyzed. It takes up to 15 minutes for events to get to CloudTrail, and up to 5 minutes for CloudTrail to write to S3.

Step 2: Create Amazon Athena table to query the CloudTrail data

Amazon Athena is an interactive query service that analyzes data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Create an Athena table in any database or default database in a Region where your hub account S3 data lake bucket resides.

If you are using Athena for the first time, follow these steps to create a database. Once the database is created you need to create Athena table. Follow these steps to create a table:

  1. Open the Athena built-in query editor,
  2. copy the following query,
  3. modify as suggested,
  4. run the query.

In the LOCATION and storage.location.template clauses, replace the bucket with CloudTrail bucket. Replace accountId with hub account’s ID and replace awsRegion with region where data lake S3 bucket is located. For projection.timestamp.range, replace 2020/01/01 with the start date you want to use.

After successful initiation of the query, you will see the CloudTrail_logs table created in Athena.

CREATE EXTERNAL TABLE cloudtrail_logs_region(

    eventVersion STRING,

    userIdentity STRUCT<

        type: STRING,

        principalId: STRING,

        arn: STRING,

        accountId: STRING,

        invokedBy: STRING,

        accessKeyId: STRING,

        userName: STRING,

        sessionContext: STRUCT<

            attributes: STRUCT<

                mfaAuthenticated: STRING,

                creationDate: STRING>,

            sessionIssuer: STRUCT<

                type: STRING,

                principalId: STRING,

                arn: STRING,

                accountId: STRING,

                userName: STRING>>>,

    eventTime STRING,

    eventSource STRING,

    eventName STRING,

    awsRegion STRING,

    sourceIpAddress STRING,

    userAgent STRING,

    errorCode STRING,

    errorMessage STRING,

    requestParameters STRING,

    responseElements STRING,

    additionalEventData STRING,

    requestId STRING,

    eventId STRING,

    readOnly STRING,

    resources ARRAY<STRUCT<

        arn: STRING,

        accountId: STRING,

        type: STRING>>,

    eventType STRING,

    apiVersion STRING,

    recipientAccountId STRING,

    serviceEventDetails STRING,

    sharedEventID STRING,

    vpcEndpointId STRING



   `timestamp` string)

ROW FORMAT SERDE 'com.amazon.emr.hive.serde.CloudTrailSerde'

STORED AS INPUTFORMAT 'com.amazon.emr.cloudtrail.CloudTrailInputFormat'

OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'











Athena screenshot

Step 3: Identify cross account Amazon S3 encryption/decryption calls

Once the Athena table is created, you can run the following query to find out cross-account AWS KMS calls made for S3 encryption /decryption.


SELECT useridentity.accountid as requestor_account_id,

              resources[1].accountid as owner_account_id,

              resources[1].arn as key_arn,       

          count(resources) as count

  FROM CloudTrail_logs_us_east_2

  WHERE eventsource='kms.amazonaws.com'

          AND timestamp between '2021/04/01' and '2021/08/30'

          AND eventname in ('Decrypt','Encrypt','GenerateDataKey')

          AND useridentity.accountid!= resources[1].accountid

    AND json_extract(json_extract(requestparameters , '$.encryptionContext'),'$.aws:s3:arn') is not null

  GROUP BY  useridentity.accountid,resources[1].accountid,resources[1].arn

  ORDER BY  key_arn,count desc


Result displays the cross account AWS KMS calls made for S3 encryption /decryption i.e. where caller account is not the key owner account for time period between April 1, 2021 and August 30, 2021.

Athena screenshot 2

The preceding example shows cross-account AWS KMS API calls generated by downloading /uploading files from centralized Amazon S3 data lake located in account A (111111111111) from spoke accounts B (222222222222), C (333333333333), and D (444444444444).

These AWS KMS quotas will get applied to caller (spoke) accounts even though key owner is hub account.

For example:

  • 2,560 AWS KMS API call quotas will be applied to account B.
  • 1,644 AWS KMS API call quotas will be applied to account C.
  • 944 AWS KMS API call quotas will be applied to account D.

Step 4: Identify IAM principal-wise cross account Amazon S3 encryption/decryption calls.

To identify IAM principal-wise cross account Amazon S3 encryption /decryption calls, you can run following query.


SELECT useridentity.accountid as requestor_account_id,
useridentity.principalid as requestor_principal,
resources[1].accountid as owner_account_id,
resources[1].arn as key_arn,
count(resources) as count
FROM CloudTrail_logs_us_east_2
WHERE eventsource='kms.amazonaws.com'
AND timestamp between '2021/04/01' and '2021/08/30'
AND eventname in ('Decrypt','Encrypt','GenerateDataKey')
AND useridentity.accountid!= resources[1].accountid
AND json_extract(json_extract(requestparameters , '$.encryptionContext'),'$.aws:s3:arn') is not null
GROUP BY useridentity.accountid,useridentity.principalid,resources[1].accountid,resources[1].arn
ORDER BY requestor_account_id,count desc


Athena screenshot 3

The preceding result shows  AWS Identity and Access Management (IAM) principal-wise cross-account AWS KMS API calls made between hub and spoke accounts. For example, Account B (22222222222) has two applications configured with IAM principals ids ending with 4C5VIMGI2, 4YFPRTQMP are accessing the centralized S3 bucket located in hub account A (111111111111).

For the time period between ‘2021/04/01’ and ‘2021/08/30’, the application configured with IAM principal ending in 4C5VIMGI2 made 1622 cross-account AWS KMS API calls. During this same time period, the application configured with IAM principal 4YFPRTQMP made 936 cross-account AWS KMS API calls.

We can further filter the results to see only KMS key ARN ending with 3aa3c82a2174 to get application- wise % of AWS KMS API calls made to the Amazon S3 centralized data lake from all the spoke accounts.

account ID table

Note:  we assume that each application is configured with a unique IAM principal.

Step 5: Identify cross account Amazon S3 encryption/decryption calls by events.


SELECT useridentity.accountid as requestor_account_id,

              resources[1].accountid as owner_account_id,

              resources[1].arn as key_arn,

              eventname as eventname,

          count(resources) as count

  FROM CloudTrail_logs_us_east_2

  WHERE eventsource='kms.amazonaws.com'

          AND timestamp between '2021/04/01' and '2021/08/30'

          AND useridentity.accountid!= resources[1].accountid

        AND json_extract(json_extract(requestparameters , '$.encryptionContext'),'$.aws:s3:arn') is not null

  GROUP BY useridentity.accountid, resources [1].accountid,resources[1].arn,eventname

  ORDER BY requestor_account_id,count desc


Athena screenshot 4

Amazon S3 makes decrypt API requests when you download the files and GenerateDataKey API request when you upload the file to encrypted S3 bucket. The result shows that:

  • Spoke account B (22222222222) made 1811 decrypt API requests to download 1811 files and 749 GenerateDataKey API requests to uploaded 749 files.
  • Spoke account C (33333333333) made 1373 decrypt API requests to download 1373 files and 271 GenerateDataKey API requests to uploaded 271 files.
  • Spoke account D (444444444444) made 638 decrypt API requests to download 638 files and 306 GenerateDataKey API requests to uploaded 306 files.

Note: When you configure your bucket to use an S3BucketKey for SSE-KMS, you may not have a separate Decrypt or GenerateDataKey for each file upload or download.

Step 6: Identify all the AWS KMS Calls.

To analyze the hub account for all the AWS KMS API calls made, run following query.


SELECT useridentity.accountid as requestor_account_id,

              resources[1].accountid as owner_account_id,

              resources[1].arn as key_arn,

          count(resources) as count

  FROM CloudTrail_logs_us_east_2

  WHERE eventsource='kms.amazonaws.com'

          AND timestamp between '2021/04/01' and '2021/08/30'

  GROUP BY useridentity.accountid, resources [1].accountid,resources[1].arn

  ORDER BY requestor_account_id,count desc


Athena screenshot 5

Results show all the AWS KMS API calls made in the hub account both within the account and across accounts. From this result, we can analyze that for centralized S3 data lake (KMS key ARN ending with 3aa3c82a2174), the majority of the calls are cross account AWS KMS API call and only 303 calls are made within account. You can do further analysis by refining the Amazon Athena queries based on your needs.

Cleaning up

To avoid incurring future charges, delete the resources that are no longer required.

Step 1: Delete the CloudTrail created in hub account

If you have created CloudTrail specifically for this solution, you can delete the CloudTrail by following the instructions in this user guide.

Step 2: Drop the Amazon Athena table

Log in to the Amazon Athena console and run the following drop table query:

Drop table < CloudTrail_logs_aws_region_1>


Tracking use of the cross-account AWS KMS APIs can be challenging in a multi-account scenario. In this blog, we learned how to use AWS CloudTrail and Amazon Athena to analyze AWS KMS API usage. In a hub and spoke account model, cross-account AWS KMS API quotas are applied to the spoke account when the spoke account accesses SSE-KMS encrypted S3 bucket in the hub account. You learned to analyze cross-account AWS KMS API quotas using AWS CloudTrail and Amazon Athena.  Finally, we learned how we can identify all the AWS KMS API call within account for period of time and analyze AWS KMS API traffic within account and across account. You can repeat the process and aggregate the data across Regions.

Additional Reading:

Manage your AWS KMS API request rates using Service Quotas and Amazon CloudWatch

Why did my CloudTrail cost and usage increase unexpectedly?

User Guide: Managing CloudTrail Costs

How Parametric Built Audit Surveillance using AWS Data Lake Architecture

Post Syndicated from Raghavarao Sodabathina original https://aws.amazon.com/blogs/architecture/how-parametric-built-audit-surveillance-using-aws-data-lake-architecture/

Parametric Portfolio Associates (Parametric), a wholly owned subsidiary of Morgan Stanley, is a registered investment adviser. Parametric provides investment advisory services to individual and institutional investors around the world. Parametric manages over 100,000 client portfolios with assets under management exceeding $400B (as of 9/30/21).

As a registered investment adviser, Parametric is subject to numerous regulatory requirements. The Parametric Compliance team conducts regular reviews on the firm’s portfolio management activities. To accomplish this, the organization needs both active and archived audit data to be readily available.

Parametric’s on-premises data lake solution was based on an MS-SQL server. They used an Apache Hadoop platform for their data storage, data management, and analytics. Significant gaps existed with the on-premises solution, which complicated audit processes. They were spending a large amount of effort on system maintenance, operational management, and software version upgrades. This required expensive consulting services and challenges with keeping the maintenance windows updated. This limited their agility, and also impacted their ability to derive more insights and value from their data. In an environment of rapid growth, adoption of more sophisticated analytics tools and processes has been slower to evolve.

In this blog post, we will show how Parametric implemented their Audit Surveillance Data Lake on AWS with purpose-built fully managed analytics services. With this solution, Parametric was able to respond to various audit requests within hours rather than days or weeks. This resulted in a system with a cost savings of 5x, with no data growth. Additionally, this new system can seamlessly support a 10x data growth.

Audit surveillance platform

The Parametric data management office (DMO) was previously running their data workloads using an on-premises data lake, which ran on the Hortonworks data platform of Apache Hadoop. This platform wasn’t up to date, and Parametric’s hardware was reaching end-of-life. Parametric was faced with a decision to either reinvest in their on-premises infrastructure or modernize their infrastructure using a modern data analytics platform on AWS. After doing a detailed cost/benefit analysis, the DMO calculated a 5x cost savings by using AWS. They decided to move forward and modernize with AWS due to these cost benefits, in addition to elasticity and security features.

The PPA compliance team asked the DMO to provide an enterprise data service to consume data from a data lake. This data was destined for downstream applications and ad-hoc data querying capabilities. It was accessed via standard JDBC tools and user-friendly business intelligence dashboards. The goal was to ensure that seven years of audit data would be readily available.

The DMO team worked with AWS to conceptualize an audit surveillance data platform architecture and help accelerate the implementation. They attended a series of AWS Immersion Days focusing on AWS fundamentals, Data Lakes, Devops, Amazon EMR, and serverless architectures. They later were involved in a four-day AWS Data Lab with AWS SMEs to create a data lake. The first use case in this Lab was creating the Audit Surveillance system on AWS.

Audit surveillance architecture on AWS

The following diagram shows the Audit Surveillance data lake architecture on AWS by using AWS purpose-built analytics services.

Figure 1. Audit Surveillance data lake architecture diagram

Figure 1. Audit Surveillance data lake architecture diagram

Architecture flow

  1. User personas: As first step, the DMO team identified three user personas for the Audit Surveillance system on AWS.
    • Data service compliance users who would like to consume audit surveillance data from the data lake into their respective applications through an enterprise data service.
    • Business users who would like to create business intelligence dashboards using a BI tool to audit data for compliance needs.
    • Complaince IT users who would like to perform ad-hoc queries on the data lake to perform analytics using an interactive query tool.
  2. Data ingestion: Data is ingested into Amazon Simple Storage Service (S3) from different on-premises data sources by using AWS Lake Formation blueprints. AWS Lake Formation provides workflows that define the data source and schedule to import data into the data lake. It is a container for AWS Glue crawlers, jobs, and triggers that are used to orchestrate the process to load and update the data lake.
  3. Data storage: Parametric used Amazon S3 as a data storage to build an Audit Surveillance data lake, as it has unmatched 11 nines of durability and 99.99% availability. The existing Hadoop storage was replaced with Amazon S3. The DMO team created a drop zone (raw), an analytics zone (transformed), and curated (enriched) storage layers for their data lake on AWS.
  4. Data cataloging: AWS Glue Data Catalog was the central catalog used to store and manage metadata for all datasets hosted in the Audit Surveillance data lake. The existing Hadoop metadata store was replaced with AWS Glue Data Catalog. AWS services such as AWS Glue, Amazon EMR, and Amazon Athena, natively integrate with AWS Glue Data Catalog.
  5. Data processing: Amazon EMR and AWS Glue process the raw data and places it into analytics zones (transformed) and curated zones (enriched) S3 buckets. Amazon EMR was used for big data processing and AWS Glue for standard ETL processes. AWS Lambda and AWS Step Functions were used to initiate monitoring and ETL processes.
  6. Data consumption: After Audit Surveillance data was transformed and enriched, the data was consumed by various personas within the firm as follows:
    • AWS Lambda and Amazon API Gateway were used to support consumption for data service compliance users.
    • Amazon QuickSight was used to create business intelligence dashboards for compliance business users.
    • Amazon Athena was used to query transformed and enriched data for compliance IT users.
  7. Security: AWS Key Management Service (KMS) customer managed keys were used for encryption at rest, and TLS for encryption at transition. Access to the encryption keys is controlled using AWS Identity and Access Management (IAM) and is monitored through detailed audit trails in AWS CloudTrail. Amazon CloudWatch was used for monitoring, and thresholds were created to determine when to send alerts.
  8. Governance: AWS IAM roles were attached to compliance users that permitted the administrator to grant access. This was only given to approved users or programs that went through authentication and authorization through AWS SSO. Access is logged and permissions can be granted or denied by the administrator. AWS Lake Formation is used for fine-grained access controls to grant/revoke permissions at the database, table, or column-level access.


The Parametric DMO team successfully replaced their on-premises Audit Surveillance Data Lake. They now have a modern, flexible, highly available, and scalable data platform on AWS, with purpose-built analytics services.

This change resulted in a 5x cost savings, and provides for a 10x data growth. There are now fast responses to internal and external audit requests (hours rather than days or weeks). This migration has given the company access to a wider breadth of AWS analytics services, which offers greater flexibility and options.

Maintaining the on-premises data lake would have required significant investment in both hardware upgrade costs and annual licensing and upgrade vendor consulting fees. Parametric’s decision to migrate their on-premises data lake has yielded proven cost benefits. And it has introduced new functions, service, and capabilities that were previously unavailable to Parametric DMO.

You may also achieve similar efficiencies and increase scalability by migrating on-premises data platforms into AWS. Read more and get started on building Data Lakes on AWS.

How US federal agencies can use AWS to encrypt data at rest and in transit

Post Syndicated from Robert George original https://aws.amazon.com/blogs/security/how-us-federal-agencies-can-use-aws-to-encrypt-data-at-rest-and-in-transit/

This post is part of a series about how Amazon Web Services (AWS) can help your US federal agency meet the requirements of the President’s Executive Order on Improving the Nation’s Cybersecurity. You will learn how you can use AWS information security practices to meet the requirement to encrypt your data at rest and in transit, to the maximum extent possible.

Encrypt your data at rest in AWS

Data at rest represents any data that you persist in non-volatile storage for any duration in your workload. This includes block storage, object storage, databases, archives, IoT devices, and any other storage medium on which data is persisted. Protecting your data at rest reduces the risk of unauthorized access when encryption and appropriate access controls are implemented.

AWS KMS provides a streamlined way to manage keys used for at-rest encryption. It integrates with AWS services to simplify using your keys to encrypt data across your AWS workloads. It uses hardware security modules that have been validated under FIPS 140-2 to protect your keys. You choose the level of access control that you need, including the ability to share encrypted resources between accounts and services. AWS KMS logs key usage to AWS CloudTrail to provide an independent view of who accessed encrypted data, including AWS services that are using keys on your behalf. As of this writing, AWS KMS integrates with 81 different AWS services. Here are details on recommended encryption for workloads using some key services:

You can use AWS KMS to encrypt other data types including application data with client-side encryption. A client-side application or JavaScript encrypts data before uploading it to S3 or other storage resources. As a result, uploaded data is protected in transit and at rest. Customer options for client-side encryption include the AWS SDK for KMS, the AWS Encryption SDK, and use of third-party encryption tools.

You can also use AWS Secrets Manager to encrypt application passwords, connection strings, and other secrets. Database credentials, resource names, and other sensitive data used in AWS Lambda functions can be encrypted and accessed at run time. This increases the security of these secrets and allows for easier credential rotation.

KMS HSMs are FIPS 140-2 validated and accessible using FIPS validated endpoints. Agencies with additional requirements that require a FIPS 140-3 validated hardware security module (HSM) (for example, for securing third-party secrets managers) can use AWS CloudHSM.

For more information about AWS KMS and key management best practices, visit these resources:

Encrypt your data in transit in AWS

In addition to encrypting data at rest, agencies must also encrypt data in transit. AWS provides a variety of solutions to help agencies encrypt data in transit and enforce this requirement.

First, all network traffic between AWS data centers is transparently encrypted at the physical layer. This data-link layer encryption includes traffic within an AWS Region as well as between Regions. Additionally, all traffic within a virtual private cloud (VPC) and between peered VPCs is transparently encrypted at the network layer when you are using supported Amazon EC2 instance types. Customers can choose to enable Transport Layer Security (TLS) for the applications they build on AWS using a variety of services. All AWS service endpoints support TLS to create a secure HTTPS connection to make API requests.

AWS offers several options for agency-managed infrastructure within the AWS Cloud that needs to terminate TLS. These options include load balancing services (for example, Elastic Load Balancing, Network Load Balancer, and Application Load Balancer), Amazon CloudFront (a content delivery network), and Amazon API Gateway. Each of these endpoint services enable customers to upload their digital certificates for the TLS connection. Digital certificates then need to be managed appropriately to account for expiration and rotation requirements. AWS Certificate Manager (ACM) simplifies generating, distributing, and rotating digital certificates. ACM offers publicly trusted certificates that can be used in AWS services that require certificates to terminate TLS connections to the internet. ACM also provides the ability to create a private certificate authority (CA) hierarchy that can integrate with existing on-premises CAs to automatically generate, distribute, and rotate certificates to secure internal communication among customer-managed infrastructure.

Finally, you can encrypt communications between your EC2 instances and other AWS resources that are connected to your VPC, such as Amazon Relational Database Service (Amazon RDS) databasesAmazon Elastic File System (Amazon EFS) file systemsAmazon S3Amazon DynamoDBAmazon Redshift, Amazon EMR, Amazon OpenSearch Service, Amazon ElasticCache for RedisAmazon FSx Windows File Server, AWS Direct Connect (DX) MACsec, and more.


This post has reviewed services that are used to encrypt data at rest and in transit, following the Executive Order on Improving the Nation’s Cybersecurity. I discussed the use of AWS KMS to manage encryption keys that handle the management of keys for at-rest encryption, as well as the use of ACM to manage certificates that protect data in transit.

Next steps

To learn more about how AWS can help you meet the requirements of the executive order, see the other posts in this series:

Subscribe to the AWS Public Sector Blog newsletter to get the latest in AWS tools, solutions, and innovations from the public sector delivered to your inbox, or contact us.

If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.


Robert George

Robert is a Solutions Architect on the Worldwide Public Sector (WWPS) team who works with customers to design secure, high-performing, and cost-effective systems in the AWS Cloud. He has previously worked in cybersecurity roles focused on designing security architectures, securing enterprise systems, and leading incident response teams for highly regulated environments.

Encrypt global data client-side with AWS KMS multi-Region keys

Post Syndicated from Jeremy Stieglitz original https://aws.amazon.com/blogs/security/encrypt-global-data-client-side-with-aws-kms-multi-region-keys/

Today, AWS Key Management Service (AWS KMS) is introducing multi-Region keys, a new capability that lets you replicate keys from one Amazon Web Services (AWS) Region into another. Multi-Region keys are designed to simplify management of client-side encryption when your encrypted data has to be copied into other Regions for disaster recovery or is replicated in Amazon DynamoDB global tables.

In this blog post, we give an overview of how we got here and how to get started using multi-Region keys. We include a code example for multi-Region encryption of data in DynamoDB global tables.

How we got here

From its inception, AWS KMS has been strictly isolated to a single AWS Region for each implementation, with no sharing of keys, policies, or audit information across Regions. Region isolation can help you comply with security standards and data residency requirements. However, not sharing keys across Regions creates challenges when you need to move data that depends on those keys across Regions. AWS services that use your KMS keys for server-side encryption address this challenge by transparently re-encrypting data on your behalf using the KMS keys you designate in the destination Region. If you use client-side encryption, this work adds extra complexity and latency of re-encrypting between regionally isolated KMS keys.

Multi-Region keys are a new feature from AWS KMS for client-side applications that makes KMS-encrypted ciphertext portable across Regions. Multi-Region keys are a set of interoperable KMS keys that have the same key ID and key material, and that you can replicate to different Regions within the same partition. With symmetric multi-Region keys, you can encrypt data in one Region and decrypt it in a different Region. With asymmetric multi-Region keys, you encrypt, decrypt, sign, and verify messages in multiple Regions.

Multi-Region keys are supported in the AWS KMS console, the AWS KMS API, the AWS Encryption SDK, Amazon DynamoDB Encryption Client, and Amazon S3 Encryption Client. AWS services also let you configure multi-Region keys for server-side encryption in case you want the same key to protect data that needs both server-side and client-side encryption.

Getting started with multi-Region keys

To use multi-Region keys, you create a primary multi-Region key with a new key ID and key material. Then, you use the primary key to create a related multi-Region replica key in a different Region of the same AWS partition. Replica keys are KMS keys that can be used independently; they aren’t a pointer to the primary key. The primary and replica keys share only certain properties, including their key ID, key rotation, and key origin. In all other aspects, every multi-Region key, whether primary or replica, is a fully functional, independent KMS key resource with its own key policy, aliases, grants, key description, lifecycle, and other attributes. The key Amazon Resource Names (ARN) of related multi-Region keys differ only in the Region portion, as shown in the following figure (Figure 1).

Figure 1: Multi-Region keys have unique ARNs but identical key IDs

Figure 1: Multi-Region keys have unique ARNs but identical key IDs

You cannot convert an existing single-Region key to a multi-Region key. This design ensures that all data protected with existing single-Region keys maintain the same data residency and data sovereignty properties.

When to use multi-Region keys

You can use multi-Region keys in any client-side application. Since multi-Region keys avoid cross-Region calls, they’re especially useful for scenarios where you don’t want to depend on another Region or incur the latency of a cross-Region call. For example, disaster recovery, global data management, distributed signing applications, and active-active applications that span multiple Regions can all benefit from using multi-Region keys. You can also create and use multi-Region keys in a single Region and choose to replicate those keys at some later date when you need to move protected data to additional Regions.

Note: If your application will run in only one Region, you should continue to use single-Region keys to benefit from their data isolation properties.

One significant benefit of multi-Region keys is using them with DynamoDB global tables. Let’s explore that interaction in detail.

Using multi-Region keys with DynamoDB global tables

AWS KMS multi-Region keys (MRKs) can be used with the DynamoDB Encryption Client to protect data in DynamoDB global tables. You can configure the DynamoDB Encryption Client to call AWS KMS for decryption in a different Region than the one in which the data was encrypted, as shown in the following figure (Figure 2). This is useful for disaster recovery, or simply to improve performance when using DynamoDB in a globally distributed application.

Figure 2: Using multi-Region keys with DynamoDB global tables

Figure 2: Using multi-Region keys with DynamoDB global tables

The steps described in Figure 2 are:

  1. Encrypt record with primary MRK
  2. Put encrypted record
  3. Global table replication
  4. Get encrypted record
  5. Decrypt record with replica MRK

Create a multi-Region primary key

Begin by creating a multi-Region primary key and replicating it into your backup Regions. We’ll assume that you’ve created a DynamoDB global table that’s replicated to the same Regions.

Configure the DynamoDB Encryption Client to encrypt records

To use AWS KMS multi-Region keys, you need to configure the DynamoDB Encryption Client with the Region you want to call, which is typically the Region where the application is running. Then, you need to configure the ARN of the KMS key you want to use in that Region.

This example encrypts records in us-east-1 (US East (N. Virginia)) and decrypts records in us-west-2 (US West (Oregon)). If you use the following example configuration code, be sure to replace the example key ARNs with valid key ARNs for your multi-Region keys.

// Specify the multi-Region key in the us-east-1 Region
String encryptRegion = "us-east-1";
String cmkArnEncrypt = "arn:aws:kms:us-east-1:<111122223333>:key/<mrk-1234abcd12ab34cd56ef12345678990ab>";

// Set up SDK clients for KMS and DDB in us-east-1
AWSKMS kmsEncrypt = AWSKMSClientBuilder.standard().withRegion(encryptRegion).build();
AmazonDynamoDB ddbEncrypt = AmazonDynamoDBClientBuilder.standard().withRegion(encryptRegion).build();

// Configure the example global table
String tableName = "global-table-example";
String employeeIdAttribute = "employeeId";
String nameAttribute = "name";

// Configure attribute actions for the Dynamo DB Encryption Client
//   Sign the employee ID field
//   Encrypt and sign the name field
Map<String, Set<EncryptionFlags>> actions = new HashMap<>();
actions.put(employeeIdAttribute, EnumSet.of(EncryptionFlags.SIGN));
actions.put(nameAttribute, EnumSet.of(EncryptionFlags.ENCRYPT, EncryptionFlags.SIGN));

// Set an encryption context. This is an optional best practice.
final EncryptionContext encryptionContext = new EncryptionContext.Builder()

// Use the Direct KMS materials provider and the DynamoDB encryptor
// Specify the key ARN of the multi-Region key in us-east-1
DirectKmsMaterialProvider cmpEncrypt = new DirectKmsMaterialProvider(kmsEncrypt, cmkArnEncrypt);
DynamoDBEncryptor encryptor = DynamoDBEncryptor.getInstance(cmpEncrypt);

// Create a record, encrypt it, 
// and put it in the DynamoDB global table
Map<String, AttributeValue> rec = new HashMap<>();
rec.put(nameAttribute, new AttributeValue().withS("Andy"));
rec.put(employeeIdAttribute, new AttributeValue().withS("1234"));

final Map<String, AttributeValue> encryptedRecord = encryptor.encryptRecord(rec, actions, encryptionContext);
ddbEncrypt.putItem(tableName, encryptedRecord);

When you save the newly encrypted record, DynamoDB global tables automatically replicates this encrypted record to the replica tables in the us-west-2 Region.

Configure the DynamoDB Encryption Client to decrypt data

Now you’re ready to configure a DynamoDB client to decrypt the record in us-west-2 where both the replica table and the replica multi-Region key exist.

// Specify the Region and key ARN to use when decrypting          
String decryptRegion = "us-west-2";
String cmkArnDecrypt = "arn:aws:kms:us-west-2:<111122223333>:key/<mrk-1234abcd12ab34cd56ef12345678990ab>";

// Set up SDK clients for KMS and DDB in us-west-2
AWSKMS kmsDecrypt = AWSKMSClientBuilder.standard()

AmazonDynamoDB ddbDecrypt = AmazonDynamoDBClientBuilder.standard()

// Configure the DynamoDB Encryption Client
// Use the Direct KMS materials provider and the DynamoDB encryptor
// Specify the key ARN of the multi-Region key in us-west-2
final DirectKmsMaterialProvider cmpDecrypt = new DirectKmsMaterialProvider(kmsDecrypt, cmkArnDecrypt);
final DynamoDBEncryptor decryptor = DynamoDBEncryptor.getInstance(cmpDecrypt);

// Set up your query
Map<String, AttributeValue> query = new HashMap<>();
query.put(employeeIdAttribute, new AttributeValue().withS("1234"));

// Get a record from DDB and decrypt it
final Map<String, AttributeValue> retrievedRecord = ddbDecrypt.getItem(tableName, query).getItem();
final Map<String, AttributeValue> decryptedRecord = decryptor.decryptRecord(retrievedRecord, actions, encryptionContext);

Note: This example encrypts with the primary multi-Region key and then decrypts with a replica multi-Region key. The process could also be reversed—every multi-Region key can be used in the encryption or decryption of data.


In this blog post, we showed you how to use AWS KMS multi-Region keys with client-side encryption to help secure data in global applications without sacrificing high availability or low latency. We also showed you how you can start working with a global application with a brief example of using multi-Region keys with the DynamoDB Encryption Client and DynamoDB global tables.

This blog post is a brief introduction to the ways you can use multi-Region keys. We encourage you to read through the Using multi-Region keys topic to learn more about their functionality and design. You’ll learn about:

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS KMS forum.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.


Jeremy Stieglitz

Jeremy is the Principal Product Manager for AWS Key Management Service (KMS) where he drives global product strategy and roadmap for AWS KMS. Jeremy has more than 20 years of experience defining new products and platforms, launching and scaling cryptography solutions, and driving end-to-end product strategies. Jeremy is the author or co-author of 23 patents in network security, user authentication and network automation and control.


Peter Zieske

Peter is a Senior Software Developer on the AWS Key Management Service team, where he works on developing features on the service-side front-end. Outside of work, he enjoys building with LEGO, gaming, and spending time with family.


Ben Farley

Ben is a Senior Software Developer on the AWS Crypto Tools team, where he works on client-side encryption libraries that help customers protect their data. Before that, he spent time focusing on the scalability and availability of services like AWS Identity and Access Management and AWS Key Management Service. Outside of work, he likes to explore the mountains with his fiancée and dog.

How to protect sensitive data for its entire lifecycle in AWS

Post Syndicated from Raj Jain original https://aws.amazon.com/blogs/security/how-to-protect-sensitive-data-for-its-entire-lifecycle-in-aws/

Many Amazon Web Services (AWS) customer workflows require ingesting sensitive and regulated data such as Payments Card Industry (PCI) data, personally identifiable information (PII), and protected health information (PHI). In this post, I’ll show you a method designed to protect sensitive data for its entire lifecycle in AWS. This method can help enhance your data security posture and be useful for fulfilling the data privacy regulatory requirements applicable to your organization for data protection at-rest, in-transit, and in-use.

An existing method for sensitive data protection in AWS is to use the field-level encryption feature offered by Amazon CloudFront. This CloudFront feature protects sensitive data fields in requests at the AWS network edge. The chosen fields are protected upon ingestion and remain protected throughout the entire application stack. The notion of protecting sensitive data early in its lifecycle in AWS is a highly desirable security architecture. However, CloudFront can protect a maximum of 10 fields and only within HTTP(S) POST requests that carry HTML form encoded payloads.

If your requirements exceed CloudFront’s native field-level encryption feature, such as a need to handle diverse application payload formats, different HTTP methods, and more than 10 sensitive fields, you can implement field-level encryption yourself using the Lambda@Edge feature in CloudFront. In terms of choosing an appropriate encryption scheme, this problem calls for an asymmetric cryptographic system that will allow public keys to be openly distributed to the CloudFront network edges while keeping the corresponding private keys stored securely within the network core. One such popular asymmetric cryptographic system is RSA. Accordingly, we’ll implement a Lambda@Edge function that uses asymmetric encryption using the RSA cryptosystem to protect an arbitrary number of fields in any HTTP(S) request. We will discuss the solution using an example JSON payload, although this approach can be applied to any payload format.

A complex part of any encryption solution is key management. To address that, I use AWS Key Management Service (AWS KMS). AWS KMS simplifies the solution and offers improved security posture and operational benefits, detailed later.

Solution overview

You can protect data in-transit over individual communications channels using transport layer security (TLS), and at-rest in individual storage silos using volume encryption, object encryption or database table encryption. However, if you have sensitive workloads, you might need additional protection that can follow the data as it moves through the application stack. Fine-grained data protection techniques such as field-level encryption allow for the protection of sensitive data fields in larger application payloads while leaving non-sensitive fields in plaintext. This approach lets an application perform business functions on non-sensitive fields without the overhead of encryption, and allows fine-grained control over what fields can be accessed by what parts of the application.

A best practice for protecting sensitive data is to reduce its exposure in the clear throughout its lifecycle. This means protecting data as early as possible on ingestion and ensuring that only authorized users and applications can access the data only when and as needed. CloudFront, when combined with the flexibility provided by Lambda@Edge, provides an appropriate environment at the edge of the AWS network to protect sensitive data upon ingestion in AWS.

Since the downstream systems don’t have access to sensitive data, data exposure is reduced, which helps to minimize your compliance footprint for auditing purposes.

The number of sensitive data elements that may need field-level encryption depends on your requirements. For example:

  • For healthcare applications, HIPAA regulates 18 personal data elements.
  • In California, the California Consumer Privacy Act (CCPA) regulates at least 11 categories of personal information—each with its own set of data elements.

The idea behind field-level encryption is to protect sensitive data fields individually, while retaining the structure of the application payload. The alternative is full payload encryption, where the entire application payload is encrypted as a binary blob, which makes it unusable until the entirety of it is decrypted. With field-level encryption, the non-sensitive data left in plaintext remains usable for ordinary business functions. When retrofitting data protection in existing applications, this approach can reduce the risk of application malfunction since the data format is maintained.

The following figure shows how PII data fields in a JSON construction that are deemed sensitive by an application can be transformed from plaintext to ciphertext with a field-level encryption mechanism.

Figure 1: Example of field-level encryption

Figure 1: Example of field-level encryption

You can change plaintext to ciphertext as depicted in Figure 1 by using a Lambda@Edge function to perform field-level encryption. I discuss the encryption and decryption processes separately in the following sections.

Field-level encryption process

Let’s discuss the individual steps involved in the encryption process as shown in Figure 2.

Figure 2: Field-level encryption process

Figure 2: Field-level encryption process

Figure 2 shows CloudFront invoking a Lambda@Edge function while processing a client request. CloudFront offers multiple integration points for invoking Lambda@Edge functions. Since you are processing a client request and your encryption behavior is related to requests being forwarded to an origin server, you want your function to run upon the origin request event in CloudFront. The origin request event represents an internal state transition in CloudFront that happens immediately before CloudFront forwards a request to the downstream origin server.

You can associate your Lambda@Edge with CloudFront as described in Adding Triggers by Using the CloudFront Console. A screenshot of the CloudFront console is shown in Figure 3. The selected event type is Origin Request and the Include Body check box is selected so that the request body is conveyed to Lambda@Edge.

Figure 3: Configuration of Lambda@Edge in CloudFront

Figure 3: Configuration of Lambda@Edge in CloudFront

The Lambda@Edge function acts as a programmable hook in the CloudFront request processing flow. You can use the function to replace the incoming request body with a request body with the sensitive data fields encrypted.

The process includes the following steps:

Step 1 – RSA key generation and inclusion in Lambda@Edge

You can generate an RSA customer managed key (CMK) in AWS KMS as described in Creating asymmetric CMKs. This is done at system configuration time.

Note: You can use your existing RSA key pairs or generate new ones externally by using OpenSSL commands, especially if you need to perform RSA decryption and key management independently of AWS KMS. Your choice won’t affect the fundamental encryption design pattern presented here.

The RSA key creation in AWS KMS requires two inputs: key length and type of usage. In this example, I created a 2048-bit key and assigned its use for encryption and decryption. The cryptographic configuration of an RSA CMK created in AWS KMS is shown in Figure 4.

Figure 4: Cryptographic properties of an RSA key managed by AWS KMS

Figure 4: Cryptographic properties of an RSA key managed by AWS KMS

Of the two encryption algorithms shown in Figure 4— RSAES_OAEP_SHA_256 and RSAES_OAEP_SHA_1, this example uses RSAES_OAEP_SHA_256. The combination of a 2048-bit key and the RSAES_OAEP_SHA_256 algorithm lets you encrypt a maximum of 190 bytes of data, which is enough for most PII fields. You can choose a different key length and encryption algorithm depending on your security and performance requirements. How to choose your CMK configuration includes information about RSA key specs for encryption and decryption.

Using AWS KMS for RSA key management versus managing the keys yourself eliminates that complexity and can help you:

  • Enforce IAM and key policies that describe administrative and usage permissions for keys.
  • Manage cross-account access for keys.
  • Monitor and alarm on key operations through Amazon CloudWatch.
  • Audit AWS KMS API invocations through AWS CloudTrail.
  • Record configuration changes to keys and enforce key specification compliance through AWS Config.
  • Generate high-entropy keys in an AWS KMS hardware security module (HSM) as required by NIST.
  • Store RSA private keys securely, without the ability to export.
  • Perform RSA decryption within AWS KMS without exposing private keys to application code.
  • Categorize and report on keys with key tags for cost allocation.
  • Disable keys and schedule their deletion.

You need to extract the RSA public key from AWS KMS so you can include it in the AWS Lambda deployment package. You can do this from the AWS Management Console, through the AWS KMS SDK, or by using the get-public-key command in the AWS Command Line Interface (AWS CLI). Figure 5 shows Copy and Download options for a public key in the Public key tab of the AWS KMS console.

Figure 5: RSA public key available for copy or download in the console

Figure 5: RSA public key available for copy or download in the console

Note: As we will see in the sample code in step 3, we embed the public key in the Lambda@Edge deployment package. This is a permissible practice because public keys in asymmetric cryptography systems aren’t a secret and can be freely distributed to entities that need to perform encryption. Alternatively, you can use Lambda@Edge to query AWS KMS for the public key at runtime. However, this introduces latency, increases the load against your KMS account quota, and increases your AWS costs. General patterns for using external data in Lambda@Edge are described in Leveraging external data in Lambda@Edge.

Step 2 – HTTP API request handling by CloudFront

CloudFront receives an HTTP(S) request from a client. CloudFront then invokes Lambda@Edge during origin-request processing and includes the HTTP request body in the invocation.

Step 3 – Lambda@Edge processing

The Lambda@Edge function processes the HTTP request body. The function extracts sensitive data fields and performs RSA encryption over their values.

The following code is sample source code for the Lambda@Edge function implemented in Python 3.7:

import Crypto
import base64
import json
from Crypto.Cipher import PKCS1_OAEP
from Crypto.PublicKey import RSA

# PEM-formatted RSA public key copied over from AWS KMS or your own public key.
RSA_PUBLIC_KEY = "-----BEGIN PUBLIC KEY-----<your key>-----END PUBLIC KEY-----"

# Example sensitive data field names in a JSON object. 
PII_SENSITIVE_FIELD_NAMES = ["fname", "lname", "email", "ssn", "dob", "phone"]


def lambda_handler(event, context):
    # Extract HTTP request and its body as per documentation:
    # https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-event-structure.html
    http_request = event['Records'][0]['cf']['request']
    body = http_request['body']
    org_body = base64.b64decode(body['data'])
    mod_body = protect_sensitive_fields_json(org_body)
    body['action'] = 'replace'
    body['encoding'] = 'text'
    body['data'] = mod_body
    return http_request

def protect_sensitive_fields_json(body):
    # Encrypts sensitive fields in sample JSON payload shown earlier in this post.
    # [{"fname": "Alejandro", "lname": "Rosalez", … }]
    person_list = json.loads(body.decode("utf-8"))
    for person_data in person_list:
        for field_name in PII_SENSITIVE_FIELD_NAMES:
            if field_name not in person_data:
            plaintext = person_data[field_name]
            ciphertext = RSA_CIPHER_OBJ.encrypt(bytes(plaintext, 'utf-8'))
            ciphertext_b64 = base64.b64encode(ciphertext).decode()
            # Optionally, add unique prefix/suffix patterns to ciphertext
            person_data[field_name] = CIPHERTEXT_PREFIX + ciphertext_b64 + CIPHERTEXT_SUFFIX 
    return json.dumps(person_list)

The event structure passed into the Lambda@Edge function is described in Lambda@Edge Event Structure. Following the event structure, you can extract the HTTP request body. In this example, the assumption is that the HTTP payload carries a JSON document based on a particular schema defined as part of the API contract. The input JSON document is parsed by the function, converting it into a Python dictionary. The Python native dictionary operators are then used to extract the sensitive field values.

Note: If you don’t know your API payload structure ahead of time or you’re dealing with unstructured payloads, you can use techniques such as regular expression pattern searches and checksums to look for patterns of sensitive data and target them accordingly. For example, credit card primary account numbers include a Luhn checksum that can be programmatically detected. Additionally, services such as Amazon Comprehend and Amazon Macie can be leveraged for detecting sensitive data such as PII in application payloads.

While iterating over the sensitive fields, individual field values are encrypted using the standard RSA encryption implementation available in the Python Cryptography Toolkit (PyCrypto). The PyCrypto module is included within the Lambda@Edge zip archive as described in Lambda@Edge deployment package.

The example uses the standard optimal asymmetric encryption padding (OAEP) and SHA-256 encryption algorithm properties. These properties are supported by AWS KMS and will allow RSA ciphertext produced here to be decrypted by AWS KMS later.

Note: You may have noticed in the code above that we’re bracketing the ciphertexts with predefined prefix and suffix strings:

person_data[field_name] = CIPHERTEXT_PREFIX + ciphertext_b64 + CIPHERTEXT_SUFFIX

This is an optional measure and is being implemented to simplify the decryption process.

The prefix and suffix strings help demarcate ciphertext embedded in unstructured data in downstream processing and also act as embedded metadata. Unique prefix and suffix strings allow you to extract ciphertext through string or regular expression (regex) searches during the decryption process without having to know the data body format or schema, or the field names that were encrypted.

Distinct strings can also serve as indirect identifiers of RSA key pair identifiers. This can enable key rotation and allow separate keys to be used for separate fields depending on the data security requirements for individual fields.

You can ensure that the prefix and suffix strings can’t collide with the ciphertext by bracketing them with characters that don’t appear in cyphertext. For example, a hash (#) character cannot be part of a base64 encoded ciphertext string.

Deploying a Lambda function as a Lambda@Edge function requires specific IAM permissions and an IAM execution role. Follow the Lambda@Edge deployment instructions in Setting IAM Permissions and Roles for Lambda@Edge.

Step 4 – Lambda@Edge response

The Lambda@Edge function returns the modified HTTP body back to CloudFront and instructs it to replace the original HTTP body with the modified one by setting the following flag:

http_request['body']['action'] = 'replace'

Step 5 – Forward the request to the origin server

CloudFront forwards the modified request body provided by Lambda@Edge to the origin server. In this example, the origin server writes the data body to persistent storage for later processing.

Field-level decryption process

An application that’s authorized to access sensitive data for a business function can decrypt that data. An example decryption process is shown in Figure 6. The figure shows a Lambda function as an example compute environment for invoking AWS KMS for decryption. This functionality isn’t dependent on Lambda and can be performed in any compute environment that has access to AWS KMS.

Figure 6: Field-level decryption process

Figure 6: Field-level decryption process

The steps of the process shown in Figure 6 are described below.

Step 1 – Application retrieves the field-level encrypted data

The example application retrieves the field-level encrypted data from persistent storage that had been previously written during the data ingestion process.

Step 2 – Application invokes the decryption Lambda function

The application invokes a Lambda function responsible for performing field-level decryption, sending the retrieved data to Lambda.

Step 3 – Lambda calls the AWS KMS decryption API

The Lambda function uses AWS KMS for RSA decryption. The example calls the KMS decryption API that inputs ciphertext and returns plaintext. The actual decryption happens in KMS; the RSA private key is never exposed to the application, which is a highly desirable characteristic for building secure applications.

Note: If you choose to use an external key pair, then you can securely store the RSA private key in AWS services like AWS Systems Manager Parameter Store or AWS Secrets Manager and control access to the key through IAM and resource policies. You can fetch the key from relevant vault using the vault’s API, then decrypt using the standard RSA implementation available in your programming language. For example, the cryptography toolkit in Python or javax.crypto in Java.

The Lambda function Python code for decryption is shown below.

import base64
import boto3
import re

kms_client = boto3.client('kms')

# This lambda function extracts event body, searches for and decrypts ciphertext 
# fields surrounded by provided prefix and suffix strings in arbitrary text bodies 
# and substitutes plaintext fields in-place.  
def lambda_handler(event, context):    
    org_data = event["body"]
    mod_data = unprotect_fields(org_data, CIPHERTEXT_PREFIX, CIPHERTEXT_SUFFIX)
    return mod_data

# Helper function that performs non-greedy regex search for ciphertext strings on
# input data and performs RSA decryption of them using AWS KMS 
def unprotect_fields(org_data, prefix, suffix):
    regex_pattern = prefix + "(.*?)" + suffix
    mod_data_parts = []
    cursor = 0

    # Search ciphertexts iteratively using python regular expression module
    for match in re.finditer(regex_pattern, org_data):
        mod_data_parts.append(org_data[cursor: match.start()])
            # Ciphertext was stored as Base64 encoded in our example. Decode it.
            ciphertext = base64.b64decode(match.group(1))

            # Decrypt ciphertext using AWS KMS  
            decrypt_rsp = kms_client.decrypt(
            decrypted_val = decrypt_rsp["Plaintext"].decode("utf-8")
        except Exception as e:
            print ("Exception: " + str(e))
            return None
        cursor = match.end()

    return "".join(mod_data_parts)

The function performs a regular expression search in the input data body looking for ciphertext strings bracketed in predefined prefix and suffix strings that were added during encryption.

While iterating over ciphertext strings one-by-one, the function calls the AWS KMS decrypt() API. The example function uses the same RSA encryption algorithm properties—OAEP and SHA-256—and the Key ID of the public key that was used during encryption in Lambda@Edge.

Note that the Key ID itself is not a secret. Any application can be configured with it, but that doesn’t mean any application will be able to perform decryption. The security control here is that the AWS KMS key policy must allow the caller to use the Key ID to perform the decryption. An additional security control is provided by Lambda execution role that should allow calling the KMS decrypt() API.

Step 4 – AWS KMS decrypts ciphertext and returns plaintext

To ensure that only authorized users can perform decrypt operation, the KMS is configured as described in Using key policies in AWS KMS. In addition, the Lambda IAM execution role is configured as described in AWS Lambda execution role to allow it to access KMS. If both the key policy and IAM policy conditions are met, KMS returns the decrypted plaintext. Lambda substitutes the plaintext in place of ciphertext in the encapsulating data body.

Steps three and four are repeated for each ciphertext string.

Step 5 – Lambda returns decrypted data body

Once all the ciphertext has been converted to plaintext and substituted in the larger data body, the Lambda function returns the modified data body to the client application.


In this post, I demonstrated how you can implement field-level encryption integrated with AWS KMS to help protect sensitive data workloads for their entire lifecycle in AWS. Since your Lambda@Edge is designed to protect data at the network edge, data remains protected throughout the application execution stack. In addition to improving your data security posture, this protection can help you comply with data privacy regulations applicable to your organization.

Since you author your own Lambda@Edge function to perform standard RSA encryption, you have flexibility in terms of payload formats and the number of fields that you consider to be sensitive. The integration with AWS KMS for RSA key management and decryption provides significant simplicity, higher key security, and rich integration with other AWS security services enabling an overall strong security solution.

By using encrypted fields with identifiers as described in this post, you can create fine-grained controls for data accessibility to meet the security principle of least privilege. Instead of granting either complete access or no access to data fields, you can ensure least privileges where a given part of an application can only access the fields that it needs, when it needs to, all the way down to controlling access field by field. Field by field access can be enabled by using different keys for different fields and controlling their respective policies.

In addition to protecting sensitive data workloads to meet regulatory and security best practices, this solution can be used to build de-identified data lakes in AWS. Sensitive data fields remain protected throughout their lifecycle, while non-sensitive data fields remain in the clear. This approach can allow analytics or other business functions to operate on data without exposing sensitive data.

If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.


Raj Jain

Raj is a Senior Cloud Architect at AWS. He is passionate about helping customers build well-architected applications in AWS. Raj is a published author in Bell Labs Technical Journal, has authored 3 IETF standards, and holds 12 patents in internet telephony and applied cryptography. In his spare time, Raj enjoys outdoors, cooking, reading, and travel.

Round 2 post-quantum TLS is now supported in AWS KMS

Post Syndicated from Alex Weibel original https://aws.amazon.com/blogs/security/round-2-post-quantum-tls-is-now-supported-in-aws-kms/

AWS Key Management Service (AWS KMS) now supports three new hybrid post-quantum key exchange algorithms for the Transport Layer Security (TLS) 1.2 encryption protocol that’s used when connecting to AWS KMS API endpoints. These new hybrid post-quantum algorithms combine the proven security of a classical key exchange with the potential quantum-safe properties of new post-quantum key exchanges undergoing evaluation for standardization. The fastest of these algorithms adds approximately 0.3 milliseconds of overheard compared to a classical TLS handshake. The new post-quantum key exchange algorithms added are Round 2 versions of Kyber, Bit Flipping Key Encapsulation (BIKE), and Supersingular Isogeny Key Encapsulation (SIKE). Each organization has submitted their algorithms to the National Institute of Standards and Technology (NIST) as part of NIST’s post-quantum cryptography standardization process. This process spans several rounds of evaluation over multiple years, and is likely to continue beyond 2021.

In our previous hybrid post-quantum TLS blog post, we announced that AWS KMS had launched hybrid post-quantum TLS 1.2 with Round 1 versions of BIKE and SIKE. The Round 1 post-quantum algorithms are still supported by AWS KMS, but at a lower priority than the Round 2 algorithms. You can choose to upgrade your client to enable negotiation of Round 2 algorithms.

Why post-quantum TLS is important

A large-scale quantum computer would be able to break the current public-key cryptography that’s used for key exchange in classical TLS connections. While a large-scale quantum computer isn’t available today, it’s still important to think about and plan for your long-term security needs. TLS traffic using classical algorithms recorded today could be decrypted by a large-scale quantum computer in the future. If you’re developing applications that rely on the long-term confidentiality of data passed over a TLS connection, you should consider a plan to migrate to post-quantum cryptography before the lifespan of the sensitivity of your data would be susceptible to an unauthorized user with a large-scale quantum computer. As an example, this means that if you believe that a large-scale quantum computer is 25 years away, and your data must be secure for 20 years, you should migrate to post-quantum schemes within the next 5 years. AWS is working to prepare for this future, and we want you to be prepared too.

We’re offering this feature now instead of waiting for standardization efforts to be complete so you have a way to measure the potential performance impact to your applications. Offering this feature now also gives you the protection afforded by the proposed post-quantum schemes today. While we believe that the use of this feature raises the already high security bar for connecting to AWS KMS endpoints, these new cipher suites will impact bandwidth utilization and latency. However, using these new algorithms could also create connection failures for intermediate systems that proxy TLS connections. We’d like to get feedback from you on the effectiveness of our implementation or any issues found so we can improve it over time.

Hybrid post-quantum TLS 1.2

Hybrid post-quantum TLS is a feature that provides the security protections of both the classical and post-quantum key exchange algorithms in a single TLS handshake. Figure 1 shows the differences in the connection secret derivation process between classical and hybrid post-quantum TLS 1.2. Hybrid post-quantum TLS 1.2 has three major differences from classical TLS 1.2:

  • The negotiated post-quantum key is appended to the ECDHE key before being used as the hash-based message authentication code (HMAC) key.
  • The text hybrid in its ASCII representation is prepended to the beginning of the HMAC message.
  • The entire client key exchange message from the TLS handshake is appended to the end of the HMAC message.
Figure 1: Differences in the connection secret derivation process between classical and hybrid post-quantum TLS 1.2

Figure 1: Differences in the connection secret derivation process between classical and hybrid post-quantum TLS 1.2

Some background on post-quantum TLS

Today, all requests to AWS KMS use TLS with key exchange algorithms that provide perfect forward secrecy and use one of the following classical schemes:

While existing FFDHE and ECDHE schemes use perfect forward secrecy to protect against the compromise of the server’s long-term secret key, these schemes don’t protect against large-scale quantum computers. In the future, a sufficiently capable large-scale quantum computer could run Shor’s Algorithm to recover the TLS session key of a recorded classical session, and thereby gain access to the data inside. Using a post-quantum key exchange algorithm during the TLS handshake protects against attacks from a large-scale quantum computer.

The possibility of large-scale quantum computing has spurred the development of new quantum-resistant cryptographic algorithms. NIST has started the process of standardizing post-quantum key encapsulation mechanisms (KEMs). A KEM is a type of key exchange that’s used to establish a shared symmetric key. AWS has chosen three NIST KEM submissions to adopt in our post-quantum efforts:

Hybrid mode ensures that the negotiated key is as strong as the weakest key agreement scheme. If one of the schemes is broken, the communications remain confidential. The Internet Engineering Task Force (IETF) Hybrid Post-Quantum Key Encapsulation Methods for Transport Layer Security 1.2 draft describes how to combine post-quantum KEMs with ECDHE to create new cipher suites for TLS 1.2.

These cipher suites use a hybrid key exchange that performs two independent key exchanges during the TLS handshake. The key exchange then cryptographically combines the keys from each into a single TLS session key. This strategy combines the proven security of a classical key exchange with the potential quantum-safe properties of new post-quantum key exchanges being analyzed by NIST.

The effect of hybrid post-quantum TLS on performance

Post-quantum cipher suites have a different performance profile and bandwidth usage from traditional cipher suites. AWS has measured bandwidth and latency across 2,000 TLS handshakes between an Amazon Elastic Compute Cloud (Amazon EC2) C5n.4xlarge client and the public AWS KMS endpoint, which were both in the us-west-2 Region. Your own performance characteristics might differ, and will depend on your environment, including your:

  • Hardware–CPU speed and number of cores.
  • Existing workloads–how often you call AWS KMS and what other work your application performs.
  • Network–location and capacity.

The following graphs and table show latency measurements performed by AWS for all newly supported Round 2 post-quantum algorithms, in addition to the classical ECDHE key exchange algorithm currently used by most customers.

Figure 2 shows the latency differences of all hybrid post-quantum algorithms compared with classical ECDHE alone, and shows that compared to ECDHE alone, SIKE adds approximately 101 milliseconds of overhead, BIKE adds approximately 9.5 milliseconds of overhead, and Kyber adds approximately 0.3 milliseconds of overhead.

Figure 2: TLS handshake latency at varying percentiles for four key exchange algorithms

Figure 2: TLS handshake latency at varying percentiles for four key exchange algorithms

Figure 3 shows the latency differences between ECDHE with Kyber, and ECDHE alone. The addition of Kyber adds approximately 0.3 milliseconds of overhead.

Figure 3: TLS handshake latency at varying percentiles, with only top two performing key exchange algorithms

Figure 3: TLS handshake latency at varying percentiles, with only top two performing key exchange algorithms

The following table shows the total amount of data (in bytes) needed to complete the TLS handshake for each cipher suite, the average latency, and latency at varying percentiles. All measurements were gathered from 2,000 TLS handshakes. The time was measured on the client from the start of the handshake until the handshake was completed, and includes all network transfer time. All connections used RSA authentication with a 2048-bit key, and ECDHE used the secp256r1 curve. All hybrid post-quantum tests used the NIST Round 2 versions. The Kyber test used the Kyber-512 parameter, the BIKE test used the BIKE-1 Level 1 parameter, and the SIKE test used the SIKEp434 parameter.

Item Bandwidth
ECDHE (classic) 3,574 2,000 3.08 2.07 3.02 3.95 4.71
ECDHE + Kyber R2 5,898 2,000 3.36 2.38 3.17 4.28 5.35
ECDHE + BIKE R2 12,456 2,000 14.91 11.59 14.16 18.27 23.58
ECDHE + SIKE R2 4,628 2,000 112.40 103.22 108.87 126.80 146.56

By default, the AWS SDK client performs a TLS handshake once to set up a new TLS connection, and then reuses that TLS connection for multiple requests. This means that the increased cost of a hybrid post-quantum TLS handshake is amortized over multiple requests sent over the TLS connection. You should take the amortization into account when evaluating the overall additional cost of using post-quantum algorithms; otherwise performance data could be skewed.

AWS KMS has chosen Kyber Round 2 to be KMS’s highest prioritized post-quantum algorithm, with BIKE Round 2, and SIKE Round 2 next in priority order for post-quantum algorithms. This is because Kyber’s performance is closest to the classical ECDHE performance that most AWS KMS customers are using today and are accustomed to.

How to use hybrid post-quantum cipher suites

To use the post-quantum cipher suites with AWS KMS, you need the preview release of the AWS Common Runtime (CRT) HTTP client for the AWS SDK for Java 2.x. Also, you will need to configure the AWS CRT HTTP client to use the s2n post-quantum hybrid cipher suites. Post-quantum TLS for AWS KMS is available in all AWS Regions except for AWS GovCloud (US-East), AWS GovCloud (US-West), AWS China (Beijing) Region operated by Beijing Sinnet Technology Co. Ltd (“Sinnet”), and AWS China (Ningxia) Region operated by Ningxia Western Cloud Data Technology Co. Ltd. (“NWCD”). Since NIST has not yet standardized post-quantum cryptography, connections that require Federal Information Processing Standards (FIPS) compliance cannot use the hybrid key exchange. For example, kms.<region>.amazonaws.com supports the use of post-quantum cipher suites, while kms-fips.<region>.amazonaws.com does not.

  1. If you’re using the AWS SDK for Java 2.x, you must add the preview release of the AWS Common Runtime client to your Maven dependencies.

  2. You then must configure the new SDK and cipher suite in the existing initialization code of your application:
        throw new RuntimeException("Post Quantum Ciphers not supported on this Platform");
    SdkAsyncHttpClient awsCrtHttpClient = AwsCrtAsyncHttpClient.builder()
    KmsAsyncClient kms = KmsAsyncClient.builder()
    ListKeysResponse response = kms.listKeys().get();

Now, all connections made to AWS KMS in supported Regions will use the new hybrid post-quantum cipher suites! To see a complete example of everything set up, check out the example application here.

Things to try

Here are some ideas about how to use this post-quantum-enabled client:

  • Run load tests and benchmarks. These new cipher suites perform differently than traditional key exchange algorithms. You might need to adjust your connection timeouts to allow for the longer handshake times or, if you’re running inside an AWS Lambda function, extend the execution timeout setting.
  • Try connecting from different locations. Depending on the network path your request takes, you might discover that intermediate hosts, proxies, or firewalls with deep packet inspection (DPI) block the request. This could be due to the new cipher suites in the ClientHello or the larger key exchange messages. If this is the case, you might need to work with your security team or IT administrators to update the relevant configuration to unblock the new TLS cipher suites. We’d like to hear from you about how your infrastructure interacts with this new variant of TLS traffic. If you have questions or feedback, please start a new thread on the AWS KMS discussion forum.


In this blog post, I announced support for Round 2 hybrid post-quantum algorithms in AWS KMS, and showed you how to begin experimenting with hybrid post-quantum key exchange algorithms for TLS when connecting to AWS KMS endpoints.

More info

If you’d like to learn more about post-quantum cryptography check out:

If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.


Alex Weibel

Alex is a Senior Software Engineer on the AWS Crypto Algorithms team. He’s one of the maintainers for Amazon’s TLS Library s2n. Previously, Alex worked on TLS termination and request proxying for S3 and the Elastic Load Balancing Service developing new features for customers. Alex holds a Bachelor of Science degree in Computer Science from the University of Texas at Austin.

Combining encryption and signing with AWS asymmetric keys

Post Syndicated from J.D. Bean original https://aws.amazon.com/blogs/security/combining-encryption-and-signing-with-aws-asymmetric-keys/

In this post, I discuss how to use AWS Key Management Service (KMS) to combine asymmetric digital signature and asymmetric encryption of the same data.

The addition of support for asymmetric keys in AWS KMS has exciting use cases for customers. The ability to create, manage, and use public and private key pairs with KMS enables you to perform digital signing operations using RSA and Elliptic Curve Cryptography (ECC) keys. AWS KMS asymmetric keys can also be used to perform digital encryption operations using RSA keys. You can use these features together to digitally sign and encrypt the same data.

Another notable property of AWS KMS asymmetric keys is that they enable disconnected use cases. For example AWS KMS asymmetric keys can be used to cryptographically verify a digital signature client-side without the need for a network connection. AWS KMS asymmetric keys also enable scenarios where customers can use KMS to securely manage decryption of data that has been encrypted by a partner’s system that does not integrate with AWS APIs or have access to AWS account credentials. For the sake of simplicity, however, the example that I discuss in this post describes a connected use case where all cryptographic actions are performed server-side in AWS KMS using AWS credentials. The use of AWS KMS asymmetric keys throughout this post allows the overall approach to be adapted to disconnected and/or non-AWS-integrated use cases.


This post contains three basic steps.

  1. Create and configure AWS asymmetric customer master keys (CMK), AWS Identity and Access Management (IAM) roles, and key policies.
  2. Use your asymmetric CMKs to encrypt and sign a sample message in the role of a sender.
  3. Use AWS KMS to decrypt and verify the message signature of the sample message archive you generated in the previous procedure using your asymmetric CMKs in the role of a receiver.


The commands I use in this tutorial were tested using AWS Command Line Interface (AWS CLI) version 2.50 on Amazon Linux 2. In order to run these commands in your in your own local environment ensure that you have first installed and updated the AWS CLI.

I assume you have at least one administrator identity available to you that has broad rights for creating roles, assuming roles, as well as creating, managing and using KMS keys. This can be a federated identity (for example, from your corporate identity provider or from a social identity), or it can be an AWS IAM user. Where no AWS identity is mentioned, I assume that you will be accessing the AWS Management Console or the AWS CLI using this administrator identity.

For simplicity, I create the KMS keys in the same region as each other. You must specify an AWS Region when using the AWS CLI, either explicitly or by setting a default Region. Before beginning, you should select an AWS Region to work in such as US East (N. Virginia). If you have not configured the AWS CLI in your environment please review the Configuration basics section of the AWS Command Line Interface User Guide for instructions. You may revert this configuration once you have finished if you do not wish to continue using a default Region with your AWS CLI. Take note of your selected region. When working in the AWS Console, if you do not see resources, such as AWS KMS keys, that you expect you may want to confirm that you are viewing resources in your chosen Region. For more information on selecting your Region in the AWS Console see Choosing a Region in AWS Management Console Getting Started Guide.

Create and configure resources

In the first phase of this tutorial you create and configure two asymmetric AWS KMS CMKs, two AWS IAM roles, and configure the key policies for both of your KMS CMKs to grant permissions to the roles. Shown in the following figure.

Figure 1: Create keys, roles, and key policies

Figure 1: Create keys, roles, and key policies

Create asymmetric signing and encryption key pairs

In the first step, you create two asymmetric master keys (CMK). One is configured for signing and verifying digital signatures while the other is configured for encrypting and decrypting data.

Note: The CMKs configured for this post are examples. RSA and Elliptic curve CMKs key specs can differ in a variety of dimensions. The RSA or elliptic curve key spec that you choose might be determined by your security standards or the requirements of your task. Different CMK key specs are priced differently and are subject to different request quotas because they each have different performance profiles. In general, use RSA or ECC keys with the highest security level that is practical and affordable for your task. For more information on CMK configuration options, please review the How to choose your CMK configuration section of the KMS Developer Guide.

To create a CMK for encryption and decryption

  1. Use the KMS CreateKey API. Pass RSA_4096 for the CustomerMasterKeySpec parameter and ENCRYPT_DECRYPT for the KeyUsage parameter in the AWS CLI example command below in order to generate a RSA 4096 key pair for signature creation and verification using AWS KMS.
    aws kms create-key --customer-master-key-spec RSA_4096 \
        --key-usage ENCRYPT_DECRYPT \
        --description "Sample Digital Encryption Key Pair"

    Note: If successful, this command returns a KeyMetadata object. Take note of the KeyID value in this object.

  2. As a best practice, assign an alias for your key. Use the following command to assign an alias of sample-encrypt-decrypt-key to your newly created CMK (replace the target-key-id value of 1234abcd-12ab-34cd-56ef-1234567890ab with your KeyID). Mapping a human-readable alias to the KeyID will make it easier to identify, use, and manage.
    aws kms create-alias \
        --alias-name alias/sample-encrypt-decrypt-key \
        --target-key-id 1234abcd-12ab-34cd-56ef-1234567890ab

To create a CMK for signature and verification

  1. Use the KMS CreateKey API. Pass ECC_NIST_P521 for the CustomerMasterKeySpec parameter and SIGN_VERIFY for the KeyUsage parameter in the AWS CLI example command below in order to generate an elliptic curve (ECC) key pair for signature creation and verification using AWS KMS.
    aws kms create-key --customer-master-key-spec \
        ECC_NIST_P521  \
        --key-usage SIGN_VERIFY \
        --description "Sample Digital Signature Key Pair"

    Note: If successful, this command returns a KeyMetadata object. Take note of the KeyID value.

  2. Use the following command to assign an alias of sample-sign-verify-key to your newly created CMK (replace the target-key-id value of 1234abcd-12ab-34cd-56ef-1234567890ab with your KeyID).
    aws kms create-alias \
        --alias-name alias/sample-sign-verify-key \
        --target-key-id 1234abcd-12ab-34cd-56ef-1234567890ab

Create sender and receiver roles

For the next step of this tutorial, you create two AWS principals. Use the steps that follow to create two roles—a sender principal and a receiver principal. Later, you will grant permissions to perform private key operations (sign and decrypt) and public key operations (verify and encrypt) to these roles.

To create and configure the roles

  1. Navigate to the AWS Identity and Access Management (IAM) Create role console dialogue that allows entities in a specified account to assume the role. Enter your Account ID and choose Next, as shown in the following figure.

    Note: If you don’t know you AWS account ID, please read Finding you AWS account ID in the AWS IAM User Guide for guidance on how to obtain this information.

    Figure 2: Enter your account ID to begin creating a role in AWS IAM

    Figure 2: Enter your account ID to begin creating a role in AWS IAM

  2. Select Next through the next two screens.

    Note: By clicking next through these dialogues you do not attach an IAM permissions policy or a tag to this new role.

  3. On the final screen, enter a Role name of SenderRole and a Role description of your choice, as shown in the following figure.
    Figure 3: Create the sender role

    Figure 3: Create the sender role

  4. Choose Create role to finish creating the sender role.
  5. To create the receiver role, repeat the preceding role creation process. However, in step 3, substitute the name ReceiverRole for SenderRole.

Configure key policy permissions

Best practice is to adhere to the principle of least privilege and provide each AWS principal with the minimal permissions necessary to perform its tasks. The sender and receiver roles that you created in the previous step currently have no permissions in your account. For this scenario, the receiver principal must be granted permission to verify digital signatures and decrypt data in AWS KMS using your asymmetric CMKs and the sender principal must be granted permission to create digital signatures and encrypt data in KMS using your asymmetric CMKs.

To provide access control permissions for AWS KMS actions to your AWS principals, attach a key policy to each of your CMKs.

Modify the CMK key policy

For the sample-encrypt-decrypt-key CMK, grant the IAM role for the sender principal (SenderRole) kms:Encrypt permissions and the IAM role for the receiver principal (ReceiverRole) kms:Decrypt permissions in the CMK key policy.

To modify the CMK key policy (console)

  1. Navigate to the AWS KMS page in the AWS Console and select customer-managed keys.
  2. Select your sample-encrypt-decrypt-key CMK.
  3. In the key policy section, choose edit.
  4. To allow your receiver principal to use the CMK to decrypt data encrypted under that CMK, append the following statement to the key policy (replace the account ID value of 111122223333 with your own).
        "Sid": "Allow use of the CMK for decryption",
        "Effect": "Allow",
        "Principal": {"AWS":"arn:aws:iam::111122223333:role/ReceiverRole"},
        "Action": "kms:Decrypt",
        "Resource": "*"

  5. To allow your sender principal to use the CMK to encrypt data, append the following statement to the key policy (replace the account ID value of 111122223333 with your own):
        "Sid": "Allow use of the CMK for encryption",
        "Effect": "Allow",
        "Principal": {"AWS":"arn:aws:iam::111122223333:role/SenderRole"},
        "Action": "kms:Encrypt",
        "Resource": "*"

  6. Choose Save changes.

Note: The kms:Encrypt permission is sufficient to permit the sender principal to encrypt small amounts of arbitrary data using your CMK directly.

Grant sign and verify permissions to the CMK key policy

For the sample-sign-verify-key CMK, grant the IAM role for the sender principal (SenderRole) kms:Sign permissions in the CMK key policy and the IAM role for the receiver principal (ReceiverRole) kms:Verify permissions in the CMK key policy.

To grant sign and verify permissions

  1. Using the same process as above, navigate to the key policy edit dialog for the sample-sign-verify-key CMK in the AWS console.
  2. To allow your sender principal to use the CMK to create digital signatures, append the following statement to the key policy (replace the account ID value of 111122223333 with your own).
        "Sid": "Allow use of the CMK for digital signing",
        "Effect": "Allow",
        "Principal": {"AWS":"arn:aws:iam::111122223333:role/SenderRole"},
        "Action": "kms:Sign",
        "Resource": "*"

  3. To allow your receiver principal to use the CMK to verify signatures created by that CMK, append the following statement to the key policy (replace the account ID value of 111122223333 with your own):
        "Sid": "Allow use of the CMK for digital signature verification",
        "Effect": "Allow",
        "Principal": {"AWS":"arn:aws:iam::111122223333:role/ReceiverRole"},
        "Action": "kms:Verify",
        "Resource": "*"

  4. Choose Save changes.

Key permissions summary

When these key policy edits have been completed the sender principal:

  • Will have permissions to encrypt data using the sample-encrypt-decrypt-key CMK and generate digital signatures using the sample-sign-verify-key CMK.
  • Will not have permissions to decrypt or to verify signatures using the CMKs.

The receiver principal:

  • Will have permissions to decrypt data which has been encrypted using the sample-encrypt-decrypt-key CMK and to verify signatures created using the sample-sign-verify-key CMK.
  • Will not have permissions to encrypt or to generate signatures using the CMKs.
Figure 4: Summary of key policy permissions

Figure 4: Summary of key policy permissions

Signing and encrypting a sample message

So far, you’ve created two asymmetric CMKs, created a set of sender and receiver roles, and configured permissions for those roles in each of your CMK key policies. In the second phase of this tutorial, you assume the role of sender and use your asymmetric signature and verification CMK to sign a sample message. You then bundle the sample message and its corresponding digital signature together into an archive and use your encryption and decryption asymmetric CMK to encrypt the archive.

Figure 5: Creating a message signature and encrypting the message along with its signature

Figure 5: Creating a message signature and encrypting the message along with its signature

Note: The order of operations in this process is that the message is first signed and then the signature and the message are encrypted together. This order is intentional. When a message is signed and then encrypted, neither the contents nor the identity of the sender will be available to unauthorized 3rd parties. If the order of operations were reversed, however, and a message was first encrypted and then signed it could leak information about the sender’s identity to unauthorized 3rd parties. Moreover, when a message is encrypted and then signed, an unauthorized 3rd party with access to the files could discard the authentic signature created by the sender and replace it with a valid signature created by their own key. This creates the potential for a 3rd party to deceptively create the appearance that they are the legitimate sender of the message and exploit that misperception further.

Assume the sender role

Start by assuming the sender role. In order to successfully assume a role you must authenticate as an IAM principal which has permission to perform sts:AssumeRole. If the principal you are authenticated as lacks this permission you will not able to assume the sender role.

To assume the sender role

  1. Run the following command, but be sure to replace the account ID value of 111122223333 with your account ID:
    aws sts assume-role \
        --role-arn arn:aws:iam::111122223333::role/SenderRole \
        --role-session-name AWSCLI-Session

  2. The return value for this command provides an access key ID, secret key, and session token. Substitute them into their respective places in the following commands and execute:
    export AWS_ACCESS_KEY_ID=ExampleAccessKeyID1
    export AWS_SECRET_ACCESS_KEY=ExampleSecretKey1
    export AWS_SESSION_TOKEN=ExampleSessionToken1

  3. Confirm that you’ve successfully assumed the sender role by issuing:
    aws sts get-caller-identity

    Note: If the output of this command contains the text assumed-role/SenderRole, then you’ve successfully assumed the sender role.

Create a message

Now, create a sample message file called message.json.

To create a message

Run the following command to create a message with the following content:

echo "
    "message": "The Magic Words are Squeamish Ossifrage", 
    "sender": "Sender Principal" 
" > ./message.json 

Create a digital signature

Creating and verifying a digital signature for the message provides confidence that the message contents haven’t been altered after being sent. This characteristic is known as integrity. Furthermore, when access to a signing key is scoped to a particular principal, creating and verifying a digital signature for the message provides confidence in the sender’s identity. This characteristic is known as authenticity. Finally, a high degree of confidence in both the integrity and authenticity of a message limits the plausible ability of a sender to fraudulently deny having signed a message. This characteristic is known a non-repudiation.

To create a digital signature

Run the following command to create a digital signature for message.json:

aws kms sign \
    --key-id alias/sample-sign-verify-key \
    --message-type RAW \
    --signing-algorithm ECDSA_SHA_512 \
    --message fileb://message.json \
    --output text \
    --query Signature | base64 --decode > message.sig

This generates an independent digital signature file, message.sig, for message.json. Any modification to the contents of message.json, such as changing the sender or message fields, will now cause signature validation of message.sig to fail for message.json.

Encrypt the message and signature

Even with the benefits of a digital signature, the message could still be viewed by any party with access to the file. In order to provide confidence that the message contents aren’t exposed to unauthorized parties, you can encrypt the message. This characteristic is known as confidentiality. In order to retain the benefits of your digital signature you can encrypt the message and corresponding signature together in a single package.

To encrypt the message and signature

  1. Combine your message and signature into an archive. For example, with the GNU Tar utility you can issue the following:
    tar -czvf message.tar.gz message.sig message.json

    This will create a new archive file named message.tar.gz containing both your message and message signature.

  2. Encrypt the archive using AWS KMS. To do so, issue the following command:
    aws kms encrypt \
        --key-id alias/sample-encrypt-decrypt-key \
        --encryption-algorithm RSAES_OAEP_SHA_256 \
        --plaintext fileb://message.tar.gz \
        --output text \
        --query CiphertextBlob | base64 --decode > message.enc

    This will output a message.enc file containing an encrypted copy of the message.tar.gz archive.

Decrypting and verifying a sample message

Now that you’ve created, signed, and encrypted a message, let’s change gears and see what working with this message.enc file is like from the perspective of a receiving party. In the final phase of this tutorial you assume the role of receiver and use your asymmetric CMKs to decrypt the encrypted message archive and verify the digital signature that you created. Finally, you will view your message. The process is shown in the following figure.

Figure 6: Decrypting a message archive and verifying the message signature

Figure 6: Decrypting a message archive and verifying the message signature

Assume the receiver role

Assume the receiver role so that you can simulate receiving a signed and encrypted message. As before, in order to assume the receiver role you must authenticate as an IAM principal which has permission to perform sts:AssumeRole. If the principal you are authenticated as lacks this permission you will not able to assume the receiver role.

To assume the receiver role

  1. Copy the message.enc file to a new directory to create a clean working space and navigate there in a terminal session.
  2. Assume your receiver role. To do so, execute the following command, replacing the account ID value of 111122223333 with your own:
    aws sts assume-role \
    	--role-arn arn:aws:iam::111122223333::role/ReceiverRole \
    	--role-session-name AWSCLI-Session

  3. The return value for this command provides an access key ID, secret key, and session token. Substitute them into their respective places in the following commands and execute:
    export AWS_ACCESS_KEY_ID=ExampleAccessKeyID1
    export AWS_SECRET_ACCESS_KEY=ExampleSecretKey1
    export AWS_SESSION_TOKEN=ExampleSessionToken1

  4. Confirm that you have successfully assumed the receiver role by issuing:
    aws sts get-caller-identity

If the output of this command contains the text assumed-role/ReceiverRole then you have successfully assumed the receiver role.

Decrypt the encrypted message archive in AWS KMS

Decrypt the encrypted message archive to access the plaintext of the message and message signature files.

To decrypt the encrypted message archive

  1. Issue the following command:
    aws kms decrypt \
        --key-id alias/sample-encrypt-decrypt-key \
        --ciphertext-blob fileb://EncryptedMessage \
        --encryption-algorithm RSAES_OAEP_SHA_256 \
        --output text \
        --query Plaintext | base64 --decode > message.tar.gz

  2. This will create an unencrypted message.tar.gz file that you can unpack with:
    tar -xvfz message.tar.gz

This, in turn, will expand the archive contents message.sig and message.json in your working directory.

Verify the message signature

To verify the signature on the message issue the following command:

aws kms verify \
    --key-id alias/sample-sign-verify-key \
    --message-type RAW \
    --message fileb://message.json \
    --signing-algorithm ECDSA_SHA_512 \
    --signature fileb://message.sig

In the response you should see that SignatureValid is marked true indicating that the signature has been verified using the specified sample-sign-verify-key that you granted the sender principal permission to generate signatures with.

View the message

Finally, open message.json and view the file’s contents by issuing the following command:

less message.json

You will see that the contents of the file have not been modified and still read:

    "message": "The Magic Words are Squeamish Ossifrage", 
    "sender": "Sender Principal" 

Note: Be careful to avoid making any changes to the contents of this file. Even a minor modification of the message contents will compromise the integrity of the message and cause future attempts at signature validation using your message.sig file to fail.


In this tutorial, you signed and encrypted data using two AWS KMS asymmetric CMKs and later decrypted and verified your signature using those CMKs.

You first created two asymmetric CMKs in AWS KMS, one for creating and verifying digital signatures and the other for encrypting and decrypting data. You then configured key policy permissions for your sender and receiver principals. Acting as your sender principal, you digitally signed a message in AWS KMS, added the message and signature to an archive and then encrypted that archive in AWS KMS. Next you assumed your receiver role and decrypted the archive in AWS KMS, viewed your message, and verified its signature in AWS KMS.

To learn more about the asymmetric keys feature of AWS KMS, please read the AWS KMS Developer Guide. If you have questions about the asymmetric keys feature, please start a new thread on the AWS KMS forum. If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.


J.D. Bean

J.D. is a Senior Solutions Architect at AWS working with public sector organizations and financial institutions based out of New York City. His interests include security, privacy, and compliance. He is passionate about his work enabling AWS customers’ successful cloud journeys. J.D. holds a Bachelor of Arts from The George Washington University and a Juris Doctor from New York University School of Law.

Architecting a Data Lake for Higher Education Student Analytics

Post Syndicated from Craig Jordan original https://aws.amazon.com/blogs/architecture/architecting-data-lake-for-higher-education-student-analytics/

One of the keys to identifying timely and impactful actions is having enough raw material to work with. However, this up-to-date information typically lives in the databases that sit behind several different applications. One of the first steps to finding data-driven insights is gathering that information into a single store that an analyst can use without interfering with those applications.

For years, reporting environments have relied on a data warehouse stored in a single, separate relational database management system (RDBMS). But now, due to the growing use of Software as a service (SaaS) applications and NoSQL database options, data may be stored outside the data center and in formats other than tables of rows and columns. It’s increasingly difficult to access the data these applications maintain, and a data warehouse may not be flexible enough to house the gathered information.

For these reasons, reporting teams are building data lakes, and those responsible for using data analytics at universities and colleges are no different. However, it can be challenging to know exactly how to start building this expanded data repository so it can be ready to use quickly and still expandable as future requirements are uncovered. Helping higher education institutions address these challenges is the topic of this post.

About Maryville University

Maryville University is a nationally recognized private institution located in St. Louis, Missouri, and was recently named the second fastest growing private university by The Chronicle of Higher Education. Even with its enrollment growth, the university is committed to a highly personalized education for each student, which requires reliable data that is readily available to multiple departments. University leaders want to offer the right help at the right time to students who may be having difficulty completing the first semester of their course of study. To get started, the data experts in the Office of Strategic Information and members of the IT Department needed to create a data environment to identify students needing assistance.

Critical data sources

Like most universities, Maryville’s student-related data centers around two significant sources: the student information system (SIS), which houses student profiles, course completion, and financial aid information; and the learning management system (LMS) in which students review course materials, complete assignments, and engage in online discussions with faculty and fellow students.

The first of these, the SIS, stores its data in an on-premises relational database, and for several years, a significant subset of its contents had been incorporated into the university’s data warehouse. The LMS, however, contains data that the team had not tried to bring into their data warehouse. Moreover, that data is managed by a SaaS application from Instructure, called “Canvas,” and is not directly accessible for traditional extract, transform, and load (ETL) processing. The team recognized they needed a new approach and began down the path of creating a data lake in AWS to support their analysis goals.

Getting started on the data lake

The first step the team took in building their data lake made use of an open source solution that Harvard’s IT department developed. The solution, comprised of AWS Lambda functions and Amazon Simple Storage Service (S3) buckets, is deployed using AWS CloudFormation. It enables any university that uses Canvas for their LMS to implement a solution that moves LMS data into an S3 data lake on a daily basis. The following diagram illustrates this portion of Maryville’s data lake architecture:

The data lake for the Learning Management System data

Diagram 1: The data lake for the Learning Management System data

The AWS Lambda functions invoke the LMS REST API on a daily schedule resulting in Maryville’s data, which has been previously unloaded and compressed by Canvas, to be securely stored into S3 objects. AWS Glue tables are defined to provide access to these S3 objects. Amazon Simple Notification Service (SNS) informs stakeholders the status of the data loads.

Expanding the data lake

The next step was deciding how to copy the SIS data into S3. The team decided to use the AWS Database Migration Service (DMS) to create daily snapshots of more than 2,500 tables from this database. DMS uses a source endpoint for secure access to the on-premises database instance over VPN. A target endpoint determines the specific S3 bucket into which the data should be written. A migration task defines which tables to copy from the source database along with other migration options. Finally, a replication instance, a fully managed virtual machine, runs the migration task to copy the data. With this configuration in place, the data lake architecture for SIS data looks like this:

Diagram 2: Migrating data from the Student Information System

Diagram 2: Migrating data from the Student Information System

Handling sensitive data

In building a data lake you have several options for handling sensitive data including:

  • Leaving it behind in the source system and avoid copying it through the data replication process
  • Copying it into the data lake, but taking precautions to ensure that access to it is limited to authorized staff
  • Copying it into the data lake, but applying processes to eliminate, mask, or otherwise obfuscate the data before it is made accessible to analysts and data scientists

The Maryville team decided to take the first of these approaches. Building the data lake gave them a natural opportunity to assess where this data was stored in the source system and then make changes to the source database itself to limit the number of highly sensitive data fields.

Validating the data lake

With these steps completed, the team turned to the final task, which was to validate the data lake. For this process they chose to make use of Amazon Athena, AWS Glue, and Amazon Redshift. AWS Glue provided multiple capabilities including metadata extraction, ETL, and data orchestration. Metadata extraction, completed by Glue crawlers, quickly converted the information that DMS wrote to S3 into metadata defined in the Glue data catalog. This enabled the data in S3 to be accessed using standard SQL statements interactively in Athena. Without the added cost and complexity of a database, Maryville’s data analyst was able to confirm that the data loads were completing successfully. He was also able to resolve specific issues encountered on particular tables. The SQL queries, written in Athena, could later be converted to ETL jobs in AWS Glue, where they could be triggered on a schedule to create additional data in S3. Athena and Glue enabled the ETL that was needed to transform the raw data delivered to S3 into prepared datasets necessary for existing dashboards.

Once curated datasets were created and stored in S3, the data was loaded into an AWS Redshift data warehouse, which supported direct access by tools outside of AWS using ODBC/JDBC drivers. This capability enabled Maryville’s team to further validate the data by attaching the data in Redshift to existing dashboards that were running in Maryville’s own data center. Redshift’s stored procedure language allowed the team to port some key ETL logic so that the engineering of these datasets could follow a process similar to approaches used in Maryville’s on-premises data warehouse environment.


The overall data lake/data warehouse architecture that the Maryville team constructed currently looks like this:

The complete architecture

Diagram 3: The complete architecture

Through this approach, Maryville’s two-person team has moved key data into position for use in a variety of workloads. The data in S3 is now readily accessible for ad hoc interactive SQL workloads in Athena, ETL jobs in Glue, and ultimately for machine learning workloads running in EC2, Lambda or Amazon Sagemaker. In addition, the S3 storage layer is easy to expand without interrupting prior workloads. At the time of this writing, the Maryville team is both beginning to use this environment for machine learning models described earlier as well as adding other data sources into the S3 layer.


The solution described in this post resulted from the collaborative effort of Christine McQuie, Data Engineer, and Josh Tepen, Cloud Engineer, at Maryville University, with guidance from Travis Berkley and Craig Jordan, AWS Solutions Architects.