Tag Archives: Key Management Service

How to BYOK (bring your own key) to AWS KMS for less than $15.00 a year using AWS CloudHSM

Post Syndicated from Tracy Pierce original https://aws.amazon.com/blogs/security/how-to-byok-bring-your-own-key-to-aws-kms-for-less-than-15-00-a-year-using-aws-cloudhsm/

Note: BYOK is helpful for certain use cases, but I recommend that you familiarize yourself with KMS best practices before you adopt this approach. You can review best practices in the AWS Key Management Services Best Practices (.pdf) whitepaper.

May 14, 2019: We’ve updated a sentence to clarify that this solution does not include instructions for creating an AWS CloudHSM cluster. For information about how to do this, please refer to our documentation.


Back in 2016, AWS Key Management Service (AWS KMS) announced the ability to bring your own keys (BYOK) for use with KMS-integrated AWS services and custom applications. This feature allows you more control over the creation, lifecycle, and durability of your keys. You decide the hardware or software used to generate the customer-managed customer master keys (CMKs), you determine when or if the keys will expire, and you get to upload your keys only when you need them and delete them when you’re done.

The documentation that walks you through how to import a key into AWS KMS provides an example that uses OpenSSL to create your master key. Some customers, however, don’t want to use OpenSSL. While it’s a valid method of creating the key material associated with a KMS CMK, security best practice is to perform key creation on a hardware security module (HSM) or a hardened key management system.

However, using an on-premises HSM to create and back up your imported keys can become expensive. You have to plan for factors like the cost of the device itself, its storage in a datacenter, electricity, maintenance of the device, and network costs, all of which can add up. An on-premises HSM device could run upwards of $10K annually even if used sparingly, in addition to the cost of purchasing the device in the first place. Even if you’re only using the HSM for key creation and backup and don’t need it on an ongoing basis, you might still need to keep it running to avoid complex re-initialization processes. This is where AWS CloudHSM comes in. CloudHSM offers HSMs that are under your control, in your virtual private cloud (VPC). You can spin up an HSM device, create your key material, export it, import it into AWS KMS for use, and then terminate the HSM (since CloudHSM saves your HSM state using secure backups). Because you’re only billed for the time your HSM instance is active, you can perform all of these steps for less than $15.00 a year!

Solution pricing

In this post, I’ll show you how to use an AWS CloudHSM cluster (which is a group that CloudHSM uses to keep all HSMs inside in sync with one another) with one HSM to create your key material. Only one HSM is needed, as you’ll use this HSM just for key generation and storage. Ongoing crypto operations that use the key will all be performed by KMS. AWS CloudHSM comes with an hourly fee that changes based on your region of choice; be sure to check the pricing page for updates prior to use. AWS KMS has a $1.00/month charge for all customer-managed CMKs, including those that you import yourself. In the following chart, I’ve calculated annual costs for some regions. These assume that you want to rotate your imported key annually and that you perform this operation once per year by running a single CloudHSM for one hour. I’ve included 12 monthly installments of $1.00/mo for your CMK. Depending on the speed of your application, additional costs may apply for KMS usage, which can be found on the AWS KMS pricing page.

REGIONCLOUDHSM PRICING STRUCTUREKMS PRICING STRUCTUREANNUAL TOTAL COST
US-EAST-1$1.60/HR$1/MO$13.60/YR
US-WEST-2$1.45/HR$1/MO$13.45/YR
EU-WEST-1$1.47/HR$1/MO$13.47/YR
US-GOV-EAST-1$2.08/HR$1/MO$14.08/YR
AP-SOUTHEAST-1$1.86/HR$1/MO$13.86/YR
EU-CENTRAL-1$1.92/HR$1/MO$13.92/YR
EU-NORTH-1$1.40/HR$1/MO$13.40/YR
AP-SOUTHEAST-2$1.99/HR$1/MO$13.99/YR
AP-NORTHEAST-1$1.81/HR$1/MO$13.81/YR

Solution overview

I’ll walk you through the process of creating a CMK in AWS KMS with no key material associated and then downloading the public key and import token that you’ll need in order to import the key material later on. I’ll also show you how to create, securely wrap, and export your symmetric key from AWS CloudHSM. Finally, I’ll show you how to import your key material into AWS KMS and then terminate your HSM to save on cost. The following diagram illustrates the steps covered in this post.
 

Figure 1: The process of creating a CMK in AWS KMS

Figure 1: The process of creating a CMK in AWS KMS

  1. Create a customer master key (CMK) in AWS KMS that has no key material associated.
  2. Download the import wrapping key and import token from KMS.
  3. Import the wrapping key provided by KMS into the HSM.
  4. Create a 256 bit symmetric key on AWS CloudHSM.
  5. Use the imported wrapping key to wrap the symmetric key.
  6. Import the symmetric key into AWS KMS using the import token from step 2.
  7. Terminate your HSM, which triggers a backup. Delete or leave your cluster, depending on your needs.

Prerequisites

In this walkthrough, I assume that you already have an AWS CloudHSM cluster set up and initialized with at least one HSM device, and an Amazon Elastic Compute Cloud (EC2) instance running Amazon Linux OS with the AWS CloudHSM client installed. You must have a crypto user (CU) on the HSM to perform the key creation and export functions. You’ll also need an IAM user or role with permissions to both AWS KMS and AWS CloudHSM, and with credentials configured in your AWS Command Line Interface (AWS CLI). Make sure to store your CU information and IAM credentials (if a user is created for this activity) in a safe location. You can use AWS Secrets Manager for secure storage.

Deploying the solution

For my demonstration, I’ll be using the AWS CLI. However, if you prefer working with a graphical user interface, step #1 and the important portion of step #3 can be done in the AWS KMS Console. All other steps are via AWS CLI only.

Step 1: Create the CMK with no key material associated

Begin by creating a customer master key (CMK) in AWS KMS that has no key material associated. The CLI command to create the CMK is as follows:

$ aws kms create-key –origin EXTERNAL –region us-east-1

If successful, you’ll see an output on the CLI similar to below. The KeyState will be PendingImport and the Origin will be EXTERNAL.


{
    "KeyMetadata": {
        "AWSAccountId": "111122223333",
        "KeyId": "1234abcd-12ab-34cd-56ef-1234567890ab",
        "Arn": "arn:aws:kms:us-east-1:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab",
        "CreationDate": 1551119868.343,
        "Enabled": false,
        "Description": "",
        "KeyUsage": "ENCRYPT_DECRYPT",
        "KeyState": "PendingImport",
        "Origin": "EXTERNAL",
        "KeyManager": "CUSTOMER"
    }
}

Step 2: Download the public key and import token

With the CMK ID created, you now need to download the import wrapping key and the import token. Wrapping is a method of encrypting the key so that it doesn’t pass in plaintext over the network. You need both the wrapping key and the import token in order to import a key into AWS KMS. You’ll use the public key to encrypt your key material, which ensures that your key is not exposed as it’s imported. When AWS KMS receives the encrypted key material, it will use the corresponding private component of the import wrapping key to decrypt it. The public and private key pair used for this action will always be a 2048-bit RSA key that is unique to each import operation. The import token contains metadata to ensure the key material is imported to the correct CMK ID. Both the import token and import wrapping key are on a time limit, so if you don’t import your key material within 24 hours of downloading them, the import wrapping key and import token will expire and you’ll need to download a new set from KMS.

  1. (OPTIONAL) In my example command, I’ll use the wrapping algorithm RSAES_OAEP_SHA_256 to encrypt and securely import my key into AWS KMS. If you’d like to choose a different algorithm, you may use any of the following: RSAES_OAEP_SHA_256, RSAES_OAEP_SHA_1, or RSAES_PKCS1_V1_5.
  2. In the command below, replace the example key ID (shown in red) with the key ID you received in step 1. If you’d like, you can also modify the wrapping algorithm. Then run the command.

    $ aws kms get-parameters-for-import –key-id <1234abcd-12ab-34cd-56ef-1234567890ab> –wrapping-algorithm <RSAES_OAEP_SHA_256> –wrapping-key-spec RSA_2048 –region us-east-1

    If the command is successful, you’ll see an output similar to what’s below. Your output will contain the import token and import wrapping key. Copy this output to a safe location, as you’ll need it in the next step. Keep in mind, these items will expire after 24 hours.

    
        {
            "KeyId": "arn:aws:kms:us-east-1:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab ",
            "ImportToken": "AQECAHjS7Vj0eN0qmSLqs8TfNtCGdw7OfGBfw+S8plTtCvzJBwAABrMwggavBgkqhkiG9w0BBwagggagMIIGnAIBADCCBpUGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMpK6U0uSCNsavw0OJAgEQgIIGZqZCSrJCf68dvMeP7Ah+AfPEgtKielFq9zG4aXAIm6Fx+AkH47Q8EDZgf16L16S6+BKc1xUOJZOd79g5UdETnWF6WPwAw4woDAqq1mYKSRtZpzkz9daiXsOXkqTlka5owac/r2iA2giqBFuIXfr2RPDB6lReyrhAQBYTwefTHnVqosKVGsv7/xxmuorn2iBwMfyqoj2gmykmoxrmWmCdK+jRdWKBHtriwplBTEHpUTdHmQnQzGVThfr611XFCQDZ+uk/wpVFY4jZ/Z/…"
            "PublicKey": "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA4KCiW36zofngmyVTOpMuFfHAXLWTfl2BAvaeq5puewoMt1zPwBP23qKdG10L7ChTVt4H9PCs1vzAWm2jGeWk5fO…",
            "ParametersValidTo": 1551207484.208
        }
        

  3. Because the output of the command is base64 encoded, you must base64 decode both components into binary data before use. To do this, use your favorite text editor to save the output into two separate files. Name one ImportToken.b64—it will have the output of the “ImportToken” section from above. Name the other PublicKey.b64—it will have the output of the “PublicKey” section from above. You will find example commands to save both below.
    
        echo 'AQECAHjS7Vj0eN0qmSLqs8TfNtCGdw7OfGBfw+S8plTtCvzJBwAABrMwggavBgkqhkiG9w0BBwagggagMIIGnAIBADCCBpUGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMpK6U0uSCNsavw0OJAgEQgIIGZqZCSrJCf68dvMeP7Ah+AfPEgtKielFq9zG4aXAIm6Fx+AkH47Q8EDZgf16L16S6+BKc1xUOJZOd79g5UdETnWF6WPwAw4woDAqq1mYKSRtZpzkz9daiXsOXkqTlka5owac/r2iA2giqBFuIXfr2RPDB6lReyrhAQBYTwefTHnVqosKVGsv7/xxmuorn2iBwMfyqoj2gmykmoxrmWmCdK+jRdWKBHtriwplBTEHpUTdHmQnQzGVThfr611XFCQDZ+uk/wpVFY4jZ/Z' > /ImportToken.b64
    
        echo 'MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA4KCiW36zofngmyVTOpMuFfHAXLWTfl2BAvaeq5puewoMt1zPwBP23qKdG10L7ChTVt4H9PCs1vzAWm2jGeWk5fO' > /PublicKey.b64 
        

  4. On both saved files, you must then run the following commands, which will base64 decode them and save them in their binary form:
    
        $ openssl enc -d -base64 -A -in PublicKey.b64 -out PublicKey.bin
    
        $ openssl enc -d -base64 -A -in ImportToken.b64 -out ImportToken.bin
        

Step 3: Import the import wrapping key provided by AWS KMS into your HSM

Now that you’ve created the CMK ID and prepared for the key material import, you’re going to move to AWS CloudHSM to create and export your symmetric encryption key. You’re going to import the import wrapping key provided by AWS KMS into your HSM. Before completing this step, be sure you’ve set up the prerequisites I listed at the start of this post.

  1. Log into your EC2 instance with the AWS CloudHSM client package installed, and launch the Key Management Utility. You will launch the utility using the command below:

    $ /opt/cloudhsm/bin/key_mgmt_util

  2. After launching the key_mgmt_util, you’ll need to log in as the CU user. Do so with the command below, being sure to replace <ExampleUser> and <Password123> for your own CU user and password.

    Command: loginHSM -u CU -p <Password123> -s <ExampleUser>

    You should see an output similar to the example below, letting you know that you’ve logged in correctly:

        
        Cfm3LoginHSM returned: 0x00 : HSM Return: SUCCESS
        Cluster Error Status
        Node id 1 and err state 0x00000000 : HSM Return: SUCCESS
        

    Next, you’ll import the import wrapping key provided by KMS into the HSM, so that you can use it to wrap the symmetric key you’re going to create.

  3. Open the file PublicKey.b64 in your favorite text editor and add the line —–BEGIN PUBLIC KEY—– at the very top of the file and the line —–END PUBLIC KEY—— at the very end. (An example of how this should look is below.) Save this new file as PublicKey.pem.
       
        -----BEGIN PUBLIC KEY-----
        MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA0UnLXHbP/jC/QykQQUroaB0xQYYVaZge//NrmIdYtYvaw32fcEUYjTHovWkYkmifUFVYkNaWKNRGuICo1oAY5cgowgxYcsBXZ4Pk2h3v43tIsxG63ZDLKDFju/dtGaa8+CwR8mR…
        -----END PUBLIC KEY-----
        

  4. Use the importPubKey command from the key_mgmt_util to import the key file. An example of the command is below. For the -l flag (label), I’ve named my example <wrapping-key> so that I’ll know what it’s for. You can name yours whatever you choose. Also, remember to replace <PublicKey.pem> with the actual filename of your public key.

    Command: importPubKey -l <wrapping-key> -f <PublicKey.pem>

    If successful, your output should look similar to the following example. Make note of the key handle, as this is the identifying ID for your wrapping key:

          
        Cfm3CreatePublicKey returned: 0x00 : HSM Return: SUCCESS
    
        Public Key Handle: 30 
        
        Cluster Error Status
        Node id 2 and err state 0x00000000 : HSM Return: SUCCESS
        

Step 4: Create a symmetric key on AWS CloudHSM

Next, you’ll create a symmetric key that you want to export from CloudHSM, so that you can import it into AWS KMS.

The first parameter you’ll use for this action is -t with the value 31. This is the key-type flag and 31 equals an AES key. KMS only accepts the AES key type. The second is -s with a value of 32. This is the key-size flag, and 32 equals a 32-byte key (256 bits). The last flag is the -l flag with the value BYOK_Key. This flag is simply a label to name your key, so you may alter this value however you wish.

Here’s what my example looks like:

Command: genSymKey -t 31 -s 32 -l BYOK-KMS

If successful, your output should look similar to what’s below. Make note of the key handle, as this will be the identifying ID of the key you wish to import into AWS KMS.

  
Cfm3LoginHSM returned: 0x00 : HSM Return: SUCCESS

Symmetric Key Created. Key Handle: 20

Cluster Error Status
Node id 1 and err state 0x00000000 : HSM Return: SUCCESS

Step 5: Use the imported import wrapping key to wrap the symmetric key

Now that you’ve imported the import wrapping key into your HSM in the PublicKey.pem file, I’ll show you how to use the PublicKey.pem file and the key_mgmt_util to wrap your symmetric key out of the HSM.

An example of the command is below. Here’s how to customize the parameters:

  • -k refers to the key handle of your symmetric key. Replace the variable that follows -k with your own key handle ID from step 4.
  • -w refers to the key handle of your wrapping key. Replace the variable with your own ID from step 3.4
  • -out refers to the file name of your exported key. Replace the variable with a file name of your choice.
  • -m refers to the wrapping-mechanism. In my example, I’ve used RSA-OAEP, which has a value of 8.
  • -t refers to the hash-type. In my example, I’ve used SHA256, which has a value of 3.
  • -noheader refers to the -noheader flag. This omits the header that specifies CloudHSM-specific key attributes.

Command: wrapKey -k <20> -w <30> -out <KMS-BYOK-March2019.bin> -m 8 -t 3 -noheader

If successful, you should see an output similar to what’s below:

 
Cfm2WrapKey5 returned: 0x00 : HSM Return: SUCCESS

	Key Wrapped.

	Wrapped Key written to file "KMS-BYOK-March2019.bin length 256

	Cfm2WrapKey returned: 0x00 : HSM Return: SUCCESS

With that, you’ve completed the AWS CloudHSM steps. You now have a symmetric key, securely wrapped for transport into AWS KMS. You can log out of the Key Management Utility by entering the word exit.

Step 6: Import the wrapped symmetric key into AWS KMS using the key import function

You’re ready to import the wrapped symmetric key into AWS KMS using the key import function. Make the following updates to the sample command that I provide below:

  • Replace the key-id value and file names with your own.
  • Leave the expiration model parameter blank, or choose from
    KEY_MATERIAL_EXPIRES or KEY_MATERIAL_DOES_NOT_EXPIRE. If you leave the parameter blank, it will default to KEY_MATERIAL_EXPIRES. This expiration model requires you to set the –valid-to parameter, which can be any time of your choosing so long as it follows the format in the example. If you choose KEY_MATERIAL_DOES_NOT_EXPIRE, you may leave the –valid-to option out of the command. To enforce an expiration and rotation of your key material, best practice is to use the KEY_MATERIAL_EXPIRES option and a date of 1-2 years.

Here’s my sample command:

$ aws kms import-key-material –key-id <1234abcd-12ab-34cd-56ef-1234567890ab> –encrypted-key-material fileb://<KMS-BYOK-March2019.bin> –import-token fileb://<ImportToken.bin> –expiration-model KEY_MATERIAL_EXPIRES –valid-to 2020-03-01T12:00:00-08:00 –region us-east-1

You can test that the import was successful by using the key ID to encrypt a small (under 4KB) file. An example command is below:

$ aws kms encrypt –key-id 1234abcd-12ab-34cd-56ef-1234567890ab –plaintext file://test.txt –region us-east-1

A successful call will output something similar to below:

 
{
    "KeyId": "arn:aws:kms:us-east-1:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab ", 
    "CiphertextBlob": "AQICAHi4Pj/Jhk6aHMZNegOpYFnnuiEKAtjRWwD33TdDnKNY5gHbUVdrwptz…"
}

Step 7: Terminate your HSM (which triggers a backup)

The last step in the process is to Terminate your HSM (which triggers a backup). Since you’ve imported the key material into AWS KMS, you no longer need to run the HSM. Terminating the HSM will automatically trigger a backup, which will take an exact copy of the key material and the users on your HSM and store it, encrypted, in an Amazon Simple Storage Service (Amazon S3) bucket. If you need to re-import your key into KMS or if your company’s security policy requires an annual rotation of your KMS master key(s), when the time comes, you can spin up an HSM from your CloudHSM backup and be back to where you started. You can re-import your existing key material or create key materials for your new CMK, either way, you’ll need to request a fresh import wrapping key and import token from KMS.

Deleting an HSM can be done with the command below. Replace <cluster-example> with your actual cluster ID and <hsm-example> with your HSM ID:

$ aws cloudhsmv2 delete-hsm –cluster-id <cluster-example> –hsm-id <hsm-example> –region us-east-1

Following these steps, you will have successfully defined a KMS CMK, created your own key material on a CloudHSM device, and then imported it into AWS KMS for use. Dependent upon region, all of this can be done for less than $15.00 a year.

Summary

In this post, I walked you through creating a CMK ID without encryption key material associated, creating and then exporting a symmetric key from AWS CloudHSM, and then importing it into AWS KMS for use with KMS-integrated AWS services. This process allows you to maintain ownership and management over your master keys, create them on FIPS 140-2 Level 3 validated hardware, and maintain a copy for disaster recovery scenarios. This saves not only the time and personnel required to maintain your own HSMs on-premises, but the cost of hardware, electricity, and housing of the device.

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

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Author

Tracy Pierce

Tracy Pierce is a Senior Cloud Support Engineer at AWS. She enjoys the peculiar culture of Amazon and uses that to ensure every day is exciting for her fellow engineers and customers alike. Customer Obsession is her highest priority and she shows this by improving processes, documentation, and building tutorials. She has her AS in Computer Security & Forensics from SCTD, SSCP certification, AWS Developer Associate certification, and AWS Security Specialist certification. Outside of work, she enjoys time with friends, her Great Dane, and three cats. She keeps work interesting by drawing cartoon characters on the walls at request.

Now You Can Create Encrypted Amazon EBS Volumes by Using Your Custom Encryption Keys When You Launch an Amazon EC2 Instance

Post Syndicated from Nishit Nagar original https://aws.amazon.com/blogs/security/create-encrypted-amazon-ebs-volumes-custom-encryption-keys-launch-amazon-ec2-instance-2/

Amazon Elastic Block Store (EBS) offers an encryption solution for your Amazon EBS volumes so you don’t have to build, maintain, and secure your own infrastructure for managing encryption keys for block storage. Amazon EBS encryption uses AWS Key Management Service (AWS KMS) customer master keys (CMKs) when creating encrypted Amazon EBS volumes, providing you all the benefits associated with using AWS KMS. You can specify either an AWS managed CMK or a customer-managed CMK to encrypt your Amazon EBS volume. If you use a customer-managed CMK, you retain granular control over your encryption keys, such as having AWS KMS rotate your CMK every year. To learn more about creating CMKs, see Creating Keys.

In this post, we demonstrate how to create an encrypted Amazon EBS volume using a customer-managed CMK when you launch an EC2 instance from the EC2 console, AWS CLI, and AWS SDK.

Creating an encrypted Amazon EBS volume from the EC2 console

Follow these steps to launch an EC2 instance from the EC2 console with Amazon EBS volumes that are encrypted by customer-managed CMKs:

  1. Sign in to the AWS Management Console and open the EC2 console.
  2. Select Launch instance, and then, in Step 1 of the wizard, select an Amazon Machine Image (AMI).
  3. In Step 2 of the wizard, select an instance type, and then provide additional configuration details in Step 3. For details about configuring your instances, see Launching an Instance.
  4. In Step 4 of the wizard, specify additional EBS volumes that you want to attach to your instances.
  5. To create an encrypted Amazon EBS volume, first add a new volume by selecting Add new volume. Leave the Snapshot column blank.
  6. In the Encrypted column, select your CMK from the drop-down menu. You can also paste the full Amazon Resource Name (ARN) of your custom CMK key ID in this box. To learn more about finding the ARN of a CMK, see Working with Keys.
  7. Select Review and Launch. Your instance will launch with an additional Amazon EBS volume with the key that you selected. To learn more about the launch wizard, see Launching an Instance with Launch Wizard.

Creating Amazon EBS encrypted volumes from the AWS CLI or SDK

You also can use RunInstances to launch an instance with additional encrypted Amazon EBS volumes by setting Encrypted to true and adding kmsKeyID along with the actual key ID in the BlockDeviceMapping object, as shown in the following command:

$> aws ec2 run-instances –image-id ami-b42209de –count 1 –instance-type m4.large –region us-east-1 –block-device-mappings file://mapping.json

In this example, mapping.json describes the properties of the EBS volume that you want to create:


{
"DeviceName": "/dev/sda1",
"Ebs": {
"DeleteOnTermination": true,
"VolumeSize": 100,
"VolumeType": "gp2",
"Encrypted": true,
"kmsKeyID": "arn:aws:kms:us-east-1:012345678910:key/abcd1234-a123-456a-a12b-a123b4cd56ef"
}
}

You can also launch instances with additional encrypted EBS data volumes via an Auto Scaling or Spot Fleet by creating a launch template with the above BlockDeviceMapping. For example:

$> aws ec2 create-launch-template –MyLTName –image-id ami-b42209de –count 1 –instance-type m4.large –region us-east-1 –block-device-mappings file://mapping.json

To learn more about launching an instance with the AWS CLI or SDK, see the AWS CLI Command Reference.

In this blog post, we’ve demonstrated a single-step, streamlined process for creating Amazon EBS volumes that are encrypted under your CMK when you launch your EC2 instance, thereby streamlining your instance launch workflow. To start using this functionality, navigate to the EC2 console.

If you have feedback about this blog post, submit comments in the Comments section below. If you have questions about this blog post, start a new thread on the Amazon EC2 forum or contact AWS Support.

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How to retain system tables’ data spanning multiple Amazon Redshift clusters and run cross-cluster diagnostic queries

Post Syndicated from Karthik Sonti original https://aws.amazon.com/blogs/big-data/how-to-retain-system-tables-data-spanning-multiple-amazon-redshift-clusters-and-run-cross-cluster-diagnostic-queries/

Amazon Redshift is a data warehouse service that logs the history of the system in STL log tables. The STL log tables manage disk space by retaining only two to five days of log history, depending on log usage and available disk space.

To retain STL tables’ data for an extended period, you usually have to create a replica table for every system table. Then, for each you load the data from the system table into the replica at regular intervals. By maintaining replica tables for STL tables, you can run diagnostic queries on historical data from the STL tables. You then can derive insights from query execution times, query plans, and disk-spill patterns, and make better cluster-sizing decisions. However, refreshing replica tables with live data from STL tables at regular intervals requires schedulers such as Cron or AWS Data Pipeline. Also, these tables are specific to one cluster and they are not accessible after the cluster is terminated. This is especially true for transient Amazon Redshift clusters that last for only a finite period of ad hoc query execution.

In this blog post, I present a solution that exports system tables from multiple Amazon Redshift clusters into an Amazon S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the Amazon S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.

I also provide another CloudFormation template later in this post. This second template helps to automate the creation of tables in the AWS Glue Data Catalog for the system tables’ data stored in Amazon S3. After the system tables are exported to Amazon S3, you can run cross-cluster diagnostic queries on the system tables’ data and derive insights about query executions in each Amazon Redshift cluster. You can do this using Amazon QuickSight, Amazon Athena, Amazon EMR, or Amazon Redshift Spectrum.

You can find all the code examples in this post, including the CloudFormation templates, AWS Glue extract, transform, and load (ETL) scripts, and the resolution steps for common errors you might encounter in this GitHub repository.

Solution overview

The solution in this post uses AWS Glue to export system tables’ log data from Amazon Redshift clusters into Amazon S3. The AWS Glue ETL jobs are invoked at a scheduled interval by AWS Lambda. AWS Systems Manager, which provides secure, hierarchical storage for configuration data management and secrets management, maintains the details of Amazon Redshift clusters for which the solution is enabled. The last-fetched time stamp values for the respective cluster-table combination are maintained in an Amazon DynamoDB table.

The following diagram covers the key steps involved in this solution.

The solution as illustrated in the preceding diagram flows like this:

  1. The Lambda function, invoke_rs_stl_export_etl, is triggered at regular intervals, as controlled by Amazon CloudWatch. It’s triggered to look up the AWS Systems Manager parameter store to get the details of the Amazon Redshift clusters for which the system table export is enabled.
  2. The same Lambda function, based on the Amazon Redshift cluster details obtained in step 1, invokes the AWS Glue ETL job designated for the Amazon Redshift cluster. If an ETL job for the cluster is not found, the Lambda function creates one.
  3. The ETL job invoked for the Amazon Redshift cluster gets the cluster credentials from the parameter store. It gets from the DynamoDB table the last exported time stamp of when each of the system tables was exported from the respective Amazon Redshift cluster.
  4. The ETL job unloads the system tables’ data from the Amazon Redshift cluster into an Amazon S3 bucket.
  5. The ETL job updates the DynamoDB table with the last exported time stamp value for each system table exported from the Amazon Redshift cluster.
  6. The Amazon Redshift cluster system tables’ data is available in Amazon S3 and is partitioned by cluster name and date for running cross-cluster diagnostic queries.

Understanding the configuration data

This solution uses AWS Systems Manager parameter store to store the Amazon Redshift cluster credentials securely. The parameter store also securely stores other configuration information that the AWS Glue ETL job needs for extracting and storing system tables’ data in Amazon S3. Systems Manager comes with a default AWS Key Management Service (AWS KMS) key that it uses to encrypt the password component of the Amazon Redshift cluster credentials.

The following table explains the global parameters and cluster-specific parameters required in this solution. The global parameters are defined once and applicable at the overall solution level. The cluster-specific parameters are specific to an Amazon Redshift cluster and repeat for each cluster for which you enable this post’s solution. The CloudFormation template explained later in this post creates these parameters as part of the deployment process.

Parameter nameTypeDescription
Global parametersdefined once and applied to all jobs
redshift_query_logs.global.s3_prefixStringThe Amazon S3 path where the query logs are exported. Under this path, each exported table is partitioned by cluster name and date.
redshift_query_logs.global.tempdirStringThe Amazon S3 path that AWS Glue ETL jobs use for temporarily staging the data.
redshift_query_logs.global.role>StringThe name of the role that the AWS Glue ETL jobs assume. Just the role name is sufficient. The complete Amazon Resource Name (ARN) is not required.
redshift_query_logs.global.enabled_cluster_listStringListA comma-separated list of cluster names for which system tables’ data export is enabled. This gives flexibility for a user to exclude certain clusters.
Cluster-specific parametersfor each cluster specified in the enabled_cluster_list parameter
redshift_query_logs.<<cluster_name>>.connectionStringThe name of the AWS Glue Data Catalog connection to the Amazon Redshift cluster. For example, if the cluster name is product_warehouse, the entry is redshift_query_logs.product_warehouse.connection.
redshift_query_logs.<<cluster_name>>.userStringThe user name that AWS Glue uses to connect to the Amazon Redshift cluster.
redshift_query_logs.<<cluster_name>>.passwordSecure StringThe password that AWS Glue uses to connect the Amazon Redshift cluster’s encrypted-by key that is managed in AWS KMS.

For example, suppose that you have two Amazon Redshift clusters, product-warehouse and category-management, for which the solution described in this post is enabled. In this case, the parameters shown in the following screenshot are created by the solution deployment CloudFormation template in the AWS Systems Manager parameter store.

Solution deployment

To make it easier for you to get started, I created a CloudFormation template that automatically configures and deploys the solution—only one step is required after deployment.

Prerequisites

To deploy the solution, you must have one or more Amazon Redshift clusters in a private subnet. This subnet must have a network address translation (NAT) gateway or a NAT instance configured, and also a security group with a self-referencing inbound rule for all TCP ports. For more information about why AWS Glue ETL needs the configuration it does, described previously, see Connecting to a JDBC Data Store in a VPC in the AWS Glue documentation.

To start the deployment, launch the CloudFormation template:

CloudFormation stack parameters

The following table lists and describes the parameters for deploying the solution to export query logs from multiple Amazon Redshift clusters.

PropertyDefaultDescription
S3BucketmybucketThe bucket this solution uses to store the exported query logs, stage code artifacts, and perform unloads from Amazon Redshift. For example, the mybucket/extract_rs_logs/data bucket is used for storing all the exported query logs for each system table partitioned by the cluster. The mybucket/extract_rs_logs/temp/ bucket is used for temporarily staging the unloaded data from Amazon Redshift. The mybucket/extract_rs_logs/code bucket is used for storing all the code artifacts required for Lambda and the AWS Glue ETL jobs.
ExportEnabledRedshiftClustersRequires InputA comma-separated list of cluster names from which the system table logs need to be exported.
DataStoreSecurityGroupsRequires InputA list of security groups with an inbound rule to the Amazon Redshift clusters provided in the parameter, ExportEnabledClusters. These security groups should also have a self-referencing inbound rule on all TCP ports, as explained on Connecting to a JDBC Data Store in a VPC.

After you launch the template and create the stack, you see that the following resources have been created:

  1. AWS Glue connections for each Amazon Redshift cluster you provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
  2. All parameters required for this solution created in the parameter store.
  3. The Lambda function that invokes the AWS Glue ETL jobs for each configured Amazon Redshift cluster at a regular interval of five minutes.
  4. The DynamoDB table that captures the last exported time stamps for each exported cluster-table combination.
  5. The AWS Glue ETL jobs to export query logs from each Amazon Redshift cluster provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
  6. The IAM roles and policies required for the Lambda function and AWS Glue ETL jobs.

After the deployment

For each Amazon Redshift cluster for which you enabled the solution through the CloudFormation stack parameter, ExportEnabledRedshiftClusters, the automated deployment includes temporary credentials that you must update after the deployment:

  1. Go to the parameter store.
  2. Note the parameters <<cluster_name>>.user and redshift_query_logs.<<cluster_name>>.password that correspond to each Amazon Redshift cluster for which you enabled this solution. Edit these parameters to replace the placeholder values with the right credentials.

For example, if product-warehouse is one of the clusters for which you enabled system table export, you edit these two parameters with the right user name and password and choose Save parameter.

Querying the exported system tables

Within a few minutes after the solution deployment, you should see Amazon Redshift query logs being exported to the Amazon S3 location, <<S3Bucket_you_provided>>/extract_redshift_query_logs/data/. In that bucket, you should see the eight system tables partitioned by customer name and date: stl_alert_event_log, stl_dlltext, stl_explain, stl_query, stl_querytext, stl_scan, stl_utilitytext, and stl_wlm_query.

To run cross-cluster diagnostic queries on the exported system tables, create external tables in the AWS Glue Data Catalog. To make it easier for you to get started, I provide a CloudFormation template that creates an AWS Glue crawler, which crawls the exported system tables stored in Amazon S3 and builds the external tables in the AWS Glue Data Catalog.

Launch this CloudFormation template to create external tables that correspond to the Amazon Redshift system tables. S3Bucket is the only input parameter required for this stack deployment. Provide the same Amazon S3 bucket name where the system tables’ data is being exported. After you successfully create the stack, you can see the eight tables in the database, redshift_query_logs_db, as shown in the following screenshot.

Now, navigate to the Athena console to run cross-cluster diagnostic queries. The following screenshot shows a diagnostic query executed in Athena that retrieves query alerts logged across multiple Amazon Redshift clusters.

You can build the following example Amazon QuickSight dashboard by running cross-cluster diagnostic queries on Athena to identify the hourly query count and the key query alert events across multiple Amazon Redshift clusters.

How to extend the solution

You can extend this post’s solution in two ways:

  • Add any new Amazon Redshift clusters that you spin up after you deploy the solution.
  • Add other system tables or custom query results to the list of exports from an Amazon Redshift cluster.

Extend the solution to other Amazon Redshift clusters

To extend the solution to more Amazon Redshift clusters, add the three cluster-specific parameters in the AWS Systems Manager parameter store following the guidelines earlier in this post. Modify the redshift_query_logs.global.enabled_cluster_list parameter to append the new cluster to the comma-separated string.

Extend the solution to add other tables or custom queries to an Amazon Redshift cluster

The current solution ships with the export functionality for the following Amazon Redshift system tables:

  • stl_alert_event_log
  • stl_dlltext
  • stl_explain
  • stl_query
  • stl_querytext
  • stl_scan
  • stl_utilitytext
  • stl_wlm_query

You can easily add another system table or custom query by adding a few lines of code to the AWS Glue ETL job, <<cluster-name>_extract_rs_query_logs. For example, suppose that from the product-warehouse Amazon Redshift cluster you want to export orders greater than $2,000. To do so, add the following five lines of code to the AWS Glue ETL job product-warehouse_extract_rs_query_logs, where product-warehouse is your cluster name:

  1. Get the last-processed time-stamp value. The function creates a value if it doesn’t already exist.

salesLastProcessTSValue = functions.getLastProcessedTSValue(trackingEntry=”mydb.sales_2000",job_configs=job_configs)

  1. Run the custom query with the time stamp.

returnDF=functions.runQuery(query="select * from sales s join order o where o.order_amnt > 2000 and sale_timestamp > '{}'".format (salesLastProcessTSValue) ,tableName="mydb.sales_2000",job_configs=job_configs)

  1. Save the results to Amazon S3.

functions.saveToS3(dataframe=returnDF,s3Prefix=s3Prefix,tableName="mydb.sales_2000",partitionColumns=["sale_date"],job_configs=job_configs)

  1. Get the latest time-stamp value from the returned data frame in Step 2.

latestTimestampVal=functions.getMaxValue(returnDF,"sale_timestamp",job_configs)

  1. Update the last-processed time-stamp value in the DynamoDB table.

functions.updateLastProcessedTSValue(“mydb.sales_2000",latestTimestampVal[0],job_configs)

Conclusion

In this post, I demonstrate a serverless solution to retain the system tables’ log data across multiple Amazon Redshift clusters. By using this solution, you can incrementally export the data from system tables into Amazon S3. By performing this export, you can build cross-cluster diagnostic queries, build audit dashboards, and derive insights into capacity planning by using services such as Athena. I also demonstrate how you can extend this solution to other ad hoc query use cases or tables other than system tables by adding a few lines of code.


Additional Reading

If you found this post useful, be sure to check out Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production and Amazon Redshift – 2017 Recap.


About the Author

Karthik Sonti is a senior big data architect at Amazon Web Services. He helps AWS customers build big data and analytical solutions and provides guidance on architecture and best practices.

 

 

 

 

AWS Achieves Spain’s ENS High Certification Across 29 Services

Post Syndicated from Oliver Bell original https://aws.amazon.com/blogs/security/aws-achieves-spains-ens-high-certification-across-29-services/

AWS has achieved Spain’s Esquema Nacional de Seguridad (ENS) High certification across 29 services. To successfully achieve the ENS High Standard, BDO España conducted an independent audit and attested that AWS meets confidentiality, integrity, and availability standards. This provides the assurance needed by Spanish Public Sector organizations wanting to build secure applications and services on AWS.

The National Security Framework, regulated under Royal Decree 3/2010, was developed through close collaboration between ENAC (Entidad Nacional de Acreditación), the Ministry of Finance and Public Administration and the CCN (National Cryptologic Centre), and other administrative bodies.

The following AWS Services are ENS High accredited across our Dublin and Frankfurt Regions:

  • Amazon API Gateway
  • Amazon DynamoDB
  • Amazon Elastic Container Service
  • Amazon Elastic Block Store
  • Amazon Elastic Compute Cloud
  • Amazon Elastic File System
  • Amazon Elastic MapReduce
  • Amazon ElastiCache
  • Amazon Glacier
  • Amazon Redshift
  • Amazon Relational Database Service
  • Amazon Simple Queue Service
  • Amazon Simple Storage Service
  • Amazon Simple Workflow Service
  • Amazon Virtual Private Cloud
  • Amazon WorkSpaces
  • AWS CloudFormation
  • AWS CloudTrail
  • AWS Config
  • AWS Database Migration Service
  • AWS Direct Connect
  • AWS Directory Service
  • AWS Elastic Beanstalk
  • AWS Key Management Service
  • AWS Lambda
  • AWS Snowball
  • AWS Storage Gateway
  • Elastic Load Balancing
  • VM Import/Export

AWS Key Management Service now offers FIPS 140-2 validated cryptographic modules enabling easier adoption of the service for regulated workloads

Post Syndicated from Sreekumar Pisharody original https://aws.amazon.com/blogs/security/aws-key-management-service-now-offers-fips-140-2-validated-cryptographic-modules-enabling-easier-adoption-of-the-service-for-regulated-workloads/

AWS Key Management Service (KMS) now uses FIPS 140-2 validated hardware security modules (HSM) and supports FIPS 140-2 validated endpoints, which provide independent assurances about the confidentiality and integrity of your keys. Having additional third-party assurances about the keys you manage in AWS KMS can make it easier to use the service for regulated workloads.

The process of gaining FIPS 140-2 validation is rigorous. First, AWS KMS HSMs were tested by an independent lab; those results were further reviewed by the Cryptographic Module Validation Program run by NIST. You can view the FIPS 140-2 certificate of the AWS Key Management Service HSM to get more details.

AWS KMS HSMs are designed so that no one, not even AWS employees, can retrieve your plaintext keys. The service uses the FIPS 140-2 validated HSMs to protect your keys when you request the service to create keys on your behalf or when you import them. Your plaintext keys are never written to disk and are only used in volatile memory of the HSMs while performing your requested cryptographic operation. Furthermore, AWS KMS keys are never transmitted outside the AWS Regions they were created. And HSM firmware updates are controlled by multi-party access that is audited and reviewed by an independent group within AWS.

AWS KMS HSMs are validated at level 2 overall and at level 3 in the following areas:

  • Cryptographic Module Specification
  • Roles, Services, and Authentication
  • Physical Security
  • Design Assurance

You can also make AWS KMS requests to API endpoints that terminate TLS sessions using a FIPS 140-2 validated cryptographic software module. To do so, connect to the unique FIPS 140-2 validated HTTPS endpoints in the AWS KMS requests made from your applications. AWS KMS FIPS 140-2 validated HTTPS endpoints are powered by the OpenSSL FIPS Object Module. FIPS 140-2 validated API endpoints are available in all commercial regions where AWS KMS is available.

Best Practices for Running Apache Kafka on AWS

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/best-practices-for-running-apache-kafka-on-aws/

This post was written in partnership with Intuit to share learnings, best practices, and recommendations for running an Apache Kafka cluster on AWS. Thanks to Vaishak Suresh and his colleagues at Intuit for their contribution and support.

Intuit, in their own words: Intuit, a leading enterprise customer for AWS, is a creator of business and financial management solutions. For more information on how Intuit partners with AWS, see our previous blog post, Real-time Stream Processing Using Apache Spark Streaming and Apache Kafka on AWS. Apache Kafka is an open-source, distributed streaming platform that enables you to build real-time streaming applications.

The best practices described in this post are based on our experience in running and operating large-scale Kafka clusters on AWS for more than two years. Our intent for this post is to help AWS customers who are currently running Kafka on AWS, and also customers who are considering migrating on-premises Kafka deployments to AWS.

AWS offers Amazon Kinesis Data Streams, a Kafka alternative that is fully managed.

Running your Kafka deployment on Amazon EC2 provides a high performance, scalable solution for ingesting streaming data. AWS offers many different instance types and storage option combinations for Kafka deployments. However, given the number of possible deployment topologies, it’s not always trivial to select the most appropriate strategy suitable for your use case.

In this blog post, we cover the following aspects of running Kafka clusters on AWS:

  • Deployment considerations and patterns
  • Storage options
  • Instance types
  • Networking
  • Upgrades
  • Performance tuning
  • Monitoring
  • Security
  • Backup and restore

Note: While implementing Kafka clusters in a production environment, make sure also to consider factors like your number of messages, message size, monitoring, failure handling, and any operational issues.

Deployment considerations and patterns

In this section, we discuss various deployment options available for Kafka on AWS, along with pros and cons of each option. A successful deployment starts with thoughtful consideration of these options. Considering availability, consistency, and operational overhead of the deployment helps when choosing the right option.

Single AWS Region, Three Availability Zones, All Active

One typical deployment pattern (all active) is in a single AWS Region with three Availability Zones (AZs). One Kafka cluster is deployed in each AZ along with Apache ZooKeeper and Kafka producer and consumer instances as shown in the illustration following.

In this pattern, this is the Kafka cluster deployment:

  • Kafka producers and Kafka cluster are deployed on each AZ.
  • Data is distributed evenly across three Kafka clusters by using Elastic Load Balancer.
  • Kafka consumers aggregate data from all three Kafka clusters.

Kafka cluster failover occurs this way:

  • Mark down all Kafka producers
  • Stop consumers
  • Debug and restack Kafka
  • Restart consumers
  • Restart Kafka producers

Following are the pros and cons of this pattern.

ProsCons
  • Highly available
  • Can sustain the failure of two AZs
  • No message loss during failover
  • Simple deployment

 

  • Very high operational overhead:
    • All changes need to be deployed three times, one for each Kafka cluster
    • Maintaining and monitoring three Kafka clusters
    • Maintaining and monitoring three consumer clusters

A restart is required for patching and upgrading brokers in a Kafka cluster. In this approach, a rolling upgrade is done separately for each cluster.

Single Region, Three Availability Zones, Active-Standby

Another typical deployment pattern (active-standby) is in a single AWS Region with a single Kafka cluster and Kafka brokers and Zookeepers distributed across three AZs. Another similar Kafka cluster acts as a standby as shown in the illustration following. You can use Kafka mirroring with MirrorMaker to replicate messages between any two clusters.

In this pattern, this is the Kafka cluster deployment:

  • Kafka producers are deployed on all three AZs.
  • Only one Kafka cluster is deployed across three AZs (active).
  • ZooKeeper instances are deployed on each AZ.
  • Brokers are spread evenly across all three AZs.
  • Kafka consumers can be deployed across all three AZs.
  • Standby Kafka producers and a Multi-AZ Kafka cluster are part of the deployment.

Kafka cluster failover occurs this way:

  • Switch traffic to standby Kafka producers cluster and Kafka cluster.
  • Restart consumers to consume from standby Kafka cluster.

Following are the pros and cons of this pattern.

ProsCons
  • Less operational overhead when compared to the first option
  • Only one Kafka cluster to manage and consume data from
  • Can handle single AZ failures without activating a standby Kafka cluster
  • Added latency due to cross-AZ data transfer among Kafka brokers
  • For Kafka versions before 0.10, replicas for topic partitions have to be assigned so they’re distributed to the brokers on different AZs (rack-awareness)
  • The cluster can become unavailable in case of a network glitch, where ZooKeeper does not see Kafka brokers
  • Possibility of in-transit message loss during failover

Intuit recommends using a single Kafka cluster in one AWS Region, with brokers distributing across three AZs (single region, three AZs). This approach offers stronger fault tolerance than otherwise, because a failed AZ won’t cause Kafka downtime.

Storage options

There are two storage options for file storage in Amazon EC2:

Ephemeral storage is local to the Amazon EC2 instance. It can provide high IOPS based on the instance type. On the other hand, Amazon EBS volumes offer higher resiliency and you can configure IOPS based on your storage needs. EBS volumes also offer some distinct advantages in terms of recovery time. Your choice of storage is closely related to the type of workload supported by your Kafka cluster.

Kafka provides built-in fault tolerance by replicating data partitions across a configurable number of instances. If a broker fails, you can recover it by fetching all the data from other brokers in the cluster that host the other replicas. Depending on the size of the data transfer, it can affect recovery process and network traffic. These in turn eventually affect the cluster’s performance.

The following table contrasts the benefits of using an instance store versus using EBS for storage.

Instance storeEBS
  • Instance storage is recommended for large- and medium-sized Kafka clusters. For a large cluster, read/write traffic is distributed across a high number of brokers, so the loss of a broker has less of an impact. However, for smaller clusters, a quick recovery for the failed node is important, but a failed broker takes longer and requires more network traffic for a smaller Kafka cluster.
  • Storage-optimized instances like h1, i3, and d2 are an ideal choice for distributed applications like Kafka.

 

  • The primary advantage of using EBS in a Kafka deployment is that it significantly reduces data-transfer traffic when a broker fails or must be replaced. The replacement broker joins the cluster much faster.
  • Data stored on EBS is persisted in case of an instance failure or termination. The broker’s data stored on an EBS volume remains intact, and you can mount the EBS volume to a new EC2 instance. Most of the replicated data for the replacement broker is already available in the EBS volume and need not be copied over the network from another broker. Only the changes made after the original broker failure need to be transferred across the network. That makes this process much faster.

 

 

Intuit chose EBS because of their frequent instance restacking requirements and also other benefits provided by EBS.

Generally, Kafka deployments use a replication factor of three. EBS offers replication within their service, so Intuit chose a replication factor of two instead of three.

Instance types

The choice of instance types is generally driven by the type of storage required for your streaming applications on a Kafka cluster. If your application requires ephemeral storage, h1, i3, and d2 instances are your best option.

Intuit used r3.xlarge instances for their brokers and r3.large for ZooKeeper, with ST1 (throughput optimized HDD) EBS for their Kafka cluster.

Here are sample benchmark numbers from Intuit tests.

ConfigurationBroker bytes (MB/s)
  • r3.xlarge
  • ST1 EBS
  • 12 brokers
  • 12 partitions

 

Aggregate 346.9

If you need EBS storage, then AWS has a newer-generation r4 instance. The r4 instance is superior to R3 in many ways:

  • It has a faster processor (Broadwell).
  • EBS is optimized by default.
  • It features networking based on Elastic Network Adapter (ENA), with up to 10 Gbps on smaller sizes.
  • It costs 20 percent less than R3.

Note: It’s always best practice to check for the latest changes in instance types.

Networking

The network plays a very important role in a distributed system like Kafka. A fast and reliable network ensures that nodes can communicate with each other easily. The available network throughput controls the maximum amount of traffic that Kafka can handle. Network throughput, combined with disk storage, is often the governing factor for cluster sizing.

If you expect your cluster to receive high read/write traffic, select an instance type that offers 10-Gb/s performance.

In addition, choose an option that keeps interbroker network traffic on the private subnet, because this approach allows clients to connect to the brokers. Communication between brokers and clients uses the same network interface and port. For more details, see the documentation about IP addressing for EC2 instances.

If you are deploying in more than one AWS Region, you can connect the two VPCs in the two AWS Regions using cross-region VPC peering. However, be aware of the networking costs associated with cross-AZ deployments.

Upgrades

Kafka has a history of not being backward compatible, but its support of backward compatibility is getting better. During a Kafka upgrade, you should keep your producer and consumer clients on a version equal to or lower than the version you are upgrading from. After the upgrade is finished, you can start using a new protocol version and any new features it supports. There are three upgrade approaches available, discussed following.

Rolling or in-place upgrade

In a rolling or in-place upgrade scenario, upgrade one Kafka broker at a time. Take into consideration the recommendations for doing rolling restarts to avoid downtime for end users.

Downtime upgrade

If you can afford the downtime, you can take your entire cluster down, upgrade each Kafka broker, and then restart the cluster.

Blue/green upgrade

Intuit followed the blue/green deployment model for their workloads, as described following.

If you can afford to create a separate Kafka cluster and upgrade it, we highly recommend the blue/green upgrade scenario. In this scenario, we recommend that you keep your clusters up-to-date with the latest Kafka version. For additional details on Kafka version upgrades or more details, see the Kafka upgrade documentation.

The following illustration shows a blue/green upgrade.

In this scenario, the upgrade plan works like this:

  • Create a new Kafka cluster on AWS.
  • Create a new Kafka producers stack to point to the new Kafka cluster.
  • Create topics on the new Kafka cluster.
  • Test the green deployment end to end (sanity check).
  • Using Amazon Route 53, change the new Kafka producers stack on AWS to point to the new green Kafka environment that you have created.

The roll-back plan works like this:

  • Switch Amazon Route 53 to the old Kafka producers stack on AWS to point to the old Kafka environment.

For additional details on blue/green deployment architecture using Kafka, see the re:Invent presentation Leveraging the Cloud with a Blue-Green Deployment Architecture.

Performance tuning

You can tune Kafka performance in multiple dimensions. Following are some best practices for performance tuning.

 These are some general performance tuning techniques:

  • If throughput is less than network capacity, try the following:
    • Add more threads
    • Increase batch size
    • Add more producer instances
    • Add more partitions
  • To improve latency when acks =-1, increase your num.replica.fetches value.
  • For cross-AZ data transfer, tune your buffer settings for sockets and for OS TCP.
  • Make sure that num.io.threads is greater than the number of disks dedicated for Kafka.
  • Adjust num.network.threads based on the number of producers plus the number of consumers plus the replication factor.
  • Your message size affects your network bandwidth. To get higher performance from a Kafka cluster, select an instance type that offers 10 Gb/s performance.

For Java and JVM tuning, try the following:

  • Minimize GC pauses by using the Oracle JDK, which uses the new G1 garbage-first collector.
  • Try to keep the Kafka heap size below 4 GB.

Monitoring

Knowing whether a Kafka cluster is working correctly in a production environment is critical. Sometimes, just knowing that the cluster is up is enough, but Kafka applications have many moving parts to monitor. In fact, it can easily become confusing to understand what’s important to watch and what you can set aside. Items to monitor range from simple metrics about the overall rate of traffic, to producers, consumers, brokers, controller, ZooKeeper, topics, partitions, messages, and so on.

For monitoring, Intuit used several tools, including Newrelec, Wavefront, Amazon CloudWatch, and AWS CloudTrail. Our recommended monitoring approach follows.

For system metrics, we recommend that you monitor:

  • CPU load
  • Network metrics
  • File handle usage
  • Disk space
  • Disk I/O performance
  • Garbage collection
  • ZooKeeper

For producers, we recommend that you monitor:

  • Batch-size-avg
  • Compression-rate-avg
  • Waiting-threads
  • Buffer-available-bytes
  • Record-queue-time-max
  • Record-send-rate
  • Records-per-request-avg

For consumers, we recommend that you monitor:

  • Batch-size-avg
  • Compression-rate-avg
  • Waiting-threads
  • Buffer-available-bytes
  • Record-queue-time-max
  • Record-send-rate
  • Records-per-request-avg

Security

Like most distributed systems, Kafka provides the mechanisms to transfer data with relatively high security across the components involved. Depending on your setup, security might involve different services such as encryption, Kerberos, Transport Layer Security (TLS) certificates, and advanced access control list (ACL) setup in brokers and ZooKeeper. The following tells you more about the Intuit approach. For details on Kafka security not covered in this section, see the Kafka documentation.

Encryption at rest

For EBS-backed EC2 instances, you can enable encryption at rest by using Amazon EBS volumes with encryption enabled. Amazon EBS uses AWS Key Management Service (AWS KMS) for encryption. For more details, see Amazon EBS Encryption in the EBS documentation. For instance store–backed EC2 instances, you can enable encryption at rest by using Amazon EC2 instance store encryption.

Encryption in transit

Kafka uses TLS for client and internode communications.

Authentication

Authentication of connections to brokers from clients (producers and consumers) to other brokers and tools uses either Secure Sockets Layer (SSL) or Simple Authentication and Security Layer (SASL).

Kafka supports Kerberos authentication. If you already have a Kerberos server, you can add Kafka to your current configuration.

Authorization

In Kafka, authorization is pluggable and integration with external authorization services is supported.

Backup and restore

The type of storage used in your deployment dictates your backup and restore strategy.

The best way to back up a Kafka cluster based on instance storage is to set up a second cluster and replicate messages using MirrorMaker. Kafka’s mirroring feature makes it possible to maintain a replica of an existing Kafka cluster. Depending on your setup and requirements, your backup cluster might be in the same AWS Region as your main cluster or in a different one.

For EBS-based deployments, you can enable automatic snapshots of EBS volumes to back up volumes. You can easily create new EBS volumes from these snapshots to restore. We recommend storing backup files in Amazon S3.

For more information on how to back up in Kafka, see the Kafka documentation.

Conclusion

In this post, we discussed several patterns for running Kafka in the AWS Cloud. AWS also provides an alternative managed solution with Amazon Kinesis Data Streams, there are no servers to manage or scaling cliffs to worry about, you can scale the size of your streaming pipeline in seconds without downtime, data replication across availability zones is automatic, you benefit from security out of the box, Kinesis Data Streams is tightly integrated with a wide variety of AWS services like Lambda, Redshift, Elasticsearch and it supports open source frameworks like Storm, Spark, Flink, and more. You may refer to kafka-kinesis connector.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Implement Serverless Log Analytics Using Amazon Kinesis Analytics and Real-time Clickstream Anomaly Detection with Amazon Kinesis Analytics.


About the Author

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.

 

 

Now Available: Encryption at Rest for Amazon DynamoDB

Post Syndicated from Nitin Sagar original https://aws.amazon.com/blogs/security/now-available-encryption-at-rest-for-amazon-dynamodb/

Today, AWS announced Amazon DynamoDB encryption at rest, a new DynamoDB feature that gives you enhanced security of your data at rest by encrypting it using your associated AWS Key Management Service encryption keys. Encryption at rest can help you meet your security requirements for regulatory compliance.

You now can create an encrypted DynamoDB table anytime with a single click in the AWS Management Console or a single API call. Encrypting DynamoDB data has no impact on table performance. DynamoDB encryption at rest is available starting today in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Ireland) Regions for no additional fees.

For more information, see the full AWS Blog post.

– Nitin

New – Encryption at Rest for DynamoDB

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-encryption-at-rest-for-dynamodb/

At AWS re:Invent 2017, Werner encouraged his audience to “Dance like nobody is watching, and to encrypt like everyone is:

The AWS team is always eager to add features that make it easier for you to protect your sensitive data and to help you to achieve your compliance objectives. For example, in 2017 we launched encryption at rest for SQS and EFS, additional encryption options for S3, and server-side encryption of Kinesis Data Streams.

Today we are giving you another data protection option with the introduction of encryption at rest for Amazon DynamoDB. You simply enable encryption when you create a new table and DynamoDB takes care of the rest. Your data (tables, local secondary indexes, and global secondary indexes) will be encrypted using AES-256 and a service-default AWS Key Management Service (KMS) key. The encryption adds no storage overhead and is completely transparent; you can insert, query, scan, and delete items as before. The team did not observe any changes in latency after enabling encryption and running several different workloads on an encrypted DynamoDB table.

Creating an Encrypted Table
You can create an encrypted table from the AWS Management Console, API (CreateTable), or CLI (create-table). I’ll use the console! I enter the name and set up the primary key as usual:

Before proceeding, I uncheck Use default settings, scroll down to the Encrypytion section, and check Enable encryption. Then I click Create and my table is created in encrypted form:

I can see the encryption setting for the table at a glance:

When my compliance team asks me to show them how DynamoDB uses the key to encrypt the data, I can create a AWS CloudTrail trail, insert an item, and then scan the table to see the calls to the AWS KMS API. Here’s an extract from the trail:

{
  "eventTime": "2018-01-24T00:06:34Z",
  "eventSource": "kms.amazonaws.com",
  "eventName": "Decrypt",
  "awsRegion": "us-west-2",
  "sourceIPAddress": "dynamodb.amazonaws.com",
  "userAgent": "dynamodb.amazonaws.com",
  "requestParameters": {
    "encryptionContext": {
      "aws:dynamodb:tableName": "reg-users",
      "aws:dynamodb:subscriberId": "1234567890"
    }
  },
  "responseElements": null,
  "requestID": "7072def1-009a-11e8-9ab9-4504c26bd391",
  "eventID": "3698678a-d04e-48c7-96f2-3d734c5c7903",
  "readOnly": true,
  "resources": [
    {
      "ARN": "arn:aws:kms:us-west-2:1234567890:key/e7bd721d-37f3-4acd-bec5-4d08c765f9f5",
      "accountId": "1234567890",
      "type": "AWS::KMS::Key"
    }
  ]
}

Available Now
This feature is available now in the US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) Regions and you can start using it today.

There’s no charge for the encryption; you will be charged for the calls that DynamoDB makes to AWS KMS on your behalf.

Jeff;

 

How to Encrypt Amazon S3 Objects with the AWS SDK for Ruby

Post Syndicated from Doug Schwartz original https://aws.amazon.com/blogs/security/how-to-encrypt-amazon-s3-objects-with-the-aws-sdk-for-ruby/

AWS KMS image

Recently, Amazon announced some new Amazon S3 encryption and security features. The AWS Blog post showed how to use the Amazon S3 console to take advantage of these new features. However, if you have a large number of Amazon S3 buckets, using the console to implement these features could take hours, if not days. As an alternative, I created documentation topics in the AWS SDK for Ruby Developer Guide that include code examples showing you how to use the new Amazon S3 encryption features using the AWS SDK for Ruby.

What are my encryption options?

You can encrypt Amazon S3 bucket objects on a server or on a client:

  • When you encrypt objects on a server, you request that Amazon S3 encrypt the objects before saving them to disk in data centers and decrypt the objects when you download them. The main advantage of this approach is that Amazon S3 manages the entire encryption process.
  • When you encrypt objects on a client, you encrypt the objects before you upload them to Amazon S3. In this case, you manage the encryption process, the encryption keys, and related tools. Use this option when:
    • Company policy and standards require it.
    • You already have a development process in place that meets your needs.

    Encrypting on the client has always been available, but you should know the following points:

    • You must be diligent about protecting your encryption keys, which is analogous to having a burglar-proof lock on your front door. If you leave a key under the mat, your security is compromised.
    • If you lose your encryption keys, you won’t be able to decrypt your data.

    If you encrypt objects on the client, we strongly recommend that you use an AWS Key Management Service (AWS KMS) managed customer master key (CMK)

How to use encryption on a server

You can specify that Amazon S3 automatically encrypts objects as you upload them to a bucket or require that objects uploaded to an Amazon S3 bucket include encryption on a server before they are uploaded to an Amazon S3 bucket.

The advantage of these settings is that when you specify them, you ensure that objects uploaded to Amazon S3 are encrypted. Alternatively, you can have Amazon S3 encrypt individual objects on the server as you upload them to a bucket or encrypt them on the server with your own key as you upload them to a bucket.

The AWS SDK for Ruby Developer Guide now contains the following topics that explain your encryption options on a server:

How to use encryption on a client

You can encrypt objects on a client before you upload them to a bucket and decrypt them after you download them from a bucket by using the Amazon S3 encryption client.

The AWS SDK for Ruby Developer Guide now contains the following topics that explain your encryption options on the client:

Note: The Amazon S3 encryption client in the AWS SDK for Ruby is compatible with other Amazon S3 encryption clients, but it is not compatible with other AWS client-side encryption libraries, including the AWS Encryption SDK and the Amazon DynamoDB encryption client for Java. Each library returns a different ciphertext (“encrypted message”) format, so you can’t use one library to encrypt objects and a different library to decrypt them. For more information, see Protecting Data Using Client-Side Encryption.

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about encrypting objects on servers and clients, start a new thread on the Amazon S3 forum or contact AWS Support.

– Doug

Now Open AWS EU (Paris) Region

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-open-aws-eu-paris-region/

Today we are launching our 18th AWS Region, our fourth in Europe. Located in the Paris area, AWS customers can use this Region to better serve customers in and around France.

The Details
The new EU (Paris) Region provides a broad suite of AWS services including Amazon API Gateway, Amazon Aurora, Amazon CloudFront, Amazon CloudWatch, CloudWatch Events, Amazon CloudWatch Logs, Amazon DynamoDB, Amazon Elastic Compute Cloud (EC2), EC2 Container Registry, Amazon ECS, Amazon Elastic Block Store (EBS), Amazon EMR, Amazon ElastiCache, Amazon Elasticsearch Service, Amazon Glacier, Amazon Kinesis Streams, Polly, Amazon Redshift, Amazon Relational Database Service (RDS), Amazon Route 53, Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), Amazon Simple Storage Service (S3), Amazon Simple Workflow Service (SWF), Amazon Virtual Private Cloud, Auto Scaling, AWS Certificate Manager (ACM), AWS CloudFormation, AWS CloudTrail, AWS CodeDeploy, AWS Config, AWS Database Migration Service, AWS Direct Connect, AWS Elastic Beanstalk, AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), AWS Lambda, AWS Marketplace, AWS OpsWorks Stacks, AWS Personal Health Dashboard, AWS Server Migration Service, AWS Service Catalog, AWS Shield Standard, AWS Snowball, AWS Snowball Edge, AWS Snowmobile, AWS Storage Gateway, AWS Support (including AWS Trusted Advisor), Elastic Load Balancing, and VM Import.

The Paris Region supports all sizes of C5, M5, R4, T2, D2, I3, and X1 instances.

There are also four edge locations for Amazon Route 53 and Amazon CloudFront: three in Paris and one in Marseille, all with AWS WAF and AWS Shield. Check out the AWS Global Infrastructure page to learn more about current and future AWS Regions.

The Paris Region will benefit from three AWS Direct Connect locations. Telehouse Voltaire is available today. AWS Direct Connect will also become available at Equinix Paris in early 2018, followed by Interxion Paris.

All AWS infrastructure regions around the world are designed, built, and regularly audited to meet the most rigorous compliance standards and to provide high levels of security for all AWS customers. These include ISO 27001, ISO 27017, ISO 27018, SOC 1 (Formerly SAS 70), SOC 2 and SOC 3 Security & Availability, PCI DSS Level 1, and many more. This means customers benefit from all the best practices of AWS policies, architecture, and operational processes built to satisfy the needs of even the most security sensitive customers.

AWS is certified under the EU-US Privacy Shield, and the AWS Data Processing Addendum (DPA) is GDPR-ready and available now to all AWS customers to help them prepare for May 25, 2018 when the GDPR becomes enforceable. The current AWS DPA, as well as the AWS GDPR DPA, allows customers to transfer personal data to countries outside the European Economic Area (EEA) in compliance with European Union (EU) data protection laws. AWS also adheres to the Cloud Infrastructure Service Providers in Europe (CISPE) Code of Conduct. The CISPE Code of Conduct helps customers ensure that AWS is using appropriate data protection standards to protect their data, consistent with the GDPR. In addition, AWS offers a wide range of services and features to help customers meet the requirements of the GDPR, including services for access controls, monitoring, logging, and encryption.

From Our Customers
Many AWS customers are preparing to use this new Region. Here’s a small sample:

Societe Generale, one of the largest banks in France and the world, has accelerated their digital transformation while working with AWS. They developed SG Research, an application that makes reports from Societe Generale’s analysts available to corporate customers in order to improve the decision-making process for investments. The new AWS Region will reduce latency between applications running in the cloud and in their French data centers.

SNCF is the national railway company of France. Their mobile app, powered by AWS, delivers real-time traffic information to 14 million riders. Extreme weather, traffic events, holidays, and engineering works can cause usage to peak at hundreds of thousands of users per second. They are planning to use machine learning and big data to add predictive features to the app.

Radio France, the French public radio broadcaster, offers seven national networks, and uses AWS to accelerate its innovation and stay competitive.

Les Restos du Coeur, a French charity that provides assistance to the needy, delivering food packages and participating in their social and economic integration back into French society. Les Restos du Coeur is using AWS for its CRM system to track the assistance given to each of their beneficiaries and the impact this is having on their lives.

AlloResto by JustEat (a leader in the French FoodTech industry), is using AWS to to scale during traffic peaks and to accelerate their innovation process.

AWS Consulting and Technology Partners
We are already working with a wide variety of consulting, technology, managed service, and Direct Connect partners in France. Here’s a partial list:

AWS Premier Consulting PartnersAccenture, Capgemini, Claranet, CloudReach, DXC, and Edifixio.

AWS Consulting PartnersABC Systemes, Atos International SAS, CoreExpert, Cycloid, Devoteam, LINKBYNET, Oxalide, Ozones, Scaleo Information Systems, and Sopra Steria.

AWS Technology PartnersAxway, Commerce Guys, MicroStrategy, Sage, Software AG, Splunk, Tibco, and Zerolight.

AWS in France
We have been investing in Europe, with a focus on France, for the last 11 years. We have also been developing documentation and training programs to help our customers to improve their skills and to accelerate their journey to the AWS Cloud.

As part of our commitment to AWS customers in France, we plan to train more than 25,000 people in the coming years, helping them develop highly sought after cloud skills. They will have access to AWS training resources in France via AWS Academy, AWSome days, AWS Educate, and webinars, all delivered in French by AWS Technical Trainers and AWS Certified Trainers.

Use it Today
The EU (Paris) Region is open for business now and you can start using it today!

Jeff;

 

Using Trusted Timestamping With Java

Post Syndicated from Bozho original https://techblog.bozho.net/using-trusted-timestamping-java/

Trusted timestamping is the process of having a trusted third party (“Time stamping authority”, TSA) certify the time of a given event in electronic form. The EU regulation eIDAS gives these timestamps legal strength – i.e. nobody can dispute the time or the content of the event if it was timestamped. It is applicable to multiple scenarios, including timestamping audit logs. (Note: timestamping is not sufficient for a good audit trail as it does not prevent a malicious actor from deleting the event altogether)

There are a number of standards for trusted timestamping, the core one being RFC 3161. As most RFCs it is hard to read. Fortunately for Java users, BouncyCastle implements the standard. Unfortunately, as with most security APIs, working with it is hard, even abysmal. I had to implement it, so I’ll share the code needed to timestamp data.

The whole gist can be found here, but I’ll try to explain the main flow. Obviously, there is a lot of code that’s there to simply follow the standard. The BouncyCastle classes are a maze that’s hard to navigate.

The main method is obviously timestamp(hash, tsaURL, username, password):

public TimestampResponseDto timestamp(byte[] hash, String tsaUrl, String tsaUsername, String tsaPassword) throws IOException {
    MessageImprint imprint = new MessageImprint(sha512oid, hash);

    TimeStampReq request = new TimeStampReq(imprint, null, new ASN1Integer(random.nextLong()),
            ASN1Boolean.TRUE, null);

    byte[] body = request.getEncoded();
    try {
        byte[] responseBytes = getTSAResponse(body, tsaUrl, tsaUsername, tsaPassword);

        ASN1StreamParser asn1Sp = new ASN1StreamParser(responseBytes);
        TimeStampResp tspResp = TimeStampResp.getInstance(asn1Sp.readObject());
        TimeStampResponse tsr = new TimeStampResponse(tspResp);

        checkForErrors(tsaUrl, tsr);

        // validate communication level attributes (RFC 3161 PKIStatus)
        tsr.validate(new TimeStampRequest(request));

        TimeStampToken token = tsr.getTimeStampToken();
            
        TimestampResponseDto response = new TimestampResponseDto();
        response.setTime(getSigningTime(token.getSignedAttributes()));
        response.setEncodedToken(Base64.getEncoder().encodeToString(token.getEncoded()));
           
        return response;
    } catch (RestClientException | TSPException | CMSException | OperatorCreationException | GeneralSecurityException e) {
        throw new IOException(e);
    }
}

It prepares the request by creating the message imprint. Note that you are passing the hash itself, but also the hashing algorithm used to make the hash. Why isn’t the API hiding that from you, I don’t know. In my case the hash is obtained in a more complicated way, so it’s useful, but still. Then we get the raw form of the request and send it to the TSA (time stamping authority). It is an HTTP request, sort of simple, but you have to take care of some request and response headers that are not necessarily consistent across TSAs. The username and password are optional, some TSAs offer the service (rate-limited) without authentication.

When you have the raw response back, you parse it to a TimeStampResponse. Again, you have to go through 2 intermediate objects (ASN1StreamParser and TimeStampResp), which may be a proper abstraction, but is not a usable API.

Then you check if the response was successful, and you also have to validate it – the TSA may have returned a bad response. Ideally all of that could’ve been hidden from you. Validation throws an exception, which in this case I just propagate by wrapping in an IOException.

Finally, you get the token and return the response. The most important thing is the content of the token, which in my case was needed as Base64, so I encode it. It could just be the raw bytes as well. If you want to get any additional data from the token (e.g. the signing time), it’s not that simple; you have to parse the low-level attributes (seen in the gist).

Okay, you have the token now, and you can store it in a database. Occasionally you may want to validate whether timestamps have not been tampered with (which is my usecase). The code is here, and I won’t even try to explain it – it’s a ton of boilerplate that is also accounting for variations in the way TSAs respond (I’ve tried a few). The fact that a DummyCertificate class is needed either means I got something very wrong, or confirms my critique for the BouncyCastle APIs. The DummyCertificate may not be needed for some TSAs, but it is for others, and you actually can’t instantiate it that easily. You need a real certificate to construct it (which is not included in the gist; using the init() method in the next gist you can create the dummy with dummyCertificate = new DummyCertificate(certificateHolder.toASN1Structure());). In my code these are all one class, but for presenting them I decided to split it, hence this little duplication.

Okay, now we can timestamp and validate timestamps. That should be enough; but for testing purposes (or limited internal use) you may want to do the timestamping locally instead of asking a TSA. The code can be found here. It uses spring, but you can instead pass the keystore details as arguments to the init method. You need a JKS store with a keypair and a certificate, and I used KeyStore Explorer to create them. If you are running your application in AWS, you may want to encrypt your keystore using KMS (Key Management Service), and then decrypt it on application load, but that’s out of the scope of this article. For the local timestamping validation works as expected, and for timestamping – instead of calling the external service, just call localTSA.timestamp(req);

How did I get to know which classes to instantiate and which parameters to pass – I don’t remember. Looking at tests, examples, answers, sources. It took a while, and so I’m sharing it, to potentially save some trouble of others.

A list of TSAs you can test with: SafeCreative, FreeTSA, time.centum.pl.

I realize this does not seem applicable to many scenarios, but I would recommend timestamping some critical pieces of your application data. And it is generally useful to have it in your “toolbox”, ready to use, rather than trying to read the standard and battling with BouncyCastle classes for days in order to achieve this allegedly simple task.

The post Using Trusted Timestamping With Java appeared first on Bozho's tech blog.

How to Enhance the Security of Sensitive Customer Data by Using Amazon CloudFront Field-Level Encryption

Post Syndicated from Alex Tomic original https://aws.amazon.com/blogs/security/how-to-enhance-the-security-of-sensitive-customer-data-by-using-amazon-cloudfront-field-level-encryption/

Amazon CloudFront is a web service that speeds up distribution of your static and dynamic web content to end users through a worldwide network of edge locations. CloudFront provides a number of benefits and capabilities that can help you secure your applications and content while meeting compliance requirements. For example, you can configure CloudFront to help enforce secure, end-to-end connections using HTTPS SSL/TLS encryption. You also can take advantage of CloudFront integration with AWS Shield for DDoS protection and with AWS WAF (a web application firewall) for protection against application-layer attacks, such as SQL injection and cross-site scripting.

Now, CloudFront field-level encryption helps secure sensitive data such as a customer phone numbers by adding another security layer to CloudFront HTTPS. Using this functionality, you can help ensure that sensitive information in a POST request is encrypted at CloudFront edge locations. This information remains encrypted as it flows to and beyond your origin servers that terminate HTTPS connections with CloudFront and throughout the application environment. In this blog post, we demonstrate how you can enhance the security of sensitive data by using CloudFront field-level encryption.

Note: This post assumes that you understand concepts and services such as content delivery networks, HTTP forms, public-key cryptography, CloudFrontAWS Lambda, and the AWS CLI. If necessary, you should familiarize yourself with these concepts and review the solution overview in the next section before proceeding with the deployment of this post’s solution.

How field-level encryption works

Many web applications collect and store data from users as those users interact with the applications. For example, a travel-booking website may ask for your passport number and less sensitive data such as your food preferences. This data is transmitted to web servers and also might travel among a number of services to perform tasks. However, this also means that your sensitive information may need to be accessed by only a small subset of these services (most other services do not need to access your data).

User data is often stored in a database for retrieval at a later time. One approach to protecting stored sensitive data is to configure and code each service to protect that sensitive data. For example, you can develop safeguards in logging functionality to ensure sensitive data is masked or removed. However, this can add complexity to your code base and limit performance.

Field-level encryption addresses this problem by ensuring sensitive data is encrypted at CloudFront edge locations. Sensitive data fields in HTTPS form POSTs are automatically encrypted with a user-provided public RSA key. After the data is encrypted, other systems in your architecture see only ciphertext. If this ciphertext unintentionally becomes externally available, the data is cryptographically protected and only designated systems with access to the private RSA key can decrypt the sensitive data.

It is critical to secure private RSA key material to prevent unauthorized access to the protected data. Management of cryptographic key material is a larger topic that is out of scope for this blog post, but should be carefully considered when implementing encryption in your applications. For example, in this blog post we store private key material as a secure string in the Amazon EC2 Systems Manager Parameter Store. The Parameter Store provides a centralized location for managing your configuration data such as plaintext data (such as database strings) or secrets (such as passwords) that are encrypted using AWS Key Management Service (AWS KMS). You may have an existing key management system in place that you can use, or you can use AWS CloudHSM. CloudHSM is a cloud-based hardware security module (HSM) that enables you to easily generate and use your own encryption keys in the AWS Cloud.

To illustrate field-level encryption, let’s look at a simple form submission where Name and Phone values are sent to a web server using an HTTP POST. A typical form POST would contain data such as the following.

POST / HTTP/1.1
Host: example.com
Content-Type: application/x-www-form-urlencoded
Content-Length:60

Name=Jane+Doe&Phone=404-555-0150

Instead of taking this typical approach, field-level encryption converts this data similar to the following.

POST / HTTP/1.1
Host: example.com
Content-Type: application/x-www-form-urlencoded
Content-Length: 1713

Name=Jane+Doe&Phone=AYABeHxZ0ZqWyysqxrB5pEBSYw4AAA...

To further demonstrate field-level encryption in action, this blog post includes a sample serverless application that you can deploy by using a CloudFormation template, which creates an application environment using CloudFront, Amazon API Gateway, and Lambda. The sample application is only intended to demonstrate field-level encryption functionality and is not intended for production use. The following diagram depicts the architecture and data flow of this sample application.

Sample application architecture and data flow

Diagram of the solution's architecture and data flow

Here is how the sample solution works:

  1. An application user submits an HTML form page with sensitive data, generating an HTTPS POST to CloudFront.
  2. Field-level encryption intercepts the form POST and encrypts sensitive data with the public RSA key and replaces fields in the form post with encrypted ciphertext. The form POST ciphertext is then sent to origin servers.
  3. The serverless application accepts the form post data containing ciphertext where sensitive data would normally be. If a malicious user were able to compromise your application and gain access to your data, such as the contents of a form, that user would see encrypted data.
  4. Lambda stores data in a DynamoDB table, leaving sensitive data to remain safely encrypted at rest.
  5. An administrator uses the AWS Management Console and a Lambda function to view the sensitive data.
  6. During the session, the administrator retrieves ciphertext from the DynamoDB table.
  7. The administrator decrypts sensitive data by using private key material stored in the EC2 Systems Manager Parameter Store.
  8. Decrypted sensitive data is transmitted over SSL/TLS via the AWS Management Console to the administrator for review.

Deployment walkthrough

The high-level steps to deploy this solution are as follows:

  1. Stage the required artifacts
    When deployment packages are used with Lambda, the zipped artifacts have to be placed in an S3 bucket in the target AWS Region for deployment. This step is not required if you are deploying in the US East (N. Virginia) Region because the package has already been staged there.
  2. Generate an RSA key pair
    Create a public/private key pair that will be used to perform the encrypt/decrypt functionality.
  3. Upload the public key to CloudFront and associate it with the field-level encryption configuration
    After you create the key pair, the public key is uploaded to CloudFront so that it can be used by field-level encryption.
  4. Launch the CloudFormation stack
    Deploy the sample application for demonstrating field-level encryption by using AWS CloudFormation.
  5. Add the field-level encryption configuration to the CloudFront distribution
    After you have provisioned the application, this step associates the field-level encryption configuration with the CloudFront distribution.
  6. Store the RSA private key in the Parameter Store
    Store the private key in the Parameter Store as a SecureString data type, which uses AWS KMS to encrypt the parameter value.

Deploy the solution

1. Stage the required artifacts

(If you are deploying in the US East [N. Virginia] Region, skip to Step 2, “Generate an RSA key pair.”)

Stage the Lambda function deployment package in an Amazon S3 bucket located in the AWS Region you are using for this solution. To do this, download the zipped deployment package and upload it to your in-region bucket. For additional information about uploading objects to S3, see Uploading Object into Amazon S3.

2. Generate an RSA key pair

In this section, you will generate an RSA key pair by using OpenSSL:

  1. Confirm access to OpenSSL.
    $ openssl version

    You should see version information similar to the following.

    OpenSSL <version> <date>

  1. Create a private key using the following command.
    $ openssl genrsa -out private_key.pem 2048

    The command results should look similar to the following.

    Generating RSA private key, 2048 bit long modulus
    ................................................................................+++
    ..........................+++
    e is 65537 (0x10001)
  1. Extract the public key from the private key by running the following command.
    $ openssl rsa -pubout -in private_key.pem -out public_key.pem

    You should see output similar to the following.

    writing RSA key
  1. Restrict access to the private key.$ chmod 600 private_key.pem Note: You will use the public and private key material in Steps 3 and 6 to configure the sample application.

3. Upload the public key to CloudFront and associate it with the field-level encryption configuration

Now that you have created the RSA key pair, you will use the AWS Management Console to upload the public key to CloudFront for use by field-level encryption. Complete the following steps to upload and configure the public key.

Note: Do not include spaces or special characters when providing the configuration values in this section.

  1. From the AWS Management Console, choose Services > CloudFront.
  2. In the navigation pane, choose Public Key and choose Add Public Key.
    Screenshot of adding a public key

Complete the Add Public Key configuration boxes:

  • Key Name: Type a name such as DemoPublicKey.
  • Encoded Key: Paste the contents of the public_key.pem file you created in Step 2c. Copy and paste the encoded key value for your public key, including the -----BEGIN PUBLIC KEY----- and -----END PUBLIC KEY----- lines.
  • Comment: Optionally add a comment.
  1. Choose Create.
  2. After adding at least one public key to CloudFront, the next step is to create a profile to tell CloudFront which fields of input you want to be encrypted. While still on the CloudFront console, choose Field-level encryption in the navigation pane.
  3. Under Profiles, choose Create profile.
    Screenshot of creating a profile

Complete the Create profile configuration boxes:

  • Name: Type a name such as FLEDemo.
  • Comment: Optionally add a comment.
  • Public key: Select the public key you configured in Step 4.b.
  • Provider name: Type a provider name such as FLEDemo.
    This information will be used when the form data is encrypted, and must be provided to applications that need to decrypt the data, along with the appropriate private key.
  • Pattern to match: Type phone. This configures field-level encryption to match based on the phone.
  1. Choose Save profile.
  2. Configurations include options for whether to block or forward a query to your origin in scenarios where CloudFront can’t encrypt the data. Under Encryption Configurations, choose Create configuration.
    Screenshot of creating a configuration

Complete the Create configuration boxes:

  • Comment: Optionally add a comment.
  • Content type: Enter application/x-www-form-urlencoded. This is a common media type for encoding form data.
  • Default profile ID: Select the profile you added in Step 3e.
  1. Choose Save configuration

4. Launch the CloudFormation stack

Launch the sample application by using a CloudFormation template that automates the provisioning process.

Input parameterInput parameter description
ProviderIDEnter the Provider name you assigned in Step 3e. The ProviderID is used in field-level encryption configuration in CloudFront (letters and numbers only, no special characters)
PublicKeyNameEnter the Key Name you assigned in Step 3b. This name is assigned to the public key in field-level encryption configuration in CloudFront (letters and numbers only, no special characters).
PrivateKeySSMPathLeave as the default: /cloudfront/field-encryption-sample/private-key
ArtifactsBucketThe S3 bucket with artifact files (staged zip file with app code). Leave as default if deploying in us-east-1.
ArtifactsPrefixThe path in the S3 bucket containing artifact files. Leave as default if deploying in us-east-1.

To finish creating the CloudFormation stack:

  1. Choose Next on the Select Template page, enter the input parameters and choose Next.
    Note: The Artifacts configuration needs to be updated only if you are deploying outside of us-east-1 (US East [N. Virginia]). See Step 1 for artifact staging instructions.
  2. On the Options page, accept the defaults and choose Next.
  3. On the Review page, confirm the details, choose the I acknowledge that AWS CloudFormation might create IAM resources check box, and then choose Create. (The stack will be created in approximately 15 minutes.)

5. Add the field-level encryption configuration to the CloudFront distribution

While still on the CloudFront console, choose Distributions in the navigation pane, and then:

    1. In the Outputs section of the FLE-Sample-App stack, look for CloudFrontDistribution and click the URL to open the CloudFront console.
    2. Choose Behaviors, choose the Default (*) behavior, and then choose Edit.
    3. For Field-level Encryption Config, choose the configuration you created in Step 3g.
      Screenshot of editing the default cache behavior
    4. Choose Yes, Edit.
    5. While still in the CloudFront distribution configuration, choose the General Choose Edit, scroll down to Distribution State, and change it to Enabled.
    6. Choose Yes, Edit.

6. Store the RSA private key in the Parameter Store

In this step, you store the private key in the EC2 Systems Manager Parameter Store as a SecureString data type, which uses AWS KMS to encrypt the parameter value. For more information about AWS KMS, see the AWS Key Management Service Developer Guide. You will need a working installation of the AWS CLI to complete this step.

  1. Store the private key in the Parameter Store with the AWS CLI by running the following command. You will find the <KMSKeyID> in the KMSKeyID in the CloudFormation stack Outputs. Substitute it for the placeholder in the following command.
    $ aws ssm put-parameter --type "SecureString" --name /cloudfront/field-encryption-sample/private-key --value file://private_key.pem --key-id "<KMSKeyID>"
    
    ------------------
    |  PutParameter  |
    +----------+-----+
    |  Version |  1  |
    +----------+-----+

  1. Verify the parameter. Your private key material should be accessible through the ssm get-parameter in the following command in the Value The key material has been truncated in the following output.
    $ aws ssm get-parameter --name /cloudfront/field-encryption-sample/private-key --with-decryption
    
    -----…
    
    ||  Value  |  -----BEGIN RSA PRIVATE KEY-----
    MIIEowIBAAKCAQEAwGRBGuhacmw+C73kM6Z…….

    Notice we use the —with decryption argument in this command. This returns the private key as cleartext.

    This completes the sample application deployment. Next, we show you how to see field-level encryption in action.

  1. Delete the private key from local storage. On Linux for example, using the shred command, securely delete the private key material from your workstation as shown below. You may also wish to store the private key material within an AWS CloudHSM or other protected location suitable for your security requirements. For production implementations, you also should implement key rotation policies.
    $ shred -zvu -n  100 private*.pem
    
    shred: private_encrypted_key.pem: pass 1/101 (random)...
    shred: private_encrypted_key.pem: pass 2/101 (dddddd)...
    shred: private_encrypted_key.pem: pass 3/101 (555555)...
    ….

Test the sample application

Use the following steps to test the sample application with field-level encryption:

  1. Open sample application in your web browser by clicking the ApplicationURL link in the CloudFormation stack Outputs. (for example, https:d199xe5izz82ea.cloudfront.net/prod/). Note that it may take several minutes for the CloudFront distribution to reach the Deployed Status from the previous step, during which time you may not be able to access the sample application.
  2. Fill out and submit the HTML form on the page:
    1. Complete the three form fields: Full Name, Email Address, and Phone Number.
    2. Choose Submit.
      Screenshot of completing the sample application form
      Notice that the application response includes the form values. The phone number returns the following ciphertext encryption using your public key. This ciphertext has been stored in DynamoDB.
      Screenshot of the phone number as ciphertext
  3. Execute the Lambda decryption function to download ciphertext from DynamoDB and decrypt the phone number using the private key:
    1. In the CloudFormation stack Outputs, locate DecryptFunction and click the URL to open the Lambda console.
    2. Configure a test event using the “Hello World” template.
    3. Choose the Test button.
  4. View the encrypted and decrypted phone number data.
    Screenshot of the encrypted and decrypted phone number data

Summary

In this blog post, we showed you how to use CloudFront field-level encryption to encrypt sensitive data at edge locations and help prevent access from unauthorized systems. The source code for this solution is available on GitHub. For additional information about field-level encryption, see the documentation.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, please start a new thread on the CloudFront forum.

– Alex and Cameron

Introducing the New GDPR Center and “Navigating GDPR Compliance on AWS” Whitepaper

Post Syndicated from Chad Woolf original https://aws.amazon.com/blogs/security/introducing-the-new-gdpr-center-and-navigating-gdpr-compliance-on-aws-whitepaper/

European Union flag

At AWS re:Invent 2017, the AWS Compliance team participated in excellent engagements with AWS customers about the General Data Protection Regulation (GDPR), including discussions that generated helpful input. Today, I am announcing resulting enhancements to our recently launched GDPR Center and the release of a new whitepaper, Navigating GDPR Compliance on AWS. The resources available on the GDPR Center are designed to give you GDPR basics, and provide some ideas as you work out the details of the regulation and find a path to compliance.

In this post, I focus on two of these new GDPR requirements in terms of articles in the GDPR, and explain some of the AWS services and other resources that can help you meet these requirements.

Background about the GDPR

The GDPR is a European privacy law that will become enforceable on May 25, 2018, and is intended to harmonize data protection laws throughout the European Union (EU) by applying a single data protection law that is binding throughout each EU member state. The GDPR not only applies to organizations located within the EU, but also to organizations located outside the EU if they offer goods or services to, or monitor the behavior of, EU data subjects. All AWS services will comply with the GDPR in advance of the May 25, 2018, enforcement date.

We are already seeing customers move personal data to AWS to help solve challenges in complying with the EU’s GDPR because of AWS’s advanced toolset for identifying, securing, and managing all types of data, including personal data. Steve Schmidt, the AWS CISO, has already written about the internal and external work we have been undertaking to help you use AWS services to meet your own GDPR compliance goals.

Article 25 – Data Protection by Design and by Default (Privacy by Design)

Privacy by Design is the integration of data privacy and compliance into the systems development process, enabling applications, systems, and accounts, among other things, to be secure by default. To secure your AWS account, we offer a script to evaluate your AWS account against the full Center for Internet Security (CIS) Amazon Web Services Foundations Benchmark 1.1. You can access this public benchmark on GitHub. Additionally, AWS Trusted Advisor is an online resource to help you improve security by optimizing your AWS environment. Among other things, Trusted Advisor lists a number of security-related controls you should be monitoring. AWS also offers AWS CloudTrail, a logging tool to track usage and API activity. Another example of tooling that enables data protection is Amazon Inspector, which includes a knowledge base of hundreds of rules (regularly updated by AWS security researchers) mapped to common security best practices and vulnerability definitions. Examples of built-in rules include checking for remote root login being enabled or vulnerable software versions installed. These and other tools enable you to design an environment that protects customer data by design.

An accurate inventory of all the GDPR-impacting data is important but sometimes difficult to assess. AWS has some advanced tooling, such as Amazon Macie, to help you determine where customer data is present in your AWS resources. Macie uses advanced machine learning to automatically discover and classify data so that you can protect data, per Article 25.

Article 32 – Security of Processing

You can use many AWS services and features to secure the processing of data regulated by the GDPR. Amazon Virtual Private Cloud (Amazon VPC) lets you provision a logically isolated section of the AWS Cloud where you can launch resources in a virtual network that you define. You have complete control over your virtual networking environment, including the selection of your own IP address range, creation of subnets, and configuration of route tables and network gateways. With Amazon VPC, you can make the Amazon Cloud a seamless extension of your existing on-premises resources.

AWS Key Management Service (AWS KMS) is a managed service that makes it easy for you to create and control the encryption keys used to encrypt your data, and uses hardware security modules (HSMs) to help protect your keys. Managing keys with AWS KMS allows you to choose to encrypt data either on the server side or the client side. AWS KMS is integrated with several other AWS services to help you protect the data you store with these services. AWS KMS is also integrated with CloudTrail to provide you with logs of all key usage to help meet your regulatory and compliance needs. You can also use the AWS Encryption SDK to correctly generate and use encryption keys, as well as protect keys after they have been used.

We also recently announced new encryption and security features for Amazon S3, including default encryption and a detailed inventory report. Services of this type as well as additional GDPR enablers will be published regularly on our GDPR Center.

Other resources

As you prepare for GDPR, you may want to visit our AWS Customer Compliance Center or Tools for Amazon Web Services to learn about options for building anything from small scripts that delete data to a full orchestration framework that uses AWS Code services.

-Chad

How to Manage Amazon GuardDuty Security Findings Across Multiple Accounts

Post Syndicated from Tom Stickle original https://aws.amazon.com/blogs/security/how-to-manage-amazon-guardduty-security-findings-across-multiple-accounts/

Introduced at AWS re:Invent 2017, Amazon GuardDuty is a managed threat detection service that continuously monitors for malicious or unauthorized behavior to help you protect your AWS accounts and workloads. In an AWS Blog post, Jeff Barr shows you how to enable GuardDuty to monitor your AWS resources continuously. That blog post shows how to get started with a single GuardDuty account and provides an overview of the features of the service. Your security team, though, will probably want to use GuardDuty to monitor a group of AWS accounts continuously.

In this post, I demonstrate how to use GuardDuty to monitor a group of AWS accounts and have their findings routed to another AWS account—the master account—that is owned by a security team. The method I demonstrate in this post is especially useful if your security team is responsible for monitoring a group of AWS accounts over which it does not have direct access—known as member accounts. In this solution, I simplify the work needed to enable GuardDuty in member accounts and configure findings by simplifying the process, which I do by enabling GuardDuty in the master account and inviting member accounts.

Enable GuardDuty in a master account and invite member accounts

To get started, you must enable GuardDuty in the master account, which will receive GuardDuty findings. The master account should be managed by your security team, and it will display the findings from all member accounts. The master account can be reverted later by removing any member accounts you add to it. Adding member accounts is a two-way handshake mechanism to ensure that administrators from both the master and member accounts formally agree to establish the relationship.

To enable GuardDuty in the master account and add member accounts:

  1. Navigate to the GuardDuty console.
  2. In the navigation pane, choose Accounts.
    Screenshot of the Accounts choice in the navigation pane
  1. To designate this account as the GuardDuty master account, start adding member accounts:
    • You can add individual accounts by choosing Add Account, or you can add a list of accounts by choosing Upload List (.csv).
  1. Now, add the account ID and email address of the member account, and choose Add. (If you are uploading a list of accounts, choose Browse, choose the .csv file with the member accounts [one email address and account ID per line], and choose Add accounts.)
    Screenshot of adding an account

For security reasons, AWS checks to make sure each account ID is valid and that you’ve entered each member account’s email address that was used to create the account. If a member account’s account ID and email address do not match, GuardDuty does not send an invitation.
Screenshot showing the Status of Invite

  1. After you add all the member accounts you want to add, you will see them listed in the Member accounts table with a Status of Invite. You don’t have to individually invite each account—you can choose a group of accounts and when you choose to invite one account in the group, all accounts are invited.
  2. When you choose Invite for each member account:
    1. AWS checks to make sure the account ID is valid and the email address provided is the email address of the member account.
    2. AWS sends an email to the member account email address with a link to the GuardDuty console, where the member account owner can accept the invitation. You can add a customized message from your security team. Account owners who receive the invitation must sign in to their AWS account to accept the invitation. The service also sends an invitation through the AWS Personal Health Dashboard in case the member email address is not monitored. This invitation appears in the member account under the AWS Personal Health Dashboard alert bell on the AWS Management Console.
    3. A pending-invitation indicator is shown on the GuardDuty console of the member account, as shown in the following screenshot.
      Screenshot showing the pending-invitation indicator

When the invitation is sent by email, it is sent to the account owner of the GuardDuty member account.
Screenshot of the invitation sent by email

The account owner can click the link in the email invitation or the AWS Personal Health Dashboard message, or the account owner can sign in to their account and navigate to the GuardDuty console. In all cases, the member account displays the pending invitation in the member account’s GuardDuty console with instructions for accepting the invitation. The GuardDuty console walks the account owner through accepting the invitation, including enabling GuardDuty if it is not already enabled.

If you prefer to work in the AWS CLI, you can enable GuardDuty and accept the invitation. To do this, call CreateDetector to enable GuardDuty, and then call AcceptInvitation, which serves the same purpose as accepting the invitation in the GuardDuty console.

  1. After the member account owner accepts the invitation, the Status in the master account is changed to Monitored. The status helps you track the status of each AWS account that you invite.
    Screenshot showing the Status change to Monitored

You have enabled GuardDuty on the member account, and all findings will be forwarded to the master account. You can now monitor the findings about GuardDuty member accounts from the GuardDuty console in the master account.

The member account owner can see GuardDuty findings by default and can control all aspects of the experience in the member account with AWS Identity and Access Management (IAM) permissions. Users with the appropriate permissions can end the multi-account relationship at any time by toggling the Accept button on the Accounts page. Note that ending the relationship changes the Status of the account to Resigned and also triggers a security finding on the side of the master account so that the security team knows the member account is no longer linked to the master account.

Working with GuardDuty findings

Most security teams have ticketing systems, chat operations, security information event management (SIEM) systems, or other security automation systems to which they would like to push GuardDuty findings. For this purpose, GuardDuty sends all findings as JSON-based messages through Amazon CloudWatch Events, a scalable service to which you can subscribe and to which AWS services can stream system events. To access these events, navigate to the CloudWatch Events console and create a rule that subscribes to the GuardDuty-related findings. You then can assign a target such as Amazon Kinesis Data Firehose that can place the findings in a number of services such as Amazon S3. The following screenshot is of the CloudWatch Events console, where I have a rule that pulls all events from GuardDuty and pushes them to a preconfigured AWS Lambda function.

Screenshot of a CloudWatch Events rule

The following example is a subset of GuardDuty findings that includes relevant context and information about the nature of a threat that was detected. In this example, the instanceId, i-00bb62b69b7004a4c, is performing Secure Shell (SSH) brute-force attacks against IP address 172.16.0.28. From a Lambda function, you can access any of the following fields such as the title of the finding and its description, and send those directly to your ticketing system.

Example GuardDuty findings

You can use other AWS services to build custom analytics and visualizations of your security findings. For example, you can connect Kinesis Data Firehose to CloudWatch Events and write events to an S3 bucket in a standard format, which can be encrypted with AWS Key Management Service and then compressed. You also can use Amazon QuickSight to build ad hoc dashboards by using AWS Glue and Amazon Athena. Similarly, you can place the data from Kinesis Data Firehose in Amazon Elasticsearch Service, with which you can use tools such as Kibana to build your own visualizations and dashboards.

Like most other AWS services, GuardDuty is a regional service. This means that when you enable GuardDuty in an AWS Region, all findings are generated and delivered in that region. If you are regulated by a compliance regime, this is often an important requirement to ensure that security findings remain in a specific jurisdiction. Because customers have let us know they would prefer to be able to enable GuardDuty globally and have all findings aggregated in one place, we intend to give the choice of regional or global isolation as we evolve this new service.

Summary

In this blog post, I have demonstrated how to use GuardDuty to monitor a group of GuardDuty member accounts and aggregate security findings in a central master GuardDuty account. You can use this solution whether or not you have direct control over the member accounts.

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about using GuardDuty, start a thread in the GuardDuty forum or contact AWS Support.

-Tom

Glenn’s Take on re:Invent Part 2

Post Syndicated from Glenn Gore original https://aws.amazon.com/blogs/architecture/glenns-take-on-reinvent-part-2/

Glenn Gore here, Chief Architect for AWS. I’m in Las Vegas this week — with 43K others — for re:Invent 2017. We’ve got a lot of exciting announcements this week. I’m going to check in to the Architecture blog with my take on what’s interesting about some of the announcements from an cloud architectural perspective. My first post can be found here.

The Media and Entertainment industry has been a rapid adopter of AWS due to the scale, reliability, and low costs of our services. This has enabled customers to create new, online, digital experiences for their viewers ranging from broadcast to streaming to Over-the-Top (OTT) services that can be a combination of live, scheduled, or ad-hoc viewing, while supporting devices ranging from high-def TVs to mobile devices. Creating an end-to-end video service requires many different components often sourced from different vendors with different licensing models, which creates a complex architecture and a complex environment to support operationally.

AWS Media Services
Based on customer feedback, we have developed AWS Media Services to help simplify distribution of video content. AWS Media Services is comprised of five individual services that can either be used together to provide an end-to-end service or individually to work within existing deployments: AWS Elemental MediaConvert, AWS Elemental MediaLive, AWS Elemental MediaPackage, AWS Elemental MediaStore and AWS Elemental MediaTailor. These services can help you with everything from storing content safely and durably to setting up a live-streaming event in minutes without having to be concerned about the underlying infrastructure and scalability of the stream itself.

In my role, I participate in many AWS and industry events and often work with the production and event teams that put these shows together. With all the logistical tasks they have to deal with, the biggest question is often: “Will the live stream work?” Compounding this fear is the reality that, as users, we are also quick to jump on social media and make noise when a live stream drops while we are following along remotely. Worse is when I see event organizers actively selecting not to live stream content because of the risk of failure and and exposure — leading them to decide to take the safe option and not stream at all.

With AWS Media Services addressing many of the issues around putting together a high-quality media service, live streaming, and providing access to a library of content through a variety of mechanisms, I can’t wait to see more event teams use live streaming without the concern and worry I’ve seen in the past. I am excited for what this also means for non-media companies, as video becomes an increasingly common way of sharing information and adding a more personalized touch to internally- and externally-facing content.

AWS Media Services will allow you to focus more on the content and not worry about the platform. Awesome!

Amazon Neptune
As a civilization, we have been developing new ways to record and store information and model the relationships between sets of information for more than a thousand years. Government census data, tax records, births, deaths, and marriages were all recorded on medium ranging from knotted cords in the Inca civilization, clay tablets in ancient Babylon, to written texts in Western Europe during the late Middle Ages.

One of the first challenges of computing was figuring out how to store and work with vast amounts of information in a programmatic way, especially as the volume of information was increasing at a faster rate than ever before. We have seen different generations of how to organize this information in some form of database, ranging from flat files to the Information Management System (IMS) used in the 1960s for the Apollo space program, to the rise of the relational database management system (RDBMS) in the 1970s. These innovations drove a lot of subsequent innovations in information management and application development as we were able to move from thousands of records to millions and billions.

Today, as architects and developers, we have a vast variety of database technologies to select from, which have different characteristics that are optimized for different use cases:

  • Relational databases are well understood after decades of use in the majority of companies who required a database to store information. Amazon Relational Database (Amazon RDS) supports many popular relational database engines such as MySQL, Microsoft SQL Server, PostgreSQL, MariaDB, and Oracle. We have even brought the traditional RDBMS into the cloud world through Amazon Aurora, which provides MySQL and PostgreSQL support with the performance and reliability of commercial-grade databases at 1/10th the cost.
  • Non-relational databases (NoSQL) provided a simpler method of storing and retrieving information that was often faster and more scalable than traditional RDBMS technology. The concept of non-relational databases has existed since the 1960s but really took off in the early 2000s with the rise of web-based applications that required performance and scalability that relational databases struggled with at the time. AWS published this Dynamo whitepaper in 2007, with DynamoDB launching as a service in 2012. DynamoDB has quickly become one of the critical design elements for many of our customers who are building highly-scalable applications on AWS. We continue to innovate with DynamoDB, and this week launched global tables and on-demand backup at re:Invent 2017. DynamoDB excels in a variety of use cases, such as tracking of session information for popular websites, shopping cart information on e-commerce sites, and keeping track of gamers’ high scores in mobile gaming applications, for example.
  • Graph databases focus on the relationship between data items in the store. With a graph database, we work with nodes, edges, and properties to represent data, relationships, and information. Graph databases are designed to make it easy and fast to traverse and retrieve complex hierarchical data models. Graph databases share some concepts from the NoSQL family of databases such as key-value pairs (properties) and the use of a non-SQL query language such as Gremlin. Graph databases are commonly used for social networking, recommendation engines, fraud detection, and knowledge graphs. We released Amazon Neptune to help simplify the provisioning and management of graph databases as we believe that graph databases are going to enable the next generation of smart applications.

A common use case I am hearing every week as I talk to customers is how to incorporate chatbots within their organizations. Amazon Lex and Amazon Polly have made it easy for customers to experiment and build chatbots for a wide range of scenarios, but one of the missing pieces of the puzzle was how to model decision trees and and knowledge graphs so the chatbot could guide the conversation in an intelligent manner.

Graph databases are ideal for this particular use case, and having Amazon Neptune simplifies the deployment of a graph database while providing high performance, scalability, availability, and durability as a managed service. Security of your graph database is critical. To help ensure this, you can store your encrypted data by running AWS in Amazon Neptune within your Amazon Virtual Private Cloud (Amazon VPC) and using encryption at rest integrated with AWS Key Management Service (AWS KMS). Neptune also supports Amazon VPC and AWS Identity and Access Management (AWS IAM) to help further protect and restrict access.

Our customers now have the choice of many different database technologies to ensure that they can optimize each application and service for their specific needs. Just as DynamoDB has unlocked and enabled many new workloads that weren’t possible in relational databases, I can’t wait to see what new innovations and capabilities are enabled from graph databases as they become easier to use through Amazon Neptune.

Look for more on DynamoDB and Amazon S3 from me on Monday.

 

Glenn at Tour de Mont Blanc