Tag Archives: EC2 instance store encryption

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.

 

 

In Case You Missed These: AWS Security Blog Posts from January, February, and March

Post Syndicated from Craig Liebendorfer original https://aws.amazon.com/blogs/security/in-case-you-missed-these-aws-security-blog-posts-from-january-february-and-march/

Image of lock and key

In case you missed any AWS Security Blog posts published so far in 2017, they are summarized and linked to below. The posts are shown in reverse chronological order (most recent first), and the subject matter ranges from protecting dynamic web applications against DDoS attacks to monitoring AWS account configuration changes and API calls to Amazon EC2 security groups.

March

March 22: How to Help Protect Dynamic Web Applications Against DDoS Attacks by Using Amazon CloudFront and Amazon Route 53
Using a content delivery network (CDN) such as Amazon CloudFront to cache and serve static text and images or downloadable objects such as media files and documents is a common strategy to improve webpage load times, reduce network bandwidth costs, lessen the load on web servers, and mitigate distributed denial of service (DDoS) attacks. AWS WAF is a web application firewall that can be deployed on CloudFront to help protect your application against DDoS attacks by giving you control over which traffic to allow or block by defining security rules. When users access your application, the Domain Name System (DNS) translates human-readable domain names (for example, www.example.com) to machine-readable IP addresses (for example, 192.0.2.44). A DNS service, such as Amazon Route 53, can effectively connect users’ requests to a CloudFront distribution that proxies requests for dynamic content to the infrastructure hosting your application’s endpoints. In this blog post, I show you how to deploy CloudFront with AWS WAF and Route 53 to help protect dynamic web applications (with dynamic content such as a response to user input) against DDoS attacks. The steps shown in this post are key to implementing the overall approach described in AWS Best Practices for DDoS Resiliency and enable the built-in, managed DDoS protection service, AWS Shield.

March 21: New AWS Encryption SDK for Python Simplifies Multiple Master Key Encryption
The AWS Cryptography team is happy to announce a Python implementation of the AWS Encryption SDK. This new SDK helps manage data keys for you, and it simplifies the process of encrypting data under multiple master keys. As a result, this new SDK allows you to focus on the code that drives your business forward. It also provides a framework you can easily extend to ensure that you have a cryptographic library that is configured to match and enforce your standards. The SDK also includes ready-to-use examples. If you are a Java developer, you can refer to this blog post to see specific Java examples for the SDK. In this blog post, I show you how you can use the AWS Encryption SDK to simplify the process of encrypting data and how to protect your encryption keys in ways that help improve application availability by not tying you to a single region or key management solution.

March 21: Updated CJIS Workbook Now Available by Request
The need for guidance when implementing Criminal Justice Information Services (CJIS)–compliant solutions has become of paramount importance as more law enforcement customers and technology partners move to store and process criminal justice data in the cloud. AWS services allow these customers to easily and securely architect a CJIS-compliant solution when handling criminal justice data, creating a durable, cost-effective, and secure IT infrastructure that better supports local, state, and federal law enforcement in carrying out their public safety missions. AWS has created several documents (collectively referred to as the CJIS Workbook) to assist you in aligning with the FBI’s CJIS Security Policy. You can use the workbook as a framework for developing CJIS-compliant architecture in the AWS Cloud. The workbook helps you define and test the controls you operate, and document the dependence on the controls that AWS operates (compute, storage, database, networking, regions, Availability Zones, and edge locations).

March 9: New Cloud Directory API Makes It Easier to Query Data Along Multiple Dimensions
Today, we made available a new Cloud Directory API, ListObjectParentPaths, that enables you to retrieve all available parent paths for any directory object across multiple hierarchies. Use this API when you want to fetch all parent objects for a specific child object. The order of the paths and objects returned is consistent across iterative calls to the API, unless objects are moved or deleted. In case an object has multiple parents, the API allows you to control the number of paths returned by using a paginated call pattern. In this blog post, I use an example directory to demonstrate how this new API enables you to retrieve data across multiple dimensions to implement powerful applications quickly.

March 8: How to Access the AWS Management Console Using AWS Microsoft AD and Your On-Premises Credentials
AWS Directory Service for Microsoft Active Directory, also known as AWS Microsoft AD, is a managed Microsoft Active Directory (AD) hosted in the AWS Cloud. Now, AWS Microsoft AD makes it easy for you to give your users permission to manage AWS resources by using on-premises AD administrative tools. With AWS Microsoft AD, you can grant your on-premises users permissions to resources such as the AWS Management Console instead of adding AWS Identity and Access Management (IAM) user accounts or configuring AD Federation Services (AD FS) with Security Assertion Markup Language (SAML). In this blog post, I show how to use AWS Microsoft AD to enable your on-premises AD users to sign in to the AWS Management Console with their on-premises AD user credentials to access and manage AWS resources through IAM roles.

March 7: How to Protect Your Web Application Against DDoS Attacks by Using Amazon Route 53 and an External Content Delivery Network
Distributed Denial of Service (DDoS) attacks are attempts by a malicious actor to flood a network, system, or application with more traffic, connections, or requests than it is able to handle. To protect your web application against DDoS attacks, you can use AWS Shield, a DDoS protection service that AWS provides automatically to all AWS customers at no additional charge. You can use AWS Shield in conjunction with DDoS-resilient web services such as Amazon CloudFront and Amazon Route 53 to improve your ability to defend against DDoS attacks. Learn more about architecting for DDoS resiliency by reading the AWS Best Practices for DDoS Resiliency whitepaper. You also have the option of using Route 53 with an externally hosted content delivery network (CDN). In this blog post, I show how you can help protect the zone apex (also known as the root domain) of your web application by using Route 53 to perform a secure redirect to prevent discovery of your application origin.

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February

February 27: Now Generally Available – AWS Organizations: Policy-Based Management for Multiple AWS Accounts
Today, AWS Organizations moves from Preview to General Availability. You can use Organizations to centrally manage multiple AWS accounts, with the ability to create a hierarchy of organizational units (OUs). You can assign each account to an OU, define policies, and then apply those policies to an entire hierarchy, specific OUs, or specific accounts. You can invite existing AWS accounts to join your organization, and you can also create new accounts. All of these functions are available from the AWS Management Console, the AWS Command Line Interface (CLI), and through the AWS Organizations API.To read the full AWS Blog post about today’s launch, see AWS Organizations – Policy-Based Management for Multiple AWS Accounts.

February 23: s2n Is Now Handling 100 Percent of SSL Traffic for Amazon S3
Today, we’ve achieved another important milestone for securing customer data: we have replaced OpenSSL with s2n for all internal and external SSL traffic in Amazon Simple Storage Service (Amazon S3) commercial regions. This was implemented with minimal impact to customers, and multiple means of error checking were used to ensure a smooth transition, including client integration tests, catching potential interoperability conflicts, and identifying memory leaks through fuzz testing.

February 22: Easily Replace or Attach an IAM Role to an Existing EC2 Instance by Using the EC2 Console
AWS Identity and Access Management (IAM) roles enable your applications running on Amazon EC2 to use temporary security credentials. IAM roles for EC2 make it easier for your applications to make API requests securely from an instance because they do not require you to manage AWS security credentials that the applications use. Recently, we enabled you to use temporary security credentials for your applications by attaching an IAM role to an existing EC2 instance by using the AWS CLI and SDK. To learn more, see New! Attach an AWS IAM Role to an Existing Amazon EC2 Instance by Using the AWS CLI. Starting today, you can attach an IAM role to an existing EC2 instance from the EC2 console. You can also use the EC2 console to replace an IAM role attached to an existing instance. In this blog post, I will show how to attach an IAM role to an existing EC2 instance from the EC2 console.

February 22: How to Audit Your AWS Resources for Security Compliance by Using Custom AWS Config Rules
AWS Config Rules enables you to implement security policies as code for your organization and evaluate configuration changes to AWS resources against these policies. You can use Config rules to audit your use of AWS resources for compliance with external compliance frameworks such as CIS AWS Foundations Benchmark and with your internal security policies related to the US Health Insurance Portability and Accountability Act (HIPAA), the Federal Risk and Authorization Management Program (FedRAMP), and other regimes. AWS provides some predefined, managed Config rules. You also can create custom Config rules based on criteria you define within an AWS Lambda function. In this post, I show how to create a custom rule that audits AWS resources for security compliance by enabling VPC Flow Logs for an Amazon Virtual Private Cloud (VPC). The custom rule meets requirement 4.3 of the CIS AWS Foundations Benchmark: “Ensure VPC flow logging is enabled in all VPCs.”

February 13: AWS Announces CISPE Membership and Compliance with First-Ever Code of Conduct for Data Protection in the Cloud
I have two exciting announcements today, both showing AWS’s continued commitment to ensuring that customers can comply with EU Data Protection requirements when using our services.

February 13: How to Enable Multi-Factor Authentication for AWS Services by Using AWS Microsoft AD and On-Premises Credentials
You can now enable multi-factor authentication (MFA) for users of AWS services such as Amazon WorkSpaces and Amazon QuickSight and their on-premises credentials by using your AWS Directory Service for Microsoft Active Directory (Enterprise Edition) directory, also known as AWS Microsoft AD. MFA adds an extra layer of protection to a user name and password (the first “factor”) by requiring users to enter an authentication code (the second factor), which has been provided by your virtual or hardware MFA solution. These factors together provide additional security by preventing access to AWS services, unless users supply a valid MFA code.

February 13: How to Create an Organizational Chart with Separate Hierarchies by Using Amazon Cloud Directory
Amazon Cloud Directory enables you to create directories for a variety of use cases, such as organizational charts, course catalogs, and device registries. Cloud Directory offers you the flexibility to create directories with hierarchies that span multiple dimensions. For example, you can create an organizational chart that you can navigate through separate hierarchies for reporting structure, location, and cost center. In this blog post, I show how to use Cloud Directory APIs to create an organizational chart with two separate hierarchies in a single directory. I also show how to navigate the hierarchies and retrieve data. I use the Java SDK for all the sample code in this post, but you can use other language SDKs or the AWS CLI.

February 10: How to Easily Log On to AWS Services by Using Your On-Premises Active Directory
AWS Directory Service for Microsoft Active Directory (Enterprise Edition), also known as Microsoft AD, now enables your users to log on with just their on-premises Active Directory (AD) user name—no domain name is required. This new domainless logon feature makes it easier to set up connections to your on-premises AD for use with applications such as Amazon WorkSpaces and Amazon QuickSight, and it keeps the user logon experience free from network naming. This new interforest trusts capability is now available when using Microsoft AD with Amazon WorkSpaces and Amazon QuickSight Enterprise Edition. In this blog post, I explain how Microsoft AD domainless logon works with AD interforest trusts, and I show an example of setting up Amazon WorkSpaces to use this capability.

February 9: New! Attach an AWS IAM Role to an Existing Amazon EC2 Instance by Using the AWS CLI
AWS Identity and Access Management (IAM) roles enable your applications running on Amazon EC2 to use temporary security credentials that AWS creates, distributes, and rotates automatically. Using temporary credentials is an IAM best practice because you do not need to maintain long-term keys on your instance. Using IAM roles for EC2 also eliminates the need to use long-term AWS access keys that you have to manage manually or programmatically. Starting today, you can enable your applications to use temporary security credentials provided by AWS by attaching an IAM role to an existing EC2 instance. You can also replace the IAM role attached to an existing EC2 instance. In this blog post, I show how you can attach an IAM role to an existing EC2 instance by using the AWS CLI.

February 8: How to Remediate Amazon Inspector Security Findings Automatically
The Amazon Inspector security assessment service can evaluate the operating environments and applications you have deployed on AWS for common and emerging security vulnerabilities automatically. As an AWS-built service, Amazon Inspector is designed to exchange data and interact with other core AWS services not only to identify potential security findings but also to automate addressing those findings. Previous related blog posts showed how you can deliver Amazon Inspector security findings automatically to third-party ticketing systems and automate the installation of the Amazon Inspector agent on new Amazon EC2 instances. In this post, I show how you can automatically remediate findings generated by Amazon Inspector. To get started, you must first run an assessment and publish any security findings to an Amazon Simple Notification Service (SNS) topic. Then, you create an AWS Lambda function that is triggered by those notifications. Finally, the Lambda function examines the findings and then implements the appropriate remediation based on the type of issue.

February 6: How to Simplify Security Assessment Setup Using Amazon EC2 Systems Manager and Amazon Inspector
In a July 2016 AWS Blog post, I discussed how to integrate Amazon Inspector with third-party ticketing systems by using Amazon Simple Notification Service (SNS) and AWS Lambda. This AWS Security Blog post continues in the same vein, describing how to use Amazon Inspector to automate various aspects of security management. In this post, I show you how to install the Amazon Inspector agent automatically through the Amazon EC2 Systems Manager when a new Amazon EC2 instance is launched. In a subsequent post, I will show you how to update EC2 instances automatically that run Linux when Amazon Inspector discovers a missing security patch.

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January

January 30: How to Protect Data at Rest with Amazon EC2 Instance Store Encryption
Encrypting data at rest is vital for regulatory compliance to ensure that sensitive data saved on disks is not readable by any user or application without a valid key. Some compliance regulations such as PCI DSS and HIPAA require that data at rest be encrypted throughout the data lifecycle. To this end, AWS provides data-at-rest options and key management to support the encryption process. For example, you can encrypt Amazon EBS volumes and configure Amazon S3 buckets for server-side encryption (SSE) using AES-256 encryption. Additionally, Amazon RDS supports Transparent Data Encryption (TDE). Instance storage provides temporary block-level storage for Amazon EC2 instances. This storage is located on disks attached physically to a host computer. Instance storage is ideal for temporary storage of information that frequently changes, such as buffers, caches, and scratch data. By default, files stored on these disks are not encrypted. In this blog post, I show a method for encrypting data on Linux EC2 instance stores by using Linux built-in libraries. This method encrypts files transparently, which protects confidential data. As a result, applications that process the data are unaware of the disk-level encryption.

January 27: How to Detect and Automatically Remediate Unintended Permissions in Amazon S3 Object ACLs with CloudWatch Events
Amazon S3 Access Control Lists (ACLs) enable you to specify permissions that grant access to S3 buckets and objects. When S3 receives a request for an object, it verifies whether the requester has the necessary access permissions in the associated ACL. For example, you could set up an ACL for an object so that only the users in your account can access it, or you could make an object public so that it can be accessed by anyone. If the number of objects and users in your AWS account is large, ensuring that you have attached correctly configured ACLs to your objects can be a challenge. For example, what if a user were to call the PutObjectAcl API call on an object that is supposed to be private and make it public? Or, what if a user were to call the PutObject with the optional Acl parameter set to public-read, therefore uploading a confidential file as publicly readable? In this blog post, I show a solution that uses Amazon CloudWatch Events to detect PutObject and PutObjectAcl API calls in near-real time and helps ensure that the objects remain private by making automatic PutObjectAcl calls, when necessary.

January 26: Now Available: Amazon Cloud Directory—A Cloud-Native Directory for Hierarchical Data
Today we are launching Amazon Cloud Directory. This service is purpose-built for storing large amounts of strongly typed hierarchical data. With the ability to scale to hundreds of millions of objects while remaining cost-effective, Cloud Directory is a great fit for all sorts of cloud and mobile applications.

January 24: New SOC 2 Report Available: Confidentiality
As with everything at Amazon, the success of our security and compliance program is primarily measured by one thing: our customers’ success. Our customers drive our portfolio of compliance reports, attestations, and certifications that support their efforts in running a secure and compliant cloud environment. As a result of our engagement with key customers across the globe, we are happy to announce the publication of our new SOC 2 Confidentiality report. This report is available now through AWS Artifact in the AWS Management Console.

January 18: Compliance in the Cloud for New Financial Services Cybersecurity Regulations
Financial regulatory agencies are focused more than ever on ensuring responsible innovation. Consequently, if you want to achieve compliance with financial services regulations, you must be increasingly agile and employ dynamic security capabilities. AWS enables you to achieve this by providing you with the tools you need to scale your security and compliance capabilities on AWS. The following breakdown of the most recent cybersecurity regulations, NY DFS Rule 23 NYCRR 500, demonstrates how AWS continues to focus on your regulatory needs in the financial services sector.

January 9: New Amazon GameDev Blog Post: Protect Multiplayer Game Servers from DDoS Attacks by Using Amazon GameLift
In online gaming, distributed denial of service (DDoS) attacks target a game’s network layer, flooding servers with requests until performance degrades considerably. These attacks can limit a game’s availability to players and limit the player experience for those who can connect. Today’s new Amazon GameDev Blog post uses a typical game server architecture to highlight DDoS attack vulnerabilities and discusses how to stay protected by using built-in AWS Cloud security, AWS security best practices, and the security features of Amazon GameLift. Read the post to learn more.

January 6: The Top 10 Most Downloaded AWS Security and Compliance Documents in 2016
The following list includes the 10 most downloaded AWS security and compliance documents in 2016. Using this list, you can learn about what other people found most interesting about security and compliance last year.

January 6: FedRAMP Compliance Update: AWS GovCloud (US) Region Receives a JAB-Issued FedRAMP High Baseline P-ATO for Three New Services
Three new services in the AWS GovCloud (US) region have received a Provisional Authority to Operate (P-ATO) from the Joint Authorization Board (JAB) under the Federal Risk and Authorization Management Program (FedRAMP). JAB issued the authorization at the High baseline, which enables US government agencies and their service providers the capability to use these services to process the government’s most sensitive unclassified data, including Personal Identifiable Information (PII), Protected Health Information (PHI), Controlled Unclassified Information (CUI), criminal justice information (CJI), and financial data.

January 4: The Top 20 Most Viewed AWS IAM Documentation Pages in 2016
The following 20 pages were the most viewed AWS Identity and Access Management (IAM) documentation pages in 2016. I have included a brief description with each link to give you a clearer idea of what each page covers. Use this list to see what other people have been viewing and perhaps to pique your own interest about a topic you’ve been meaning to research.

January 3: The Most Viewed AWS Security Blog Posts in 2016
The following 10 posts were the most viewed AWS Security Blog posts that we published during 2016. You can use this list as a guide to catch up on your blog reading or even read a post again that you found particularly useful.

January 3: How to Monitor AWS Account Configuration Changes and API Calls to Amazon EC2 Security Groups
You can use AWS security controls to detect and mitigate risks to your AWS resources. The purpose of each security control is defined by its control objective. For example, the control objective of an Amazon VPC security group is to permit only designated traffic to enter or leave a network interface. Let’s say you have an Internet-facing e-commerce website, and your security administrator has determined that only HTTP (TCP port 80) and HTTPS (TCP 443) traffic should be allowed access to the public subnet. As a result, your administrator configures a security group to meet this control objective. What if, though, someone were to inadvertently change this security group’s rules and enable FTP or other protocols to access the public subnet from any location on the Internet? That expanded access could weaken the security posture of your assets. Consequently, your administrator might need to monitor the integrity of your company’s security controls so that the controls maintain their desired effectiveness. In this blog post, I explore two methods for detecting unintended changes to VPC security groups. The two methods address not only control objectives but also control failures.

If you have questions about or issues with implementing the solutions in any of these posts, please start a new thread on the forum identified near the end of each post.

– Craig

How to Protect Data at Rest with Amazon EC2 Instance Store Encryption

Post Syndicated from Assaf Namer original https://aws.amazon.com/blogs/security/how-to-protect-data-at-rest-with-amazon-ec2-instance-store-encryption/

Encrypting data at rest is vital for regulatory compliance to ensure that sensitive data saved on disks is not readable by any user or application without a valid key. Some compliance regulations such as PCI DSS and HIPAA require that data at rest be encrypted throughout the data lifecycle. To this end, AWS provides data-at-rest options and key management to support the encryption process. For example, you can encrypt Amazon EBS volumes and configure Amazon S3 buckets for server-side encryption (SSE) using AES-256 encryption. Additionally, Amazon RDS supports Transparent Data Encryption (TDE).

Instance storage provides temporary block-level storage for Amazon EC2 instances. This storage is located on disks attached physically to a host computer. Instance storage is ideal for temporary storage of information that frequently changes, such as buffers, caches, and scratch data. By default, files stored on these disks are not encrypted.

In this blog post, I show a method for encrypting data on Linux EC2 instance stores by using Linux built-in libraries. This method encrypts files transparently, which protects confidential data. As a result, applications that process the data are unaware of the disk-level encryption.

First, though, I will provide some background information required for this solution.

Disk and file system encryption

You can use two methods to encrypt files on instance stores. The first method is disk encryption, in which the entire disk or block within the disk is encrypted by using one or more encryption keys. Disk encryption operates below the file-system level, is operating-system agnostic, and hides directory and file information such as name and size. Encrypting File System, for example, is a Microsoft extension to the Windows NT operating system’s New Technology File System (NTFS) that provides disk encryption.

The second method is file-system-level encryption. Files and directories are encrypted, but not the entire disk or partition. File-system-level encryption operates on top of the file system and is portable across operating systems.

The Linux dm-crypt Infrastructure

Dm-crypt is a Linux kernel-level encryption mechanism that allows users to mount an encrypted file system. Mounting a file system is the process in which a file system is attached to a directory (mount point), making it available to the operating system. After mounting, all files in the file system are available to applications without any additional interaction; however, these files are encrypted when stored on disk.

Device mapper is an infrastructure in the Linux 2.6 and 3.x kernel that provides a generic way to create virtual layers of block devices. The device mapper crypt target provides transparent encryption of block devices using the kernel crypto API. The solution in this post uses dm-crypt in conjunction with a disk-backed file system mapped to a logical volume by the Logical Volume Manager (LVM). LVM provides logical volume management for the Linux kernel.

The following diagram depicts the relationship between an application, file system, and dm-crypt. Dm-crypt sits between the physical disk and the file system, and data written from the operating system to the disk is encrypted. The application is unaware of such disk-level encryption. Applications use a specific mount point in order to store and retrieve files, and these files are encrypted when stored to disk. If the disk is lost or stolen, the data on the disk is useless.

Overview of the solution

In this post, I create a new file system called secretfs. This file system is encrypted using dm-crypt. This example uses LVM and Linux Unified Key Setup (LUKS) to encrypt a file system. The encrypted file system sits on the EC2 instance store disk. Note that the internal store file system is not encrypted but rather a newly created file system.

The following diagram shows how the newly encrypted file system resides in the EC2 internal store disk. Applications that need to save sensitive data temporarily will use the secretfs mount point (‘/mnt/secretfs’) directory to store temporary or scratch files.

Requirements

This solution has three requirements for the solution to work. First, you need to configure the related items on boot using EC2 launch configuration because the encrypted file system is created at boot time. An administrator should have full control over every step and should be able to grant and revoke the encrypted file system creation or access to keys. Second, you must enable logging for every encryption or decryption request by using AWS CloudTrail. In particular, logging is critical when the keys are created and when an EC2 instance requests password decryption to unlock an encrypted file system. Lastly, you should integrate the solution with other AWS services, as described in the next section.

AWS services used in this solution

I use the following AWS services in this solution:

  • AWS Key Management Service (KMS) – AWS KMS is a managed service that enables easy creation and control of encryption keys used to encrypt data. KMS uses envelope encryption in which data is encrypted using a data key that is then encrypted using a master key. Master keys can also be used to encrypt and decrypt up to 4 kilobytes of data. In our solution, I use KMS encrypt/decrypt APIs to encrypt the encrypted file system’s password. See more information about envelope encryption.
  • AWS CloudTrail – CloudTrail records AWS API calls for your account. KMS and CloudTrail are fully integrated, which means CloudTrail logs each request to and from KMS for future auditing. This post’s solution enables CloudTrail for monitoring and audit.
  • Amazon S3 – S3 is an AWS storage I use S3 in this post to save the encrypted file system password.
  • AWS Identity and Access Management (IAM) – AWS IAM enables you to control access securely to AWS services. In this post, I configure and attach a policy to EC2 instances that allows access to the S3 bucket to read the encrypted password file and to KMS to decrypt the file system password.

Architectural overview

The following diagram illustrates the steps in the process of encrypting the EC2 instance store.

Diagram illustrating the steps in the process of encrypting the EC2 instance store

In this architectural diagram:

  1. The administrator encrypts a secret password by using KMS. The encrypted password is stored in a file.
  2. The administrator puts the file containing the encrypted password in an S3 bucket.
  3. At instance boot time, the instance copies the encrypted file to an internal disk.
  4. The EC2 instance then decrypts the file using KMS and retrieves the plaintext password. The password is used to configure the Linux encrypted file system with LUKS. All data written to the encrypted file system is encrypted by using an AES-128 encryption algorithm when stored on disk.

Implementing the solution

Create an S3 bucket

First, you create a bucket to store the encrypted password file. This file contains the password (key) used to encrypt the file system. Each EC2 instance upon boot copies the encrypted password file, decrypts the file, and retrieves the plaintext password, which is used to encrypt the file system on the instance store disk.

In this step, you create the S3 bucket that stores the encrypted password file, and apply the necessary permissions. If you are using an Amazon VPC endpoint for Amazon S3, you also need to add permissions to the bucket to allow access from the endpoint. (For a detailed example, see Example Bucket Policies for VPC Endpoints for Amazon S3.)

To create a new bucket:

  1. Sign in to the S3 console and choose Create Bucket.
  2. In the Bucket Name box, type your bucket name and then choose Create.
  3. You should see the details about your new bucket in the right pane.

Configure IAM roles and permission for the S3 bucket

When an EC2 instance boots, it must read the encrypted password file from S3 and then decrypt the password using KMS. In this section, I configure an IAM policy that allows the EC2 instance to assume a role with the right access permissions to the S3 bucket. The following policy grants the correct access permissions, in which your-bucket-name is the S3 bucket that stores the encrypted password file.

To create and configure the IAM policy:

  1. Sign in to the AWS Management Console and navigate to the IAM console. In the navigation pane, choose Policies, choose Create Policy, select Create Your Own Policy, name and describe the policy, and paste the following policy. Choose Create Policy. For more details, see Creating Customer Managed Policies.
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Sid": "Stmt1478729875000",
                "Effect": "Allow",
                "Action": [
                    "s3:GetObject"
                ],
                "Resource": [
                    "arn:aws:s3:::<your-bucket-name>/LuksInternalStorageKey"
                ]
            }
        ]
    }

    The preceding policy grants read access to the bucket where the encrypted password is stored. This policy is used by the EC2 instance, which requires you to configure an IAM role. You will configure KMS permissions later in this post.

  1. In the IAM console, choose Roles, and then choose Create New Role.
  2. In Step 1: Role Name, type your role name, and choose Next Step.
  3. In Step 2: Select Role Type, choose Amazon EC2 and choose Next Step.
  4. In Step 3: Established Trust, choose Next Step.
  5. In Step 4: Attach Policy, choose the policy you created in Step 1, as shown in the following screenshot.Screenshot of choosing the policy
  1. In Step 5: Review, review the configuration and complete the steps. The newly created IAM role is now ready. You will use it when launching new EC2 instances, which will have the permission to access the encrypted password file in the S3 bucket.

You now should have a new IAM role listed on the Roles page. Choose Roles to list all roles in your account and then select the role you just created as shown in the following screenshot.

Screenshot of selecting the role you just created

Encrypt a secret password with KMS and store it in the S3 bucket

Next, you use KMS to encrypt a secret password. To encrypt text by using KMS, you must use AWS CLI. AWS CLI is installed by default on EC2 Amazon Linux instances and you can install it on Linux, Windows, or Mac computers.

To encrypt a secret password with KMS and store it in the S3 bucket:

  • From the AWS CLI, type the following command to encrypt a secret password by using KMS (replace the region name with your region). You must have the right permissions in order to create keys and put objects in S3 (for more details, see Using IAM Policies with AWS KMS). In this example, I have used AWS CLI on the Linux OS to encrypt and generate the encrypted password file.
aws --region us-east-1 kms encrypt --key-id 'alias/EncFSForEC2InternalStorageKey' --plaintext "ThisIs-a-SecretPassword" --query CiphertextBlob --output text | base64 --decode > LuksInternalStorageKey

aws s3 cp LuksInternalStorageKey s3://<bucket-name>/LuksInternalStorageKey

The preceding commands encrypt the password (Base64 is used to decode the cipher text). The command outputs the results to a file called LuksInternalStorageKey. It also creates a key alias (key name) that makes it easy to identify different keys; the alias is called EncFSForEC2InternalStorageKey. The file is then copied to the S3 bucket I created earlier in this post.

Configure permissions to allow the role to access the KMS key

Next, you grant the role access to the key you just created with KMS:

  1. From the IAM console, choose Encryption keys from the navigation pane.
  1. Select EncFSForEC2InternalStorageKey (this is the key alias you configured in the previous section). To add a new role that can use the key, scroll down to the Key Policy and then choose Add under Key Users.Screenshot of adding a new role that can use the KMS key
  1. Choose the new role you created earlier in this post and then choose Attach.
  1. The role now has permission to use the key.

Configure EC2 with role and launch configurations

In this section, you launch a new EC2 instance with the new IAM role and a bootstrap script that executes the steps to encrypt the file system, as described earlier in the “Architectural overview” section:

  1. In the EC2 console, launch a new instance (see this tutorial for more details). In Step 3: Configure Instance Details, choose the IAM role you configured earlier, as shown in the following screenshot.Screenshot of configuring EC2 instance details
  1. Expand the Advanced Details section (see previous screenshot) and paste the following script in the EC2 instance’s User data Keep the As text check box selected. The script will be executed at EC2 boot time.
    #!/bin/bash
    
    ## Initial setup to be executed on boot
    ##====================================
    
    # Create an empty file. This file will be used to host the file system.
    # In this example we create a 2 GB file called secretfs (Secret File System).
    dd of=secretfs bs=1G count=0 seek=2
    # Lock down normal access to the file.
    chmod 600 secretfs
    # Associate a loopback device with the file.
    losetup /dev/loop0 secretfs
    #Copy encrypted password file from S3. The password is used to configure LUKE later on.
    aws s3 cp s3://an-internalstoragekeybucket/LuksInternalStorageKey .
    # Decrypt the password from the file with KMS, save the secret password in LuksClearTextKey
    LuksClearTextKey=$(aws --region us-east-1 kms decrypt --ciphertext-blob fileb://LuksInternalStorageKey --output text --query Plaintext | base64 --decode)
    # Encrypt storage in the device. cryptsetup will use the Linux
    # device mapper to create, in this case, /dev/mapper/secretfs.
    # Initialize the volume and set an initial key.
    echo "$LuksClearTextKey" | cryptsetup -y luksFormat /dev/loop0
    # Open the partition, and create a mapping to /dev/mapper/secretfs.
    echo "$LuksClearTextKey" | cryptsetup luksOpen /dev/loop0 secretfs
    # Clear the LuksClearTextKey variable because we don't need it anymore.
    unset LuksClearTextKey
    # Check its status (optional).
    cryptsetup status secretfs
    # Zero out the new encrypted device.
    dd if=/dev/zero of=/dev/mapper/secretfs
    # Create a file system and verify its status.
    mke2fs -j -O dir_index /dev/mapper/secretfs
    # List file system configuration (optional).
    tune2fs -l /dev/mapper/secretfs
    # Mount the new file system to /mnt/secretfs.
    mkdir /mnt/secretfs
    mount /dev/mapper/secretfs /mnt/secretfs

  2. If you have not enabled it already, be sure to enable CloudTrail on your account. Using CloudTrail, you will be able to monitor and audit access to the KMS key.
  3. Launch the EC2 instance, which copies the password file from S3, decrypts the file using KMS, and configures an encrypted file system. The file system is mounted on /mnt/secretfs. Therefore, every file written to this mount point is encrypted when stored to disk. Applications that process sensitive data and need temporary storage should use the encrypted file system by writing and reading files from the mount point, ‘/mnt/secretfs’. The rest of the file system (for example, /home/ec2-user) is not encrypted.

You can list the encrypted file system’s status. First, SSH to the EC2 instance using the key pair you used to launch the EC2 instance. (For more information about logging in to an EC2 instance using a key pair, see Getting Started with Amazon EC2 Linux Instances.) Then, run the following command as root.

[[email protected] ec2-user]# cryptsetup status secretfs
/dev/mapper/secretfs is active and is in use.
    type:    LUKS1
    cipher:  aes-xts-plain64
    keysize: 256 bits
    device:  /dev/loop0
    loop:    /secretfs
    offset:  4096 sectors
    size:    4190208 sectors
    mode:    read/write

As the command’s results should show, the file system is encrypted with AES-256 using XTS mode. XTS is a configuration method that allows ciphers to work with large data streams, without the risk of compromising the provided security.

Conclusion

This blog post shows you how to encrypt a file system on EC2 instance storage by using built-in Linux libraries and drivers with LVM and LUKS, in conjunction with AWS services such as S3 and KMS. If your applications need temporary storage, you can use an EC2 internal disk that is physically attached to the host computer. The data on instance stores persists only during the lifetime of its associated instance. However, instance store volumes are not encrypted. This post provides a simple solution that balances between the speed and availability of instance stores and the need for encryption at rest when dealing with sensitive data.

If you have comments about this blog post, submit them in the “Comments” section below. If you have implementation questions about the solution in this post, please start a new thread on the EC2 forum.

– Assaf