Tag Archives: EC2 Systems Manager

Automating Blue/Green Deployments of Infrastructure and Application Code using AMIs, AWS Developer Tools, & Amazon EC2 Systems Manager

Post Syndicated from Ramesh Adabala original https://aws.amazon.com/blogs/devops/bluegreen-infrastructure-application-deployment-blog/

Previous DevOps blog posts have covered the following use cases for infrastructure and application deployment automation:

An AMI provides the information required to launch an instance, which is a virtual server in the cloud. You can use one AMI to launch as many instances as you need. It is security best practice to customize and harden your base AMI with required operating system updates and, if you are using AWS native services for continuous security monitoring and operations, you are strongly encouraged to bake into the base AMI agents such as those for Amazon EC2 Systems Manager (SSM), Amazon Inspector, CodeDeploy, and CloudWatch Logs. A customized and hardened AMI is often referred to as a “golden AMI.” The use of golden AMIs to create EC2 instances in your AWS environment allows for fast and stable application deployment and scaling, secure application stack upgrades, and versioning.

In this post, using the DevOps automation capabilities of Systems Manager, AWS developer tools (CodePipeLine, CodeDeploy, CodeCommit, CodeBuild), I will show you how to use AWS CodePipeline to orchestrate the end-to-end blue/green deployments of a golden AMI and application code. Systems Manager Automation is a powerful security feature for enterprises that want to mature their DevSecOps practices.

Here are the high-level phases and primary services covered in this use case:

 

You can access the source code for the sample used in this post here: https://github.com/awslabs/automating-governance-sample/tree/master/Bluegreen-AMI-Application-Deployment-blog.

This sample will create a pipeline in AWS CodePipeline with the building blocks to support the blue/green deployments of infrastructure and application. The sample includes a custom Lambda step in the pipeline to execute Systems Manager Automation to build a golden AMI and update the Auto Scaling group with the golden AMI ID for every rollout of new application code. This guarantees that every new application deployment is on a fully patched and customized AMI in a continuous integration and deployment model. This enables the automation of hardened AMI deployment with every new version of application deployment.

 

 

We will build and run this sample in three parts.

Part 1: Setting up the AWS developer tools and deploying a base web application

Part 1 of the AWS CloudFormation template creates the initial Java-based web application environment in a VPC. It also creates all the required components of Systems Manager Automation, CodeCommit, CodeBuild, and CodeDeploy to support the blue/green deployments of the infrastructure and application resulting from ongoing code releases.

Part 1 of the AWS CloudFormation stack creates these resources:

After Part 1 of the AWS CloudFormation stack creation is complete, go to the Outputs tab and click the Elastic Load Balancing link. You will see the following home page for the base web application:

Make sure you have all the outputs from the Part 1 stack handy. You need to supply them as parameters in Part 3 of the stack.

Part 2: Setting up your CodeCommit repository

In this part, you will commit and push your sample application code into the CodeCommit repository created in Part 1. To access the initial git commands to clone the empty repository to your local machine, click Connect to go to the AWS CodeCommit console. Make sure you have the IAM permissions required to access AWS CodeCommit from command line interface (CLI).

After you’ve cloned the repository locally, download the sample application files from the part2 folder of the Git repository and place the files directly into your local repository. Do not include the aws-codedeploy-sample-tomcat folder. Go to the local directory and type the following commands to commit and push the files to the CodeCommit repository:

git add .
git commit -a -m "add all files from the AWS Java Tomcat CodeDeploy application"
git push

After all the files are pushed successfully, the repository should look like this:

 

Part 3: Setting up CodePipeline to enable blue/green deployments     

Part 3 of the AWS CloudFormation template creates the pipeline in AWS CodePipeline and all the required components.

a) Source: The pipeline is triggered by any change to the CodeCommit repository.

b) BuildGoldenAMI: This Lambda step executes the Systems Manager Automation document to build the golden AMI. After the golden AMI is successfully created, a new launch configuration with the new AMI details will be updated into the Auto Scaling group of the application deployment group. You can watch the progress of the automation in the EC2 console from the Systems Manager –> Automations menu.

c) Build: This step uses the application build spec file to build the application build artifact. Here are the CodeBuild execution steps and their status:

d) Deploy: This step clones the Auto Scaling group, launches the new instances with the new AMI, deploys the application changes, reroutes the traffic from the elastic load balancer to the new instances and terminates the old Auto Scaling group. You can see the execution steps and their status in the CodeDeploy console.

After the CodePipeline execution is complete, you can access the application by clicking the Elastic Load Balancing link. You can find it in the output of Part 1 of the AWS CloudFormation template. Any consecutive commits to the application code in the CodeCommit repository trigger the pipelines and deploy the infrastructure and code with an updated AMI and code.

 

If you have feedback about this post, add it to the Comments section below. If you have questions about implementing the example used in this post, open a thread on the Developer Tools forum.


About the author

 

Ramesh Adabala is a Solutions Architect in Southeast Enterprise Solution Architecture team at Amazon Web Services.

Deploying Java Microservices on Amazon EC2 Container Service

Post Syndicated from Nathan Taber original https://aws.amazon.com/blogs/compute/deploying-java-microservices-on-amazon-ec2-container-service/

This post and accompanying code graciously contributed by:

Huy Huynh
Sr. Solutions Architect
Magnus Bjorkman
Solutions Architect

Java is a popular language used by many enterprises today. To simplify and accelerate Java application development, many companies are moving from a monolithic to microservices architecture. For some, it has become a strategic imperative. Containerization technology, such as Docker, lets enterprises build scalable, robust microservice architectures without major code rewrites.

In this post, I cover how to containerize a monolithic Java application to run on Docker. Then, I show how to deploy it on AWS using Amazon EC2 Container Service (Amazon ECS), a high-performance container management service. Finally, I show how to break the monolith into multiple services, all running in containers on Amazon ECS.

Application Architecture

For this example, I use the Spring Pet Clinic, a monolithic Java application for managing a veterinary practice. It is a simple REST API, which allows the client to manage and view Owners, Pets, Vets, and Visits.

It is a simple three-tier architecture:

  • Client
    You simulate this by using curl commands.
  • Web/app server
    This is the Java and Spring-based application that you run using the embedded Tomcat. As part of this post, you run this within Docker containers.
  • Database server
    This is the relational database for your application that stores information about owners, pets, vets, and visits. For this post, use MySQL RDS.

I decided to not put the database inside a container as containers were designed for applications and are transient in nature. The choice was made even easier because you have a fully managed database service available with Amazon RDS.

RDS manages the work involved in setting up a relational database, from provisioning the infrastructure capacity that you request to installing the database software. After your database is up and running, RDS automates common administrative tasks, such as performing backups and patching the software that powers your database. With optional Multi-AZ deployments, Amazon RDS also manages synchronous data replication across Availability Zones with automatic failover.

Walkthrough

You can find the code for the example covered in this post at amazon-ecs-java-microservices on GitHub.

Prerequisites

You need the following to walk through this solution:

  • An AWS account
  • An access key and secret key for a user in the account
  • The AWS CLI installed

Also, install the latest versions of the following:

  • Java
  • Maven
  • Python
  • Docker

Step 1: Move the existing Java Spring application to a container deployed using Amazon ECS

First, move the existing monolith application to a container and deploy it using Amazon ECS. This is a great first step before breaking the monolith apart because you still get some benefits before breaking apart the monolith:

  • An improved pipeline. The container also allows an engineering organization to create a standard pipeline for the application lifecycle.
  • No mutations to machines.

You can find the monolith example at 1_ECS_Java_Spring_PetClinic.

Container deployment overview

The following diagram is an overview of what the setup looks like for Amazon ECS and related services:

This setup consists of the following resources:

  • The client application that makes a request to the load balancer.
  • The load balancer that distributes requests across all available ports and instances registered in the application’s target group using round-robin.
  • The target group that is updated by Amazon ECS to always have an up-to-date list of all the service containers in the cluster. This includes the port on which they are accessible.
  • One Amazon ECS cluster that hosts the container for the application.
  • A VPC network to host the Amazon ECS cluster and associated security groups.

Each container has a single application process that is bound to port 8080 within its namespace. In reality, all the containers are exposed on a different, randomly assigned port on the host.

The architecture is containerized but still monolithic because each container has all the same features of the rest of the containers

The following is also part of the solution but not depicted in the above diagram:

  • One Amazon EC2 Container Registry (Amazon ECR) repository for the application.
  • A service/task definition that spins up containers on the instances of the Amazon ECS cluster.
  • A MySQL RDS instance that hosts the applications schema. The information about the MySQL RDS instance is sent in through environment variables to the containers, so that the application can connect to the MySQL RDS instance.

I have automated setup with the 1_ECS_Java_Spring_PetClinic/ecs-cluster.cf AWS CloudFormation template and a Python script.

The Python script calls the CloudFormation template for the initial setup of the VPC, Amazon ECS cluster, and RDS instance. It then extracts the outputs from the template and uses those for API calls to create Amazon ECR repositories, tasks, services, Application Load Balancer, and target groups.

Environment variables and Spring properties binding

As part of the Python script, you pass in a number of environment variables to the container as part of the task/container definition:

'environment': [
{
'name': 'SPRING_PROFILES_ACTIVE',
'value': 'mysql'
},
{
'name': 'SPRING_DATASOURCE_URL',
'value': my_sql_options['dns_name']
},
{
'name': 'SPRING_DATASOURCE_USERNAME',
'value': my_sql_options['username']
},
{
'name': 'SPRING_DATASOURCE_PASSWORD',
'value': my_sql_options['password']
}
],

The preceding environment variables work in concert with the Spring property system. The value in the variable SPRING_PROFILES_ACTIVE, makes Spring use the MySQL version of the application property file. The other environment files override the following properties in that file:

  • spring.datasource.url
  • spring.datasource.username
  • spring.datasource.password

Optionally, you can also encrypt sensitive values by using Amazon EC2 Systems Manager Parameter Store. Instead of handing in the password, you pass in a reference to the parameter and fetch the value as part of the container startup. For more information, see Managing Secrets for Amazon ECS Applications Using Parameter Store and IAM Roles for Tasks.

Spotify Docker Maven plugin

Use the Spotify Docker Maven plugin to create the image and push it directly to Amazon ECR. This allows you to do this as part of the regular Maven build. It also integrates the image generation as part of the overall build process. Use an explicit Dockerfile as input to the plugin.

FROM frolvlad/alpine-oraclejdk8:slim
VOLUME /tmp
ADD spring-petclinic-rest-1.7.jar app.jar
RUN sh -c 'touch /app.jar'
ENV JAVA_OPTS=""
ENTRYPOINT [ "sh", "-c", "java $JAVA_OPTS -Djava.security.egd=file:/dev/./urandom -jar /app.jar" ]

The Python script discussed earlier uses the AWS CLI to authenticate you with AWS. The script places the token in the appropriate location so that the plugin can work directly against the Amazon ECR repository.

Test setup

You can test the setup by running the Python script:
python setup.py -m setup -r <your region>

After the script has successfully run, you can test by querying an endpoint:
curl <your endpoint from output above>/owner

You can clean this up before going to the next section:
python setup.py -m cleanup -r <your region>

Step 2: Converting the monolith into microservices running on Amazon ECS

The second step is to convert the monolith into microservices. For a real application, you would likely not do this as one step, but re-architect an application piece by piece. You would continue to run your monolith but it would keep getting smaller for each piece that you are breaking apart.

By migrating microservices, you would get four benefits associated with microservices:

  • Isolation of crashes
    If one microservice in your application is crashing, then only that part of your application goes down. The rest of your application continues to work properly.
  • Isolation of security
    When microservice best practices are followed, the result is that if an attacker compromises one service, they only gain access to the resources of that service. They can’t horizontally access other resources from other services without breaking into those services as well.
  • Independent scaling
    When features are broken out into microservices, then the amount of infrastructure and number of instances of each microservice class can be scaled up and down independently.
  • Development velocity
    In a monolith, adding a new feature can potentially impact every other feature that the monolith contains. On the other hand, a proper microservice architecture has new code for a new feature going into a new service. You can be confident that any code you write won’t impact the existing code at all, unless you explicitly write a connection between two microservices.

Find the monolith example at 2_ECS_Java_Spring_PetClinic_Microservices.
You break apart the Spring Pet Clinic application by creating a microservice for each REST API operation, as well as creating one for the system services.

Java code changes

Comparing the project structure between the monolith and the microservices version, you can see that each service is now its own separate build.
First, the monolith version:

You can clearly see how each API operation is its own subpackage under the org.springframework.samples.petclinic package, all part of the same monolithic application.
This changes as you break it apart in the microservices version:

Now, each API operation is its own separate build, which you can build independently and deploy. You have also duplicated some code across the different microservices, such as the classes under the model subpackage. This is intentional as you don’t want to introduce artificial dependencies among the microservices and allow these to evolve differently for each microservice.

Also, make the dependencies among the API operations more loosely coupled. In the monolithic version, the components are tightly coupled and use object-based invocation.

Here is an example of this from the OwnerController operation, where the class is directly calling PetRepository to get information about pets. PetRepository is the Repository class (Spring data access layer) to the Pet table in the RDS instance for the Pet API:

@RestController
class OwnerController {

    @Inject
    private PetRepository pets;
    @Inject
    private OwnerRepository owners;
    private static final Logger logger = LoggerFactory.getLogger(OwnerController.class);

    @RequestMapping(value = "/owner/{ownerId}/getVisits", method = RequestMethod.GET)
    public ResponseEntity<List<Visit>> getOwnerVisits(@PathVariable int ownerId){
        List<Pet> petList = this.owners.findById(ownerId).getPets();
        List<Visit> visitList = new ArrayList<Visit>();
        petList.forEach(pet -> visitList.addAll(pet.getVisits()));
        return new ResponseEntity<List<Visit>>(visitList, HttpStatus.OK);
    }
}

In the microservice version, call the Pet API operation and not PetRepository directly. Decouple the components by using interprocess communication; in this case, the Rest API. This provides for fault tolerance and disposability.

@RestController
class OwnerController {

    @Value("#{environment['SERVICE_ENDPOINT'] ?: 'localhost:8080'}")
    private String serviceEndpoint;

    @Inject
    private OwnerRepository owners;
    private static final Logger logger = LoggerFactory.getLogger(OwnerController.class);

    @RequestMapping(value = "/owner/{ownerId}/getVisits", method = RequestMethod.GET)
    public ResponseEntity<List<Visit>> getOwnerVisits(@PathVariable int ownerId){
        List<Pet> petList = this.owners.findById(ownerId).getPets();
        List<Visit> visitList = new ArrayList<Visit>();
        petList.forEach(pet -> {
            logger.info(getPetVisits(pet.getId()).toString());
            visitList.addAll(getPetVisits(pet.getId()));
        });
        return new ResponseEntity<List<Visit>>(visitList, HttpStatus.OK);
    }

    private List<Visit> getPetVisits(int petId){
        List<Visit> visitList = new ArrayList<Visit>();
        RestTemplate restTemplate = new RestTemplate();
        Pet pet = restTemplate.getForObject("http://"+serviceEndpoint+"/pet/"+petId, Pet.class);
        logger.info(pet.getVisits().toString());
        return pet.getVisits();
    }
}

You now have an additional method that calls the API. You are also handing in the service endpoint that should be called, so that you can easily inject dynamic endpoints based on the current deployment.

Container deployment overview

Here is an overview of what the setup looks like for Amazon ECS and the related services:

This setup consists of the following resources:

  • The client application that makes a request to the load balancer.
  • The Application Load Balancer that inspects the client request. Based on routing rules, it directs the request to an instance and port from the target group that matches the rule.
  • The Application Load Balancer that has a target group for each microservice. The target groups are used by the corresponding services to register available container instances. Each target group has a path, so when you call the path for a particular microservice, it is mapped to the correct target group. This allows you to use one Application Load Balancer to serve all the different microservices, accessed by the path. For example, https:///owner/* would be mapped and directed to the Owner microservice.
  • One Amazon ECS cluster that hosts the containers for each microservice of the application.
  • A VPC network to host the Amazon ECS cluster and associated security groups.

Because you are running multiple containers on the same instances, use dynamic port mapping to avoid port clashing. By using dynamic port mapping, the container is allocated an anonymous port on the host to which the container port (8080) is mapped. The anonymous port is registered with the Application Load Balancer and target group so that traffic is routed correctly.

The following is also part of the solution but not depicted in the above diagram:

  • One Amazon ECR repository for each microservice.
  • A service/task definition per microservice that spins up containers on the instances of the Amazon ECS cluster.
  • A MySQL RDS instance that hosts the applications schema. The information about the MySQL RDS instance is sent in through environment variables to the containers. That way, the application can connect to the MySQL RDS instance.

I have again automated setup with the 2_ECS_Java_Spring_PetClinic_Microservices/ecs-cluster.cf CloudFormation template and a Python script.

The CloudFormation template remains the same as in the previous section. In the Python script, you are now building five different Java applications, one for each microservice (also includes a system application). There is a separate Maven POM file for each one. The resulting Docker image gets pushed to its own Amazon ECR repository, and is deployed separately using its own service/task definition. This is critical to get the benefits described earlier for microservices.

Here is an example of the POM file for the Owner microservice:

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>org.springframework.samples</groupId>
    <artifactId>spring-petclinic-rest</artifactId>
    <version>1.7</version>
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>1.5.2.RELEASE</version>
    </parent>
    <properties>
        <!-- Generic properties -->
        <java.version>1.8</java.version>
        <docker.registry.host>${env.docker_registry_host}</docker.registry.host>
    </properties>
    <dependencies>
        <dependency>
            <groupId>javax.inject</groupId>
            <artifactId>javax.inject</artifactId>
            <version>1</version>
        </dependency>
        <!-- Spring and Spring Boot dependencies -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-actuator</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-rest</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-cache</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-jpa</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <!-- Databases - Uses HSQL by default -->
        <dependency>
            <groupId>org.hsqldb</groupId>
            <artifactId>hsqldb</artifactId>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <scope>runtime</scope>
        </dependency>
        <!-- caching -->
        <dependency>
            <groupId>javax.cache</groupId>
            <artifactId>cache-api</artifactId>
        </dependency>
        <dependency>
            <groupId>org.ehcache</groupId>
            <artifactId>ehcache</artifactId>
        </dependency>
        <!-- end of webjars -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-devtools</artifactId>
            <scope>runtime</scope>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-maven-plugin</artifactId>
            </plugin>
            <plugin>
                <groupId>com.spotify</groupId>
                <artifactId>docker-maven-plugin</artifactId>
                <version>0.4.13</version>
                <configuration>
                    <imageName>${env.docker_registry_host}/${project.artifactId}</imageName>
                    <dockerDirectory>src/main/docker</dockerDirectory>
                    <useConfigFile>true</useConfigFile>
                    <registryUrl>${env.docker_registry_host}</registryUrl>
                    <!--dockerHost>https://${docker.registry.host}</dockerHost-->
                    <resources>
                        <resource>
                            <targetPath>/</targetPath>
                            <directory>${project.build.directory}</directory>
                            <include>${project.build.finalName}.jar</include>
                        </resource>
                    </resources>
                    <forceTags>false</forceTags>
                    <imageTags>
                        <imageTag>${project.version}</imageTag>
                    </imageTags>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

Test setup

You can test this by running the Python script:

python setup.py -m setup -r <your region>

After the script has successfully run, you can test by querying an endpoint:

curl <your endpoint from output above>/owner

Conclusion

Migrating a monolithic application to a containerized set of microservices can seem like a daunting task. Following the steps outlined in this post, you can begin to containerize monolithic Java apps, taking advantage of the container runtime environment, and beginning the process of re-architecting into microservices. On the whole, containerized microservices are faster to develop, easier to iterate on, and more cost effective to maintain and secure.

This post focused on the first steps of microservice migration. You can learn more about optimizing and scaling your microservices with components such as service discovery, blue/green deployment, circuit breakers, and configuration servers at http://aws.amazon.com/containers.

If you have questions or suggestions, please comment below.

Amazon EC2 Systems Manager Patch Manager now supports Linux

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-ec2-systems-manager-patch-manager-now-supports-linux/

Hot on the heels of some other great Amazon EC2 Systems Manager (SSM) updates is another vital enhancement: the ability to use Patch Manager on Linux instances!

We launched Patch Manager with SSM at re:Invent in 2016 and Linux support was a commonly requested feature. Starting today we can support patch manager in:

  • Amazon Linux 2014.03 and later (2015.03 and later for 64-bit)
  • Ubuntu Server 16.04 LTS, 14.04 LTS, and 12.04 LTS
  • RHEL 6.5 and later (7.x and later for 64-Bit)

When I think about patching a big group of heterogenous systems I get a little anxious. Years ago, I administered my school’s computer lab. This involved a modest group of machines running a small number of VMs with an immodest number of distinct Linux distros. When there was a critical security patch it was a lot of work to remember the constraints of each system. I remember having to switch back and forth between arcane invocations of various package managers – pinning and unpinning packages: sudo yum update -y, rpm -Uvh ..., apt-get, or even emerge (one of our professors loved Gentoo).

Even now, when I use configuration management systems like Chef or Puppet I still have to specify the package manager and remember a portion of the invocation – and I don’t always want to roll out a patch without some manual approval process. Based on these experiences I decided it was time for me to update my skillset and learn to use Patch Manager.

Patch Manager is a fully-managed service (provided at no additional cost) that helps you simplify your operating system patching process, including defining the patches you want to approve for deployment, the method of patch deployment, the timing for patch roll-outs, and determining patch compliance status across your entire fleet of instances. It’s extremely configurable with some sensible defaults and helps you easily deal with patching hetergenous clusters.

Since I’m not running that school computer lab anymore my fleet is a bit smaller these days:

a list of instances with amusing names

As you can see above I only have a few instances in this region but if you look at the launch times they range from 2014 to a few minutes ago. I’d be willing to bet I’ve missed a patch or two somewhere (luckily most of these have strict security groups). To get started I installed the SSM agent on all of my machines by following the documentation here. I also made sure I had the appropriate role and IAM profile attached to the instances to talk to SSM – I just used this managed policy: AmazonEC2RoleforSSM.

Now I need to define a Patch Baseline. We’ll make security updates critical and all other updates informational and subject to my approval.

 

Next, I can run the AWS-RunPatchBaseline SSM Run Command in “Scan” mode to generate my patch baseline data.

Then, we can go to the Patch Compliance page in the EC2 console and check out how I’m doing.

Yikes, looks like I need some security updates! Now, I can use Maintenance Windows, Run Command, or State Manager in SSM to actually manage this patching process. One thing to note, when patching is completed, your machine reboots – so managing that roll out with Maintenance Windows or State Manager is a best practice. If I had a larger set of instances I could group them by creating a tag named “Patch Group”.

For now, I’ll just use the same AWS-RunPatchBaseline Run Command command from above with the “Install” operation to update these machines.

As always, the CLIs and APIs have been updated to support these new options. The documentation is here. I hope you’re all able to spend less time patching and more time coding!

Randall

Manage Instances at Scale without SSH Access Using EC2 Run Command

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/manage-instances-at-scale-without-ssh-access-using-ec2-run-command/

The guest post below, written by Ananth Vaidyanathan (Senior Product Manager for EC2 Systems Manager) and Rich Urmston (Senior Director of Cloud Architecture at Pegasystems) shows you how to use EC2 Run Command to manage a large collection of EC2 instances without having to resort to SSH.

Jeff;


Enterprises often have several managed environments and thousands of Amazon EC2 instances. It’s important to manage systems securely, without the headaches of Secure Shell (SSH). Run Command, part of Amazon EC2 Systems Manager, allows you to run remote commands on instances (or groups of instances using tags) in a controlled and auditable manner. It’s been a nice added productivity boost for Pega Cloud operations, which rely daily on Run Command services.

You can control Run Command access through standard IAM roles and policies, define documents to take input parameters, control the S3 bucket used to return command output. You can also share your documents with other AWS accounts, or with the public. All in all, Run Command provides a nice set of remote management features.

Better than SSH
Here’s why Run Command is a better option than SSH and why Pegasystems has adopted it as their primary remote management tool:

Run Command Takes Less Time –  Securely connecting to an instance requires a few steps e.g. jumpboxes to connect to or IP addresses to whitelist etc. With Run Command, cloud ops engineers can invoke commands directly from their laptop, and never have to find keys or even instance IDs. Instead, system security relies on AWS auth, IAM roles and policies.

Run Command Operations are Fully Audited – With SSH, there is no real control over what they can do, nor is there an audit trail. With Run Command, every invoked operation is audited in CloudTrail, including information on the invoking user, instances on which command was run, parameters, and operation status. You have full control and ability to restrict what functions engineers can perform on a system.

Run Command has no SSH keys to Manage – Run Command leverages standard AWS credentials, API keys, and IAM policies. Through integration with a corporate auth system, engineers can interact with systems based on their corporate credentials and identity.

Run Command can Manage Multiple Systems at the Same Time – Simple tasks such as looking at the status of a Linux service or retrieving a log file across a fleet of managed instances is cumbersome using SSH. Run Command allows you to specify a list of instances by IDs or tags, and invokes your command, in parallel, across the specified fleet. This provides great leverage when troubleshooting or managing more than the smallest Pega clusters.

Run Command Makes Automating Complex Tasks Easier – Standardizing operational tasks requires detailed procedure documents or scripts describing the exact commands. Managing or deploying these scripts across the fleet is cumbersome. Run Command documents provide an easy way to encapsulate complex functions, and handle document management and access controls. When combined with AWS Lambda, documents provide a powerful automation platform to handle any complex task.

Example – Restarting a Docker Container
Here is an example of a simple document used to restart a Docker container. It takes one parameter; the name of the Docker container to restart. It uses the AWS-RunShellScript method to invoke the command. The output is collected automatically by the service and returned to the caller. For an example of the latest document schema, see Creating Systems Manager Documents.

{
  "schemaVersion":"1.2",
  "description":"Restart the specified docker container.",
  "parameters":{
    "param":{
      "type":"String",
      "description":"(Required) name of the container to restart.",
      "maxChars":1024
    }
  },
  "runtimeConfig":{
    "aws:runShellScript":{
      "properties":[
        {
          "id":"0.aws:runShellScript",
          "runCommand":[
            "docker restart {{param}}"
          ]
        }
      ]
    }
  }
}

Putting Run Command into practice at Pegasystems
The Pegasystems provisioning system sits on AWS CloudFormation, which is used to deploy and update Pega Cloud resources. Layered on top of it is the Pega Provisioning Engine, a serverless, Lambda-based service that manages a library of CloudFormation templates and Ansible playbooks.

A Configuration Management Database (CMDB) tracks all the configurations details and history of every deployment and update, and lays out its data using a hierarchical directory naming convention. The following diagram shows how the various systems are integrated:

For cloud system management, Pega operations uses a command line version called cuttysh and a graphical version based on the Pega 7 platform, called the Pega Operations Portal. Both tools allow you to browse the CMDB of deployed environments, view configuration settings, and interact with deployed EC2 instances through Run Command.

CLI Walkthrough
Here is a CLI walkthrough for looking into a customer deployment and interacting with instances using Run Command.

Launching the cuttysh tool brings you to the root of the CMDB and a list of the provisioned customers:

% cuttysh
d CUSTA
d CUSTB
d CUSTC
d CUSTD

You interact with the CMDB using standard Linux shell commands, such as cd, ls, cat, and grep. Items prefixed with s are services that have viewable properties. Items prefixed with d are navigable subdirectories in the CMDB hierarchy.

In this example, change directories into customer CUSTB’s portion of the CMDB hierarchy, and then further into a provisioned Pega environment called env1, under the Dev network. The tool displays the artifacts that are provisioned for that environment. These entries map to provisioned CloudFormation templates.

> cd CUSTB
/ROOT/CUSTB/us-east-1 > cd DEV/env1

The ls –l command shows the version of the provisioned resources. These version numbers map back to source control–managed artifacts for the CloudFormation, Ansible, and other components that compose a version of the Pega Cloud.

/ROOT/CUSTB/us-east-1/DEV/env1 > ls -l
s 1.2.5 RDSDatabase 
s 1.2.5 PegaAppTier 
s 7.2.1 Pega7 

Now, use Run Command to interact with the deployed environments. To do this, use the attach command and specify the service with which to interact. In the following example, you attach to the Pega Web Tier. Using the information in the CMDB and instance tags, the CLI finds the corresponding EC2 instances and displays some basic information about them. This deployment has three instances.

/ROOT/CUSTB/us-east-1/DEV/env1 > attach PegaWebTier
 # ID         State  Public Ip    Private Ip  Launch Time
 0 i-0cf0e84 running 52.63.216.42 10.96.15.70 2017-01-16 
 1 i-0043c1d running 53.47.191.22 10.96.15.43 2017-01-16 
 2 i-09b879e running 55.93.118.27 10.96.15.19 2017-01-16 

From here, you can use the run command to invoke Run Command documents. In the following example, you run the docker-ps document against instance 0 (the first one on the list). EC2 executes the command and returns the output to the CLI, which in turn shows it.

/ROOT/CUSTB/us-east-1/DEV/env1 > run 0 docker-ps
. . 
CONTAINER ID IMAGE             CREATED      STATUS        NAMES
2f187cc38c1  pega-7.2         10 weeks ago  Up 8 weeks    pega-web

Using the same command and some of the other documents that have been defined, you can restart a Docker container or even pull back the contents of a file to your local system. When you get a file, Run Command also leaves a copy in an S3 bucket in case you want to pass the link along to a colleague.

/ROOT/CUSTB/us-east-1/DEV/env1 > run 0 docker-restart pega-web
..
pega-web

/ROOT/CUSTB/us-east-1/DEV/env1 > run 0 get-file /var/log/cfn-init-cmd.log
. . . . . 
get-file

Data has been copied locally to: /tmp/get-file/i-0563c9e/data
Data is also available in S3 at: s3://my-bucket/CUSTB/cuttysh/get-file/data

Now, leverage the Run Command ability to do more than one thing at a time. In the following example, you attach to a deployment with three running instances and want to see the uptime for each instance. Using the par (parallel) option for run, the CLI tells Run Command to execute the uptime document on all instances in parallel.

/ROOT/CUSTB/us-east-1/DEV/env1 > run par uptime
 …
Output for: i-006bdc991385c33
 20:39:12 up 15 days, 3:54, 0 users, load average: 0.42, 0.32, 0.30

Output for: i-09390dbff062618
 20:39:12 up 15 days, 3:54, 0 users, load average: 0.08, 0.19, 0.22

Output for: i-08367d0114c94f1
 20:39:12 up 15 days, 3:54, 0 users, load average: 0.36, 0.40, 0.40

Commands are complete.
/ROOT/PEGACLOUD/CUSTB/us-east-1/PROD/prod1 > 

Summary
Run Command improves productivity by giving you faster access to systems and the ability to run operations across a group of instances. Pega Cloud operations has integrated Run Command with other operational tools to provide a clean and secure method for managing systems. This greatly improves operational efficiency, and gives greater control over who can do what in managed deployments. The Pega continual improvement process regularly assesses why operators need access, and turns those operations into new Run Command documents to be added to the library. In fact, their long-term goal is to stop deploying cloud systems with SSH enabled.

If you have any questions or suggestions, please leave a comment for us!

— Ananth and Rich

How to Deploy Local Administrator Password Solution with AWS Microsoft AD

Post Syndicated from Dragos Madarasan original https://aws.amazon.com/blogs/security/how-to-deploy-local-administrator-password-solution-with-aws-microsoft-ad/

Local Administrator Password Solution (LAPS) from Microsoft simplifies password management by allowing organizations to use Active Directory (AD) to store unique passwords for computers. Typically, an organization might reuse the same local administrator password across the computers in an AD domain. However, this approach represents a security risk because it can be exploited during lateral escalation attacks. LAPS solves this problem by creating unique, randomized passwords for the Administrator account on each computer and storing it encrypted in AD.

Deploying LAPS with AWS Microsoft AD requires the following steps:

  1. Install the LAPS binaries on instances joined to your AWS Microsoft AD domain. The binaries add additional client-side extension (CSE) functionality to the Group Policy client.
  2. Extend the AWS Microsoft AD schema. LAPS requires new AD attributes to store an encrypted password and its expiration time.
  3. Configure AD permissions and delegate the ability to retrieve the local administrator password for IT staff in your organization.
  4. Configure Group Policy on instances joined to your AWS Microsoft AD domain to enable LAPS. This configures the Group Policy client to process LAPS settings and uses the binaries installed in Step 1.

The following diagram illustrates the setup that I will be using throughout this post and the associated tasks to set up LAPS. Note that the AWS Directory Service directory is deployed across multiple Availability Zones, and monitoring automatically detects and replaces domain controllers that fail.

Diagram illustrating this blog post's solution

In this blog post, I explain the prerequisites to set up Local Administrator Password Solution, demonstrate the steps involved to update the AD schema on your AWS Microsoft AD domain, show how to delegate permissions to IT staff and configure LAPS via Group Policy, and demonstrate how to retrieve the password using the graphical user interface or with Windows PowerShell.

This post assumes you are familiar with Lightweight Directory Access Protocol Data Interchange Format (LDIF) files and AWS Microsoft AD. If you need more of an introduction to Directory Service and AWS Microsoft AD, see How to Move More Custom Applications to the AWS Cloud with AWS Directory Service, which introduces working with schema changes in AWS Microsoft AD.

Prerequisites

In order to implement LAPS, you must use AWS Directory Service for Microsoft Active Directory (Enterprise Edition), also known as AWS Microsoft AD. Any instance on which you want to configure LAPS must be joined to your AWS Microsoft AD domain. You also need a Management instance on which you install the LAPS management tools.

In this post, I use an AWS Microsoft AD domain called example.com that I have launched in the EU (London) region. To see which the regions in which Directory Service is available, see AWS Regions and Endpoints.

Screenshot showing the AWS Microsoft AD domain example.com used in this blog post

In addition, you must have at least two instances launched in the same region as the AWS Microsoft AD domain. To join the instances to your AWS Microsoft AD domain, you have two options:

  1. Use the Amazon EC2 Systems Manager (SSM) domain join feature. To learn more about how to set up domain join for EC2 instances, see joining a Windows Instance to an AWS Directory Service Domain.
  2. Manually configure the DNS server addresses in the Internet Protocol version 4 (TCP/IPv4) settings of the network card to use the AWS Microsoft AD DNS addresses (172.31.9.64 and 172.31.16.191, for this blog post) and perform a manual domain join.

For the purpose of this post, my two instances are:

  1. A Management instance on which I will install the management tools that I have tagged as Management.
  2. A Web Server instance on which I will be deploying the LAPS binary.

Screenshot showing the two EC2 instances used in this post

Implementing the solution

 

1. Install the LAPS binaries on instances joined to your AWS Microsoft AD domain by using EC2 Run Command

LAPS binaries come in the form of an MSI installer and can be downloaded from the Microsoft Download Center. You can install the LAPS binaries manually, with an automation service such as EC2 Run Command, or with your existing software deployment solution.

For this post, I will deploy the LAPS binaries on my Web Server instance (i-0b7563d0f89d3453a) by using EC2 Run Command:

  1. While signed in to the AWS Management Console, choose EC2. In the Systems Manager Services section of the navigation pane, choose Run Command.
  2. Choose Run a command, and from the Command document list, choose AWS-InstallApplication.
  3. From Target instances, choose the instance on which you want to deploy the LAPS binaries. In my case, I will be selecting the instance tagged as Web Server. If you do not see any instances listed, make sure you have met the prerequisites for Amazon EC2 Systems Manager (SSM) by reviewing the Systems Manager Prerequisites.
  4. For Action, choose Install, and then stipulate the following values:
    • Parameters: /quiet
    • Source: https://download.microsoft.com/download/C/7/A/C7AAD914-A8A6-4904-88A1-29E657445D03/LAPS.x64.msi
    • Source Hash: f63ebbc45e2d080630bd62a195cd225de734131a56bb7b453c84336e37abd766
    • Comment: LAPS deployment

Leave the other options with the default values and choose Run. The AWS Management Console will return a Command ID, which will initially have a status of In Progress. It should take less than 5 minutes to download and install the binaries, after which the Command ID will update its status to Success.

Status showing the binaries have been installed successfully

If the Command ID runs for more than 5 minutes or returns an error, it might indicate a problem with the installer. To troubleshoot, review the steps in Troubleshooting Systems Manager Run Command.

To verify the binaries have been installed successfully, open Control Panel and review the recently installed applications in Programs and Features.

Screenshot of Control Panel that confirms LAPS has been installed successfully

You should see an entry for Local Administrator Password Solution with a version of 6.2.0.0 or newer.

2. Extend the AWS Microsoft AD schema

In the previous section, I used EC2 Run Command to install the LAPS binaries on an EC2 instance. Now, I am ready to extend the schema in an AWS Microsoft AD domain. Extending the schema is a requirement because LAPS relies on new AD attributes to store the encrypted password and its expiration time.

In an on-premises AD environment, you would update the schema by running the Update-AdmPwdADSchema Windows PowerShell cmdlet with schema administrator credentials. Because AWS Microsoft AD is a managed service, I do not have permissions to update the schema directly. Instead, I will update the AD schema from the Directory Service console by importing an LDIF file. If you are unfamiliar with schema updates or LDIF files, see How to Move More Custom Applications to the AWS Cloud with AWS Directory Service.

To make things easier for you, I am providing you with a sample LDIF file that contains the required AD schema changes. Using Notepad or a similar text editor, open the SchemaChanges-0517.ldif file and update the values of dc=example,dc=com with your own AWS Microsoft AD domain and suffix.

After I update the LDIF file with my AWS Microsoft AD details, I import it by using the AWS Management Console:

  1. On the Directory Service console, select from the list of directories in the Microsoft AD directory by choosing its identifier (it will look something like d-534373570ea).
  2. On the Directory details page, choose the Schema extensions tab and choose Upload and update schema.
    Screenshot showing the "Upload and update schema" option
  3. When prompted for the LDIF file that contains the changes, choose the sample LDIF file.
  4. In the background, the LDIF file is validated for errors and a backup of the directory is created for recovery purposes. Updating the schema might take a few minutes and the status will change to Updating Schema. When the process has completed, the status of Completed will be displayed, as shown in the following screenshot.

Screenshot showing the schema updates in progress
When the process has completed, the status of Completed will be displayed, as shown in the following screenshot.

Screenshot showing the process has completed

If the LDIF file contains errors or the schema extension fails, the Directory Service console will generate an error code and additional debug information. To help troubleshoot error messages, see Schema Extension Errors.

The sample LDIF file triggers AWS Microsoft AD to perform the following actions:

  1. Create the ms-Mcs-AdmPwd attribute, which stores the encrypted password.
  2. Create the ms-Mcs-AdmPwdExpirationTime attribute, which stores the time of the password’s expiration.
  3. Add both attributes to the Computer class.

3. Configure AD permissions

In the previous section, I updated the AWS Microsoft AD schema with the required attributes for LAPS. I am now ready to configure the permissions for administrators to retrieve the password and for computer accounts to update their password attribute.

As part of configuring AD permissions, I grant computers the ability to update their own password attribute and specify which security groups have permissions to retrieve the password from AD. As part of this process, I run Windows PowerShell cmdlets that are not installed by default on Windows Server.

Note: To learn more about Windows PowerShell and the concept of a cmdlet (pronounced “command-let”), go to Getting Started with Windows PowerShell.

Before getting started, I need to set up the required tools for LAPS on my Management instance, which must be joined to the AWS Microsoft AD domain. I will be using the same LAPS installer that I downloaded from the Microsoft LAPS website. In my Management instance, I have manually run the installer by clicking the LAPS.x64.msi file. On the Custom Setup page of the installer, under Management Tools, for each option I have selected Install on local hard drive.

Screenshot showing the required management tools

In the preceding screenshot, the features are:

  • The fat client UI – A simple user interface for retrieving the password (I will use it at the end of this post).
  • The Windows PowerShell module – Needed to run the commands in the next sections.
  • The GPO Editor templates – Used to configure Group Policy objects.

The next step is to grant computers in the Computers OU the permission to update their own attributes. While connected to my Management instance, I go to the Start menu and type PowerShell. In the list of results, right-click Windows PowerShell and choose Run as administrator and then Yes when prompted by User Account Control.

In the Windows PowerShell prompt, I type the following command.

Import-module AdmPwd.PS

Set-AdmPwdComputerSelfPermission –OrgUnit “OU=Computers,OU=MyMicrosoftAD,DC=example,DC=com

To grant the administrator group called Admins the permission to retrieve the computer password, I run the following command in the Windows PowerShell prompt I previously started.

Import-module AdmPwd.PS

Set-AdmPwdReadPasswordPermission –OrgUnit “OU=Computers, OU=MyMicrosoftAD,DC=example,DC=com” –AllowedPrincipals “Admins”

4. Configure Group Policy to enable LAPS

In the previous section, I deployed the LAPS management tools on my management instance, granted the computer accounts the permission to self-update their local administrator password attribute, and granted my Admins group permissions to retrieve the password.

Note: The following section addresses the Group Policy Management Console and Group Policy objects. If you are unfamiliar with or wish to learn more about these concepts, go to Get Started Using the GPMC and Group Policy for Beginners.

I am now ready to enable LAPS via Group Policy:

  1. On my Management instance (i-03b2c5d5b1113c7ac), I have installed the Group Policy Management Console (GPMC) by running the following command in Windows PowerShell.
Install-WindowsFeature –Name GPMC
  1. Next, I have opened the GPMC and created a new Group Policy object (GPO) called LAPS GPO.
  2. In the Local Group Policy Editor, I navigate to Computer Configuration > Policies > Administrative Templates > LAPS. I have configured the settings using the values in the following table.

Setting

State

Options

Password Settings

Enabled

Complexity: large letters, small letters, numbers, specials

Do not allow password expiration time longer than required by policy

Enabled

N/A

Enable local admin password management

Enabled

N/A

  1. Next, I need to link the GPO to an organizational unit (OU) in which my machine accounts sit. In your environment, I recommend testing the new settings on a test OU and then deploying the GPO to production OUs.

Note: If you choose to create a new test organizational unit, you must create it in the OU that AWS Microsoft AD delegates to you to manage. For example, if your AWS Microsoft AD directory name were example.com, the test OU path would be example.com/example/Computers/Test.

  1. To test that LAPS works, I need to make sure the computer has received the new policy by forcing a Group Policy update. While connected to the Web Server instance (i-0b7563d0f89d3453a) using Remote Desktop, I open an elevated administrative command prompt and run the following command: gpupdate /force. I can check if the policy is applied by running the command: gpresult /r | findstr LAPS GPO, where LAPS GPO is the name of the GPO created in the second step.
  2. Back on my Management instance, I can then launch the LAPS interface from the Start menu and use it to retrieve the password (as shown in the following screenshot). Alternatively, I can run the Get-ADComputer Windows PowerShell cmdlet to retrieve the password.
Get-ADComputer [YourComputerName] -Properties ms-Mcs-AdmPwd | select name, ms-Mcs-AdmPwd

Screenshot of the LAPS UI, which you can use to retrieve the password

Summary

In this blog post, I demonstrated how you can deploy LAPS with an AWS Microsoft AD directory. I then showed how to install the LAPS binaries by using EC2 Run Command. Using the sample LDIF file I provided, I showed you how to extend the schema, which is a requirement because LAPS relies on new AD attributes to store the encrypted password and its expiration time. Finally, I showed how to complete the LAPS setup by configuring the necessary AD permissions and creating the GPO that starts the LAPS password change.

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 Directory Service forum.

– Dragos

New – AWS Management Tools Blog

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-aws-management-tools-blog/

The AWS Blog collection has grown over the past couple of years. As you can see from the list on the right, we now have blogs that cover a wide variety of topics and development tools. We also have blogs that are designed for those of you who read languages other than English!

The AWS Management Tools Blog is the newest member of the collection. This blog focuses on AWS tools that help you to provision, configure, monitor, track, audit, and manage the costs of your AWS and on-premises resources at scale. Topics planned for the blog include deep technical coverage of feature updates, tips and tricks, sample apps, CloudFormation templates, and an on-going discussion of use cases. Here are some of the initial posts:

You can subscribe to the blog’s RSS feed in order to make sure that you see all of this helpful new content!

Jeff;

 

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.

Image of lock and key

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.

Image of lock and key

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

EC2 Run Command is Now a CloudWatch Events Target

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-run-command-is-now-a-cloudwatch-events-target/

Ok, time for another peanut butter and chocolate post! Let’s combine EC2 Run Command (New EC2 Run Command – Remote Instance Management at Scale) and CloudWatch Events (New CloudWatch Events – Track and Respond to Changes to Your AWS Resources) and see what we get.

EC2 Run Command is part of EC2 Systems Manager. It allows you to operate on collections of EC2 instances and on-premises servers reliably and at scale, in a controlled and selective fashion. You can run scripts, install software, collect metrics and log files, manage patches, and much more, on both Windows and Linux.

CloudWatch Events gives you the ability to track changes to AWS resources in near real-time. You get a stream of system events that you can easily route to one or more targets including AWS Lambda functions, Amazon Kinesis streams, Amazon SNS topics, and built-in EC2 and EBS targets.

Better Together
Today we are bringing these two services together. You can now create CloudWatch Events rules that use EC2 Run Command to perform actions on EC2 instances or on-premises servers. This opens the door to all sorts of interesting ideas; here are a few that I came up with:

Final Log Collection – Collect application or system logs from instances that are being shut down (either manually or as a result of a scale-in operation initiated by Auto Scaling).

Error Log Condition – Collect logs after an application crash or a security incident.

Instance Setup – After an instance has started, download & install applications, set parameters and configurations, and launch processes.

Configuration Updates – When a config file is changed in S3, install it on applicable instances (perhaps determined by tags). For example, you could install an updated Apache web server config file on a set of properly tagged instances, and then restart the server so that it picks up the changes. Or, update an instance-level firewall each time the AWS IP Address Ranges are updated.

EBS Snapshot Testing and Tracking – After a fresh snapshot has been created, mount it on a test instance, check the filesystem for errors, and then index the files in the snapshot.

Instance Coordination – Every time an instance is launched or terminated, inform the others so that they can update internal tracking information or rebalance their workloads.

I’m sure that you have some more interesting ideas; please feel free to share them in the comments.

Time for Action!
Let’s set this up. Suppose I want to run a specific PowerShell script every time Auto Scaling adds another instance to an Auto Scaling Group.

I start by opening the CloudWatch Events Console and clicking on Create rule:

I configure my Event Source to be my Auto Scaling Group (AS-Main-1), and indicate that I want to take action when EC2 instances are launched successfully:

Then I set up the target. I choose SSM Run Command, pick the AWS-RunShellScript document, and indicate that I want the command to be run on the instances that are tagged as coming from my Auto Scaling group:

Then I click on Configure details, give my rule a name and a description, and click on Create rule:

With everything set up, the command service httpd start will be run on each instance launched as a result of a scale out operation.

Available Now
This new feature is available now and you can start using it today.

Jeff;

 

Streamline AMI Maintenance and Patching Using Amazon EC2 Systems Manager | Automation

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/streamline-ami-maintenance-and-patching-using-amazon-ec2-systems-manager-automation/

Here to tell you about using Automation for streamline AMI maintenance and patching is Taylor Anderson, a Senior Product Manager with EC2.

-Ana


 

Last December at re:Invent, we launched Amazon EC2 Systems Manager, which helps you automatically collect software inventory, apply OS patches, create system images, and configure Windows and Linux operating systems. These capabilities enable automated configuration and ongoing management of systems at scale, and help maintain software compliance for instances running in Amazon EC2 or on-premises.

One feature within Systems Manager is Automation, which can be used to patch, update agents, or bake applications into an Amazon Machine Image (AMI). With Automation, you can avoid the time and effort associated with manual image updates, and instead build AMIs through a streamlined, repeatable, and auditable process.

Recently, we released the first public document for Automation: AWS-UpdateLinuxAmi. This document allows you to automate patching of Ubuntu, CentOS, RHEL, and Amazon Linux AMIs, as well as automating the installation of additional site-specific packages and configurations.

More importantly, it makes it easy to get started with Automation, eliminating the need to first write an Automation document. AWS-UpdateLinuxAmi can also be used as a template when building your own Automation workflow. Windows users can expect the equivalent document―AWS-UpdateWindowsAmi―in the coming weeks.

AWS-UpdateLinuxAmi automates the following workflow:

  1. Launch a temporary EC2 instance from a source Linux AMI.
  2. Update the instance.
    • Invoke a user-provided, pre-update hook script on the instance (optional).
    • Update any AWS tools and agents on the instance, if present.
    • Update the instance’s distribution packages using the native package manager.
    • Invoke a user-provided post-update hook script on the instance (optional).
  3. Stop the temporary instance.
  4. Create a new AMI from the stopped instance.
  5. Terminate the instance.

Warning: Creation of an AMI from a running instance carries a risk that credentials, secrets, or other confidential information from that instance may be recorded to the new image. Use caution when managing AMIs created by this process.

Configuring roles and permissions for Automation

If you haven’t used Automation before, you need to configure IAM roles and permissions. This includes creating a service role for Automation, assigning a passrole to authorize a user to provide the service role, and creating an instance role to enable instance management under Systems Manager. For more details, see Configuring Access to Automation.

Executing Automation

      1. In the EC2 console, choose Systems Manager Services, Automations.
      2. Choose Run automation document
      3. Expand Document name and choose AWS-UpdateLinuxAmi.
      4. Choose the latest document version.
      5.  For SourceAmiId, enter the ID of the Linux AMI to update.
      6. For InstanceIamRole, enter the name of the instance role you created enabling Systems Manager to manage an instance (that is, it includes the AmazonEC2RoleforSSM managed policy). For more details, see Configuring Access to Automation.
      7.  For AutomationAssumeRole, enter the ARN of the service role you created for Automation. For more details, see Configuring Access to Automation.
      8.  Choose Run Automation.
      9. Monitor progress in the Automation Steps tab, and view step-level outputs.

After execution is complete, choose Description to view any outputs returned by the workflow. In this example, AWS-UpdateLinuxAmi returns the new AMI ID.

Next, choose Images, AMIs to view your new AMI.

There is no additional charge to use the Automation service, and any resources created by a workflow incur nominal charges. Note that if you terminate AWS-UpdateLinuxAmi before reaching the “Terminate Instance” step, shut down the temporary instance created by the workflow.

A CLI walkthrough of the above steps can be found at Automation CLI Walkthrough: Patch a Linux AMI.

Conclusion

Now that you’ve successfully run AWS-UpdateLinuxAmi, you may want to create default values for the service and instance roles. You can customize your workflow by creating your own Automation document based on AWS-UpdateLinuxAmi. For more details, see Create an Automaton Document. After you’ve created your document, you can write additional steps and add them to the workflow.

Example steps include:

      • Updating an Auto Scaling group with the new AMI ID (aws:invokeLambdaFunction action type)
      • Creating an encrypted copy of your new AMI (aws:encrypedCopy action type)
      • Validating your new AMI using Run Command with the RunPowerShellScript document (aws:runCommand action type)

Automation also makes a great addition to a CI/CD pipeline for application bake-in, and can be invoked as a CLI build step in Jenkins. For details on these examples, be sure to check out the Automation technical documentation. For updates on Automation, Amazon EC2 Systems Manager, Amazon CloudFormation, AWS Config, AWS OpsWorks and other management services, be sure to follow the all-new Management Tools blog.

 

AWS Week in Review – March 6, 2017

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-week-in-review-march-6-2017/

This edition includes all of our announcements, content from all of our blogs, and as much community-generated AWS content as I had time for!

Monday

March 6

Tuesday

March 7

Wednesday

March 8

Thursday

March 9

Friday

March 10

Saturday

March 11

Sunday

March 12

Jeff;

 

Automating the Creation of Consistent Amazon EBS Snapshots with Amazon EC2 Systems Manager (Part 2)

Post Syndicated from Bryan Liston original https://aws.amazon.com/blogs/compute/automating-the-creation-of-consistent-amazon-ebs-snapshots-with-amazon-ec2-systems-manager-part-2/

Nicolas Malaval, AWS Professional Consultant

In my previous blog post, I discussed the challenge of creating Amazon EBS snapshots when you cannot turn off the instance during backup because this might exclude any data that has been cached by any applications or the operating system. I showed how you can use EC2 Systems Manager to run a script remotely on EC2 instances to prepare the applications and the operating system for backup and to automate the creating of snapshots on a daily basis. I gave a practical example of creating consistent Amazon EBS snapshots of Amazon Linux running a MySQL database.

In this post, I walk you through another practical example to create consistent snapshots of a Windows Server instance with Microsoft VSS (Volume Shadow Copy Service).

Understanding the example

VSS (Volume Shadow Copy Service) is a Windows built-in service that coordinates backup of VSS-compatible applications (SQL Server, Exchange Server, etc.) to flush and freeze their I/O operations.

The VSS service initiates and oversees the creation of shadow copies. A shadow copy is a point-in-time and consistent snapshot of a logical volume. For example, C: is a logical volume, which is different than an EBS snapshot. Multiple components are involved in the shadow copy creation:

  • The VSS requester requests the creation of shadow copies.
  • The VSS provider creates and maintains the shadow copies.
  • The VSS writers guarantee that you have a consistent data set to back up. They flush and freeze I/O operations, before the VSS provider creates the shadow copies, and release I/O operations, after the VSS provider has completed this action. There is usually one VSS writer for each VSS-compatible application.

I use Run Command to execute a PowerShell script on the Windows instance:

$EbsSnapshotPsFileName = "C:/tmp/ebsSnapshot.ps1"

$EbsSnapshotPs = New-Item -Type File $EbsSnapshotPsFileName -Force

Add-Content $EbsSnapshotPs '$InstanceID = Invoke-RestMethod -Uri http://169.254.169.254/latest/meta-data/instance-id'
Add-Content $EbsSnapshotPs '$AZ = Invoke-RestMethod -Uri http://169.254.169.254/latest/meta-data/placement/availability-zone'
Add-Content $EbsSnapshotPs '$Region = $AZ.Substring(0, $AZ.Length-1)'
Add-Content $EbsSnapshotPs '$Volumes = ((Get-EC2InstanceAttribute -Region $Region -Instance "$InstanceId" -Attribute blockDeviceMapping).BlockDeviceMappings.Ebs |? {$_.Status -eq "attached"}).VolumeId'
Add-Content $EbsSnapshotPs '$Volumes | New-EC2Snapshot -Region $Region -Description " Consistent snapshot of a Windows instance with VSS" -Force'
Add-Content $EbsSnapshotPs 'Exit $LastExitCode'

First, the script writes in a local file named ebsSnapshot.ps1 a PowerShell script that creates a snapshot of every EBS volume attached to the instance.

$EbsSnapshotCmdFileName = "C:/tmp/ebsSnapshot.cmd"
$EbsSnapshotCmd = New-Item -Type File $EbsSnapshotCmdFileName -Force

Add-Content $EbsSnapshotCmd 'powershell.exe -ExecutionPolicy Bypass -file $EbsSnapshotPsFileName'
Add-Content $EbsSnapshotCmd 'exit $?'

It writes in a second file named ebsSnapshot.cmd a shell script that executes the PowerShell script created earlier.

$VssScriptFileName = "C:/tmp/scriptVss.txt"
$VssScript = New-Item -Type File $VssScriptFileName -Force

Add-Content $VssScript 'reset'
Add-Content $VssScript 'set context persistent'
Add-Content $VssScript 'set option differential'
Add-Content $VssScript 'begin backup'

$Drives = Get-WmiObject -Class Win32_LogicalDisk |? {$_.VolumeName -notmatch "Temporary" -and $_.DriveType -eq "3"} | Select-Object DeviceID

$Drives | ForEach-Object { Add-Content $VssScript $('add volume ' + $_.DeviceID + ' alias Volume' + $_.DeviceID.Substring(0, 1)) }

Add-Content $VssScript 'create'
Add-Content $VssScript "exec $EbsSnapshotCmdFileName"
Add-Content $VssScript 'end backup'

$Drives | ForEach-Object { Add-Content $VssScript $('delete shadows id %Volume' + $_.DeviceID.Substring(0, 1) + '%') }

Add-Content $VssScript 'exit'

It creates a third file named scriptVss.txt containing DiskShadow commands. DiskShadow is a tool included in Windows Server 2008 and above, that exposes the functionality offered by the VSS service. The script creates a shadow copy of each logical volume stored on EBS, runs the shell script ebsSnapshot.cmd to create a snapshot of underlying EBS volumes, and then deletes the shadow copies to free disk space.

diskshadow.exe /s $VssScriptFileName
Exit $LastExitCode

Finally, it runs DiskShadow in script mode.

This PowerShell script is contained in a new SSM document and the maintenance window executes a command from this document every day at midnight on every Windows instance that has a tag “ConsistentSnapshot” equal to “WindowsVSS”.

Implementing and testing the example

First, use AWS CloudFormation to provision some of the required resources in your AWS account.

  1. Open Create a Stack to create a CloudFormation stack from the template.
  2. Choose Next.
  3. Enter the ID of the latest AWS Windows Server 2016 Base AMI available in the current region (see Finding a Windows AMI) in pWindowsAmiId.
  4. Follow the on-screen instructions.

CloudFormation creates the following resources:

  • A VPC with an Internet gateway attached.
  • A subnet on this VPC with a new route table, to enable access to the Internet and therefore to the AWS APIs.
  • An IAM role to grant an EC2 instance the required permissions.
  • A security group that allows RDP access from the Internet, as you need to log on to the instance later on.
  • A Windows instance in the subnet with the IAM role and the security group attached.
  • A SSM document containing the script described in the section above to create consistent EBS snapshots.
  • Another SSM document containing a script to restore logical volumes to a consistent state, as explained in the next section.
  • An IAM role to grant the maintenance window the required permissions.

After the stack creation completes, choose Outputs in the CloudFormation console and note the values returned:

  • IAM role for the maintenance window
  • Names of the two SSM documents

Then, manually create a maintenance window, if you have not already created it. For detailed instructions, see the “Example” section in the previous blog post.

After you create a maintenance window, assign a target where the task will run:

  1. In the Maintenance Window list, choose the maintenance window that you just created.
  2. For Actions, choose Register targets.
  3. For Owner information, enter WindowsVSS.
  4. Under the Select targets by section, choose Specifying tags. For Tag Name, choose ConsistentSnapshot. For Tag Value, choose WindowsVSS.
  5. Choose Register targets.

Finally, assign a task to perform during the window:

  1. In the Maintenance Window list, choose the maintenance window that you just created.
  2. For Actions, choose Register tasks.
  3. For Document, select the name of the SSM document that was returned by CloudFormation, with which to create snapshots.
  4. Under the Target by section, choose the target that you just created.
  5. Under the Role section, select the IAM role that was returned by CloudFormation.
  6. Under Execute on, for Targets, enter 1. For Stop after, enter 1 errors.
  7. Choose Register task.

You can view the history either in the History tab of the Maintenance Windows navigation pane of the Amazon EC2 console, as illustrated on the following figure, or in the Run Command navigation pane, with more details about each command executed.

Restoring logical volumes to a consistent state

DiskShadow―the VSS requester in this case―uses the Windows built-in VSS provider. To create a shadow copy, this built-in provider does not make a complete copy of the data. Instead, it keeps a copy of a block data before a change overwrites it, in a dedicated storage area. The logical volume can be restored to its initial consistent state, by combining the actual volume data with the initial data of the changed blocks.

The DiskShadow command create instructs the VSS service to proceed with the creation of shadow copies, including the release of I/O operations by the VSS writers after the shadow copies are created. Therefore, the EBS snapshots created by the next command exec may not be fully consistent.

Note: A workaround could be to build your own VSS provider in charge of creating EBS snapshots. Doing so would enable the EBS snapshots to be created before I/O operations are released. We will not develop this solution in this blog post.

Therefore, you need to “undo” any I/O operations that may have happened between the moment when the shadow copy was created and the moment when the EBS snapshots were created.

A solution consists of creating an EBS volume from the snapshot, attaching it to an intermediate Windows instance and to “revert” the VSS shadow copy to restore the EBS volume to a consistent state. For sake of simplicity, use the Windows instance that was backed up as the intermediate instance.

To manually restore an EBS snapshot to a consistent state:

  1. In the Amazon EC2 console, choose Instances.
  2. In the search box, enter Consistent EBS Snapshots – Windows with VSS. The search results should display a single instance. Note the Availability Zone for this instance.
  3. Choose Snapshots.
  4. Select the latest snapshot with the description “Consistent snapshot of Windows with VSS” and choose Actions, Create Volume.
  5. Select the same Availability Zone as the instance and choose Create, Volumes.
  6. Select the volume that was just created and choose Actions, Attach Volume.
  7. For Instance, choose Consistent EBS Snapshots – Windows with VSS and choose Attach.
  8. Choose Run Command, Run a command.
  9. In Command document, select the name of a SSM document to restore snapshots returned by CloudFormation. For Target instances, select the Windows and choose Run.

Run Command executes the following PowerShell script on the Windows instance. It retrieves the list of offline disks—which corresponds in this case to the EBS volume that you just attached—and for each offline disk, takes it online, revert existing shadow copies and takes it offline again.

$OfflineDisks = (Get-Disk |? {$_.OperationalStatus -eq "Offline"})

foreach ($OfflineDisk in $OfflineDisks) {
  Set-Disk -Number $OfflineDisk.Number -IsOffline $False
  Set-Disk -Number $OfflineDisk.Number -IsReadonly $False
  Write-Host "Disk " $OfflineDisk.Signature " is now online"
}

$ShadowCopyIds = (Get-CimInstance Win32_ShadowCopy).Id
Write-Host "Number of shadow copies found: " $ShadowCopyIds.Count

foreach ($ShadowCopyId in $ShadowCopyIds) {
  "revert " + $ShadowCopyId | diskshadow
}

foreach ($OfflineDisk in $OfflineDisks) {
  $CurrentSignature = (Get-Disk -Number $OfflineDisk.Number).Signature
  if ($OfflineDisk.Signature -eq $CurrentSignature) {
    Set-Disk -Number $OfflineDisk.Number -IsReadonly $True
    Set-Disk -Number $OfflineDisk.Number -IsOffline $True
    Write-Host "Disk " $OfflineDisk.Number " is now offline"
  }
  else {
    Set-Disk -Number $OfflineDisk.Number -Signature $OfflineDisk.Signature
    Write-Host "Reverting to the initial disk signature: " $OfflineDisk.Signature
  }
}

The EBS volume is now in a consistent state and can be detached from the intermediate instance.

Conclusion

In this series of blog posts, I showed how you can use Amazon EC2 Systems Manager to create consistent EBS snapshots on a daily basis, with two practical examples for Linux and Windows. You can adapt this solution to your own requirements. For example, you may develop scripts for your own applications.

If you have questions or suggestions, please comment below.

Amazon EBS Update – New Elastic Volumes Change Everything

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-ebs-update-new-elastic-volumes-change-everything/

It is always interesting to speak with our customers and to learn how the dynamic nature of their business and their applications drives their block storage requirements. These needs change over time, creating the need to modify existing volumes to add capacity or to change performance characteristics. Today’s 24×7 operating models leaves no room for downtime; as a result, customers want to make changes without going offline or otherwise impacting operations.

Over the years, we have introduced new EBS offerings that support an ever-widening set of use cases. For example, we introduced two new volume types in 2016 – Throughput Optimized HDD (st1) and Cold HDD (sc1). Our customers want to use these volume types as storage tiers, modifying the volume type to save money or to change the performance characteristics, without impacting operations.

In other words, our customers want their EBS volumes to be even more elastic!

New Elastic Volumes
Today we are launching a new EBS feature we call Elastic Volumes and making it available for all current-generation EBS volumes attached to current-generation EC2 instances. You can now increase volume size, adjust performance, or change the volume type while the volume is in use. You can continue to use your application while the change takes effect.

This new feature will greatly simplify (or even eliminate) many of your planning, tuning, and space management chores. Instead of a traditional provisioning cycle that can take weeks or months, you can make changes to your storage infrastructure instantaneously, with a simple API call.

You can address the following scenarios (and many more that you can come up with on your own) using Elastic Volumes:

Changing Workloads – You set up your infrastructure in a rush and used the General Purpose SSD volumes for your block storage. After gaining some experience you figure out that the Throughput Optimized volumes are a better fit, and simply change the type of the volume.

Spiking Demand – You are running a relational database on a Provisioned IOPS volume that is set to handle a moderate amount of traffic during the month, with a 10x spike in traffic  during the final three days of each month due to month-end processing.  You can use Elastic Volumes to dial up the provisioning in order to handle the spike, and then dial it down afterward.

Increasing Storage – You provisioned a volume for 100 GiB and an alarm goes off indicating that it is now at 90% of capacity. You increase the size of the volume and expand the file system to match, with no downtime, and in a fully automated fashion.

Using Elastic Volumes
You can manage all of this from the AWS Management Console, via API calls, or from the AWS Command Line Interface (CLI).

To make a change from the Console, simply select the volume and choose Modify Volume from the Action menu:

Then make any desired changes to the volume type, size, and Provisioned IOPS (if appropriate). Here I am changing my 75 GiB General Purpose (gp2) volume into a 400 GiB Provisioned IOPS volume, with 20,000 IOPS:

When I click on Modify I confirm my intent, and click on Yes:

The volume’s state reflects the progress of the operation (modifying, optimizing, or complete):

The next step is to expand the file system so that it can take advantage of the additional storage space. To learn how to do that, read Expanding the Storage Space of an EBS Volume on Linux or Expanding the Storage Space of an EBS Volume on Windows. You can expand the file system as soon as the state transitions to optimizing (typically a few seconds after you start the operation). The new configuration is in effect at this point, although optimization may continue for up to 24 hours. Billing for the new configuration begins as soon as the state turns to optimizing (there’s no charge for the modification itself).

Automatic Elastic Volume Operations
While manual changes are fine, there’s plenty of potential for automation. Here are a couple of ideas:

Right-Sizing – Use a CloudWatch alarm to watch for a volume that is running at or near its IOPS limit. Initiate a workflow and approval process that could provision additional IOPS or change the type of the volume. Or, publish a “free space” metric to CloudWatch and use a similar approval process to resize the volume and the filesystem.

Cost Reduction – Use metrics or schedules to reduce IOPS or to change the type of a volume. Last week I spoke with a security auditor at a university. He collects tens of gigabytes of log files from all over campus each day and retains them for 60 days. Most of the files are never read, and those that are can be scanned at a leisurely pace. They could address this use case by creating a fresh General Purpose volume each day, writing the logs to it at high speed, and then changing the type to Throughput Optimized.

As I mentioned earlier, you need to resize the file system in order to be able to access the newly provisioned space on the volume. In order to show you how to automate this process, my colleagues built a sample that makes use of CloudWatch Events, AWS Lambda, EC2 Systems Manager, and some PowerShell scripting. The rule matches the modifyVolume event emitted by EBS and invokes the logEvents Lambda function:

The function locates the volume, confirms that it is attached to an instance that is managed by EC2 Systems Manager, and then adds a “maintenance tag” to the instance:

def lambda_handler(event, context):
    volume =(event['resources'][0].split('/')[1])
    attach=ec2.describe_volumes(VolumeIds=[volume])['Volumes'][0]['Attachments']
    if len(attach)>0: 
        instance = attach[0]['InstanceId']
        filter={'key': 'InstanceIds', 'valueSet': [instance]}
        info = ssm.describe_instance_information(InstanceInformationFilterList=[filter])['InstanceInformationList']
        if len(info)>0:
            ec2.create_tags(Resources=[instance],Tags=[tags])
            print (info[0]['PlatformName']+' Instance '+ instance+ ' has been tagged for maintenance' )

Later (either manually or on a schedule), EC2 Systems Manager is used to run a PowerShell script on all of the instances that are tagged for maintenance. The script looks at the instance’s disks and partitions, and resizes all of the drives (filesystems) to the maximum allowable size. Here’s an excerpt:

foreach ($DriveLetter in $DriveLetters) {
	$Error.Clear()
        $SizeMax = (Get-PartitionSupportedSize -DriveLetter $DriveLetter).SizeMax
}

To learn more, take a look at the [[Elastic Volume Sample]].

Available Today
The Elastic Volumes feature is available today and you can start using it right now!

To learn about some important special cases and a few limitations on instance types, read Considerations When Modifying EBS Volumes.

Jeff;

PS – If you would like to design and build cool, game-changing storage services like EBS, take a look at our EBS Jobs page!

 

How to Remediate Amazon Inspector Security Findings Automatically

Post Syndicated from Eric Fitzgerald original https://aws.amazon.com/blogs/security/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.

Use case

In this post’s example, I find a common vulnerability and exposure (CVE) for a missing update and use Lambda to call the Amazon EC2 Systems Manager to update the instance. However, this is just one use case and the underlying logic can be used for multiple cases such as software and application patching, kernel version updates, security permissions and roles changes, and configuration changes.

The solution

Overview

The solution in this blog post does the following:

  1. Launches a new Amazon EC2 instance, deploying the EC2 Simple Systems Manager (SSM) agent and its role to the instance.
  2. Deploys the Amazon Inspector agent to the instance by using EC2 Systems Manager.
  3. Creates an SNS topic to which Amazon Inspector will publish messages.
  4. Configures an Amazon Inspector assessment template to post finding notifications to the SNS topic.
  5. Creates the Lambda function that is triggered by notifications to the SNS topic and uses EC2 Systems Manager from within the Lambda function to perform automatic remediation on the instance.

1.  Launch an EC2 instance with EC2 Systems Manager enabled

In my previous Security Blog post, I discussed the use of EC2 user data to deploy the EC2 SSM agent to a Linux instance. To enable the type of autoremediation we are talking about, it is necessary to have the EC2 Systems Manager installed on your instances. If you already have EC2 Systems Manager installed on your instances, you can move on to Step 2. Otherwise, let’s take a minute to review how the process works:

  1. Create an AWS Identity and Access Management (IAM) role so that the on-instance EC2 SSM agent can communicate with EC2 Systems Manager. You can learn more about the process of creating a role while launching an instance.
  2. While launching the instance with the EC2 launch wizard, associate the role you just created with the new instance and provide the appropriate script as user data for your operating system and architecture to install the EC2 Systems Manager agent as the instance is launched. See the process and scripts.

Screenshot of configuring instance details

Note: You must change the scripts slightly when copying them from the instructions to the EC2 user data. The word region in the curl command must be replaced with the AWS region code (for example, us-east-1).

2.  Deploy the Amazon Inspector agent to the instance by using EC2 Systems Manager

You can deploy the Amazon Inspector agent with EC2 Systems Manager, with EC2 instance user data, or by connecting to an EC2 instance via SSH and running the installation steps manually. Because you just installed the EC2 SSM agent, you will use that method.

To deploy the Amazon Inspector agent:

  1. Navigate to the EC2 console in the desired region. In the navigation pane, choose Command History under Commands near the bottom of the list.
  2. Choose Run a command.
  3. Choose the AWS-RunShellScript command document, and then choose Select instances to specify the instance that you created previously. Note: If you do not see the instance in that list, you probably did not successfully install the EC2 SSM agent. This means you have to start over with the previous section. Common mistakes include failing to associate a role with the instance, failing to associate the correct policy with the role, or providing an incorrect user data script.
  4. Paste the following script in the Commands.
    #!/bin/bash
    cd /tmp
    curl -O https://d1wk0tztpsntt1.cloudfront.net/linux/latest/install
    chmod a+x /tmp/install
    bash /tmp/install

  5. Choose Run to execute the script on the instance.

Screenshot of deploying the Amazon Inspector agent

3.  Create an SNS topic to which Amazon Inspector will publish messages

Amazon SNS uses topics, communication channels for sending messages and subscribing to notifications. You will create an SNS topic for this solution to which Amazon Inspector publishes messages whenever there is a security finding. Later, you will create a Lambda function that subscribes to this topic and receives a notification whenever a new security finding is generated.

To create an SNS topic:

  1. In the AWS Management Console, navigate to the SNS console.
  2. Choose Create topic. Type a topic name and a display name, and choose Create topic.
  3. From the list of displayed topics, choose the topic that you just created by selecting the check box to the left of the topic name, and then choose Edit topic policy from the Other topic actions drop-down list.
  4. In the Advanced view tab, find the Principal section of the policy document. In that section, replace the line that says “AWS”: “*” with the following text: “Service”: “inspector.amazonaws.com” (see the following screenshot).
  5. Choose Update policy to save the changes.
  6. Choose Edit topic policy On the Basic view tab, set the topic policy to allow Only me (topic owner) to subscribe to the topic, and choose Update policy to save the changes.

Screenshot of editing the topic policy

4.  Configure an Amazon Inspector assessment template to post finding notifications to the SNS topic

An assessment template is a configuration that tells Amazon Inspector how to construct a specific security evaluation. For example, an assessment template can tell Amazon Inspector which EC2 instances to target and which rules packages to evaluate. You can configure a template to tell Amazon Inspector to generate SNS notifications when findings are identified. In order to enable automatic remediation, you either create a new template or modify an existing template to set up SNS notifications to the SNS topic that you just created.

To enable automatic remediation:

  1. Sign in to the AWS Management Console and navigate to the Amazon Inspector console.
  2. Choose Assessment templates in the navigation pane.
  3. Choose one of your existing Amazon Inspector assessment templates. If you need to create a new Amazon Inspector template, type a name for the template and choose the Common Vulnerabilities and Exposures rules package. Then go back to the list and select the template.
  4. Expand the template so that you can see all the settings by choosing the right-pointing arrowhead in the row for that template.
  5. Choose the pencil icon next to the SNS topics.
  6. Add the SNS topic that you created in the previous section by choosing it from the Select a new topic to notify of events drop-down list (see the following screenshot).
  7. Choose Save to save your changes.

Screenshot of configuring the SNS topic

5.  Create the Lambda autoremediation function

Now, create a Lambda function that listens for Amazon Inspector to notify it of new security findings, and then tells the EC2 SSM agent to run the appropriate system update command (apt-get update or yum update) if the finding is for an unpatched CVE vulnerability.

Step 1: Create an IAM role for the Lambda function to send EC2 Systems Manager commands

A Lambda function needs specific permissions to interact with your AWS resources. You provide these permissions in the form of an IAM role, and the role has a policy attached that permits the Lambda function to receive SNS notifications and to send commands to the Amazon Inspector agent via EC2 Systems Manager.

To create the IAM role:

  1. Sign in to the AWS Management Console, and navigate to the IAM console.
  2. Choose Roles in the navigation pane, and then choose Create new role.
  3. Type a name for the role. You should (but are not required to) use a descriptive name such as Amazon Inspector-agent-autodeploy-lambda. Regardless of the name you choose, remember the name because you will need it in the next section.
  4. Choose the AWS Lambda role type.
  5. Attach the policies AWSLambdaBasicExecutionRole and AmazonSSMFullAccess.
  6. Choose Create the role.

Step 2: Create the Lambda function that will update the host by sending the appropriate commands through EC2 Systems Manager

Now, create the Lambda function. You can download the source code for this function from the .zip file link in the following procedure. Some things to note about the function are:

  • The function listens for notifications on the configured SNS topic, but only acts on notifications that are from Amazon Inspector that report a finding and are reporting a CVE vulnerability.
  • The function checks to ensure that the EC2 SSM agent is installed, running, and healthy on the EC2 instance for which the finding was reported.
  • The function checks the operating system of the EC2 instance and determines if it is a supported Linux distribution (Ubuntu or Amazon Linux).
  • The function sends the distribution-appropriate package update command (apt-get update or yum update) to the EC2 instance via EC2 Systems Manager.
  • The function does not reboot the agent. You either have to add that functionality yourself or reboot the agent manually.

To create the Lambda function:

  1. Sign in to the AWS Management Console in the region that you intend to use, and navigate to the Lambda console.
  2. Choose Create a Lambda function.
  3. On the Select a blueprint page, choose the Hello World Python blueprint and choose Next.
  4. On the Configure triggers page, choose SNS as the trigger, and choose the SNS topic that you created in the last section. Choose the Enable trigger check box and choose Next.
  5. Type a name and description for the function. Choose Python 2.7 runtime.
  6. Download and save this .zip file.
  7. Unzip the .zip file, and copy the entire contents of lambda-auto-remediate.py to your clipboard.
  8. Choose Edit code inline under Code entry type in the Lambda function, and replace all the existing text with the text that you just copied from lambda-auto-remediate.py.
  9. Select Choose an existing role from the Role drop-down list, and then in the Existing role box, choose the IAM role that you created in Step 1 of this section.
  10. Choose Next and then Create function to complete the creation of the function.

You now have a working system that monitors Amazon Inspector for CVE findings and will patch affected Ubuntu or Amazon Linux instances automatically. You can view or modify the source code for the function in the Lambda console. Additionally, Lambda and EC2 Systems Manager will generate logs whenever the function causes an agent to patch itself.

Note: If you have multiple CVE findings for an instance, the remediation commands might be executed more than once, but the package managers for Linux handle this efficiently. You still have to reboot the instances yourself, but EC2 Systems Manager includes a feature to do that as well.

Summary

Using Amazon Inspector with Lambda allows you to automate certain security tasks. Because Lambda supports Python and JavaScript, development of such automation is similar to automating any other kind of administrative task via scripting. Even better, you can take actions on EC2 instances in response to Amazon Inspector findings by using Lambda to invoke EC2 Systems Manager. This enables you to take instance-specific actions based on issues that Amazon Inspector finds. Combining these capabilities allows you to build event-driven security automation to help better secure your AWS environment in near real time.

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

– Eric

How to Simplify Security Assessment Setup Using Amazon EC2 Systems Manager and Amazon Inspector

Post Syndicated from Eric Fitzgerald original https://aws.amazon.com/blogs/security/how-to-simplify-security-assessment-setup-using-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.

An overview of EC2 Systems Manager and EC2 Simple Systems Manager (SSM)

Amazon EC2 Systems Manager is a set of services that makes it easy to manage your Windows or Linux hosts running on EC2 instances. EC2 Systems Manager does this through an agent called EC2 Simple Systems Manager (SSM), which is installed on your instances. With SSM on your EC2 instances, you can save yourself an SSH or RDP session to the instance to perform management tasks.

With EC2 Systems Manager, you can perform various tasks at scale through a simple API, CLI, or EC2 Run Command. The EC2 Run Command can execute a Unix shell script on Linux instances or a Windows PowerShell script on Windows instances. When you use EC2 Systems Manager to run a script on an EC2 instance, the output is piped to a text file in Amazon S3 for you automatically. Therefore, you can examine the output without visiting the system or inventing your own mechanism for capturing console output.

The solution

Step 1: Enable EC2 Systems Manager and install the EC2 SSM agent

Setting up EC2 Systems Manager is relatively straightforward, but you must set up EC2 Systems Manager at the time you launch the instance. This is because the SSM agent will use an instance role to communicate with the EC2 Systems Manager securely. When launched with the appropriately configured IAM role, the EC2 instance is provided with a set of credentials that allows the SSM agent to perform actions on behalf of the account owner. The policy on the IAM role determines the permissions associated with these credentials.

The easiest way I have found to do this is to create the role, and then each time you launch an instance, associate the role with the instance and provide the SSM agent installation script in the instance’s user data in the launch wizard or API. Here’s how:

  1. Create an instance role so that the on-instance SSM agent can communicate with EC2 Systems Manager. If you already need an instance role for some other purpose, use the IAM console to attach the AmazonEC2RoleforSSM managed policy to your existing role.
  2. When launching the instance with the EC2 launch wizard, associate the role you just created with the new instance.
  3. When launching the instance with the EC2 launch wizard, provide the appropriate script as user data for your operating system and architecture to install the SSM agent as the instance is launched. To see this process and scripts in full, see Installing the SSM Agent.

Note: You must change the scripts slightly when copying them from the instructions to the EC2 user data: the word region in the curl command must be replaced with the AWS region code (for example, us-east-1).

When your instance starts, the SSM agent is installed. Having the SSM agent on the instance is the key component to the automated installation of the Amazon Inspector agent on the instance.

Step 2: Automatically install the Amazon Inspector agent when new EC2 instances are launched

Let’s assume that you will install the SSM agent when you first launch your instances. With that assumption in mind, you have two methods for installing the Amazon Inspector agent.

Method 1: Install the Amazon Inspector agent with user data

Just as we did above with the SSM agent, we can use the user data feature of EC2 to execute the Amazon Inspector agent installation script during instance launch. This is useful if you have decided not to install the SSM agent, but it is more work than necessary if you are in the habit of deploying the SSM agent at the launch of an instance.

To install the Amazon Inspector agent with user data on Linux systems, simply add the following commands to the User data box in the instance launch wizard (as shown in the following screenshot). This script works without modification on any Linux distribution that Amazon Inspector supports.

#!/bin/bash
cd /tmp
curl -O https://d1wk0tztpsntt1.cloudfront.net/linux/latest/install
chmod +x /tmp/install
/tmp/install

Note: If you are adding these commands to existing user data, be sure that only the first line of user data is #!/bin/bash. You should not have multiple copies of this line.

Finish launching the EC2 instance and the Amazon Inspector agent is installed as the instance is starting for the first time. To read more about this process, see Working with AWS Agents on Linux-based Operating Systems.

Method 2: Install the Amazon Inspector agent whenever a new EC2 instance starts

In environments that launch new instances continually, installing the Amazon Inspector agent automatically when an instance starts prevents some additional work. As we discussed in the previous method, you need to modify your instance launch process to include the EC2 SSM agent. This means you need to configure your instances with an EC2 Systems Manager role, as well as run the EC2 SSM agent.

First, create an IAM role that gives your Lambda function the permissions it needs to deploy the Amazon Inspector agent. Then, create the Lambda job that uses the SSM RunShellScript to install the Amazon Inspector agent. Finally, set up Amazon CloudWatch Events to run the Lambda job whenever a new instance enters the Running state.

Here are the details of the three-step process:

Step 1 – Create an IAM role for the Lambda function to use to send commands to EC2 Systems Manager:

  1. Sign in to the AWS Management Console and navigate to the IAM console.
  2. Choose Roles in the navigation pane. Choose Create new role.
  3. Type a name for a role. You should (but are not required to) use a descriptive name such as Inspector-agent-autodeploy-Lambda. Remember the name you choose because you will need it in Step 2.
  4. Choose the AWS Lambda role type.
  5. Attach the policies AWSLambdaBasicExecutionRole and AmazonSSMFullAccess.
  6. Choose Create the role to finish.

Step 2 – Create the Lambda function that will run EC2 Systems Manager commands to install the Amazon Inspector agent:

  1. Sign in to the AWS Management Console in your chosen region and navigate to the Lambda console.
  2. Choose Create a Lambda function.
  3. Skip Select blueprint.
  4. On the Configure triggers page, choose Next. Type a Name and Description for the function. Choose Python 2.7 for Runtime.
  5. Download and save autodeploy.py. Unzip the file, and copy the entire contents of autodeploy.py.
  6. From the Code entry type drop-down list, choose Edit code inline, and replace all the existing text with the text that you just copied from autodeploy.py.
  7. From the Role drop-down list, choose Choose an existing role, and then from the Existing role drop-down list, choose the role that you created in Step 1.
  8. Choose Next and then Create function to finish creating the function.

Step 3 – Set up CloudWatch Events to trigger the function:

  1. In the AWS Management Console in the same region as you used in Step 2, navigate to the CloudWatch console and then choose Events in the navigation pane.
  2. Choose Create rule. From the Select event source drop-down list, choose Amazon EC2.
  3. Choose Specific state(s) and Running. This tells CloudWatch to generate an event when an instance enters the Running state.
  4. Under Targets, choose Add target and then Lambda function.
  5. Choose the function that you created in Step 2.
  6. Click Configure details. Type a name and description for the event, and choose Create rule.

Summary

You have completed the setup! Now, whenever an EC2 instance enters the Running state (either on initial creation or on reboot), CloudWatch Events triggers an event that invokes the Lambda function that you created. The Lambda function then uses EC2 System Manager to install the Amazon Inspector agent on the instance.

In a subsequent AWS Security Blog post, I will show you how to take your security assessment automation a step further by automatically performing remediations for Amazon Inspector findings by using EC2 System Manager and Lambda.

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

– Eric

Managing Secrets for Amazon ECS Applications Using Parameter Store and IAM Roles for Tasks

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/compute/managing-secrets-for-amazon-ecs-applications-using-parameter-store-and-iam-roles-for-tasks/

Thanks to my colleague Stas Vonholsky  for a great blog on managing secrets with Amazon ECS applications.

—–

As containerized applications and microservice-oriented architectures become more popular, managing secrets, such as a password to access an application database, becomes more challenging and critical.

Some examples of the challenges include:

  • Support for various access patterns across container environments such as dev, test, and prod
  • Isolated access to secrets on a container/application level rather than at the host level
  • Multiple decoupled services with their own needs for access, both as services and as clients of other services

This post focuses on newly released features that support further improvements to secret management for containerized applications running on Amazon ECS. My colleague, Matthew McClean, also published an excellent post on the AWS Security Blog, How to Manage Secrets for Amazon EC2 Container Service–Based Applications by Using Amazon S3 and Docker, which discusses some of the limitations of passing and storing secrets with container parameter variables.

Most secret management tools provide the following functionality:

  • Highly secured storage system
  • Central management capabilities
  • Secure authorization and authentication mechanisms
  • Integration with key management and encryption providers
  • Secure introduction mechanisms for access
  • Auditing
  • Secret rotation and revocation

Amazon EC2 Systems Manager Parameter Store

Parameter Store is a feature of Amazon EC2 Systems Manager. It provides a centralized, encrypted store for sensitive information and has many advantages when combined with other capabilities of Systems Manager, such as Run Command and State Manager. The service is fully managed, highly available, and highly secured.

Because Parameter Store is accessible using the Systems Manager API, AWS CLI, and AWS SDKs, you can also use it as a generic secret management store. Secrets can be easily rotated and revoked. Parameter Store is integrated with AWS KMS so that specific parameters can be encrypted at rest with the default or custom KMS key. Importing KMS keys enables you to use your own keys to encrypt sensitive data.

Access to Parameter Store is enabled by IAM policies and supports resource level permissions for access. An IAM policy that grants permissions to specific parameters or a namespace can be used to limit access to these parameters. CloudTrail logs, if enabled for the service, record any attempt to access a parameter.

While Amazon S3 has many of the above features and can also be used to implement a central secret store, Parameter Store has the following added advantages:

  • Easy creation of namespaces to support different stages of the application lifecycle.
  • KMS integration that abstracts parameter encryption from the application while requiring the instance or container to have access to the KMS key and for the decryption to take place locally in memory.
  • Stored history about parameter changes.
  • A service that can be controlled separately from S3, which is likely used for many other applications.
  • A configuration data store, reducing overhead from implementing multiple systems.
  • No usage costs.

Note: At the time of publication, Systems Manager doesn’t support VPC private endpoint functionality. To enforce stricter access to a Parameter Store endpoint from a private VPC, use a NAT gateway with a set Elastic IP address together with IAM policy conditions that restrict parameter access to a limited set of IP addresses.

IAM roles for tasks

With IAM roles for Amazon ECS tasks, you can specify an IAM role to be used by the containers in a task. Applications interacting with AWS services must sign their API requests with AWS credentials. This feature provides a strategy for managing credentials for your applications to use, similar to the way that Amazon EC2 instance profiles provide credentials to EC2 instances.

Instead of creating and distributing your AWS credentials to the containers or using the EC2 instance role, you can associate an IAM role with an ECS task definition or the RunTask API operation. For more information, see IAM Roles for Tasks.

You can use IAM roles for tasks to securely introduce and authenticate the application or container with the centralized Parameter Store. Access to the secret manager should include features such as:

  • Limited TTL for credentials used
  • Granular authorization policies
  • An ID to track the requests in the logs of the central secret manager
  • Integration support with the scheduler that could map between the container or task deployed and the relevant access privileges

IAM roles for tasks support this use case well, as the role credentials can be accessed only from within the container for which the role is defined. The role exposes temporary credentials and these are rotated automatically. Granular IAM policies are supported with optional conditions about source instances, source IP addresses, time of day, and other options.

The source IAM role can be identified in the CloudTrail logs based on a unique Amazon Resource Name and the access permissions can be revoked immediately at any time with the IAM API or console. As Parameter Store supports resource level permissions, a policy can be created to restrict access to specific keys and namespaces.

Dynamic environment association

In many cases, the container image does not change when moving between environments, which supports immutable deployments and ensures that the results are reproducible. What does change is the configuration: in this context, specifically the secrets. For example, a database and its password might be different in the staging and production environments. There’s still the question of how do you point the application to retrieve the correct secret? Should it retrieve prod.app1.secret, test.app1.secret or something else?

One option can be to pass the environment type as an environment variable to the container. The application then concatenates the environment type (prod, test, etc.) with the relative key path and retrieves the relevant secret. In most cases, this leads to a number of separate ECS task definitions.

When you describe the task definition in a CloudFormation template, you could base the entry in the IAM role that provides access to Parameter Store, KMS key, and environment property on a single CloudFormation parameter, such as “environment type.” This approach could support a single task definition type that is based on a generic CloudFormation template.

Walkthrough: Securely access Parameter Store resources with IAM roles for tasks

This walkthrough is configured for the North Virginia region (us-east-1). I recommend using the same region.

Step 1: Create the keys and parameters

First, create the following KMS keys with the default security policy to be used to encrypt various parameters:

  • prod-app1 –used to encrypt any secrets for app1.
  • license-key –used to encrypt license-related secrets.
aws kms create-key --description prod-app1 --region us-east-1
aws kms create-key --description license-code --region us-east-1

Note the KeyId property in the output of both commands. You use it throughout the walkthrough to identify the KMS keys.

The following commands create three parameters in Parameter Store:

  • prod.app1.db-pass (encrypted with the prod-app1 KMS key)
  • general.license-code (encrypted with the license-key KMS key)
  • prod.app2.user-name (stored as a standard string without encryption)
aws ssm put-parameter --name prod.app1.db-pass --value "AAAAAAAAAAA" --type SecureString --key-id "<key-id-for-prod-app1-key>" --region us-east-1
aws ssm put-parameter --name general.license-code --value "CCCCCCCCCCC" --type SecureString --key-id "<key-id-for-license-code-key>" --region us-east-1
aws ssm put-parameter --name prod.app2.user-name --value "BBBBBBBBBBB" --type String --region us-east-1

Step 2: Create the IAM role and policies

Now, create a role and an IAM policy to be associated later with the ECS task that you create later on.
The trust policy for the IAM role needs to allow the ecs-tasks entity to assume the role.

{
   "Version": "2012-10-17",
   "Statement": [
     {
       "Sid": "",
       "Effect": "Allow",
       "Principal": {
         "Service": "ecs-tasks.amazonaws.com"
       },
       "Action": "sts:AssumeRole"
     }
   ]
 }

Save the above policy as a file in the local directory with the name ecs-tasks-trust-policy.json.

aws iam create-role --role-name prod-app1 --assume-role-policy-document file://ecs-tasks-trust-policy.json

The following policy is attached to the role and later associated with the app1 container. Access is granted to the prod.app1.* namespace parameters, the encryption key required to decrypt the prod.app1.db-pass parameter and the license code parameter. The namespace resource permission structure is useful for building various hierarchies (based on environments, applications, etc.).

Make sure to replace <key-id-for-prod-app1-key> with the key ID for the relevant KMS key and <account-id> with your account ID in the following policy.

{
     "Version": "2012-10-17",
     "Statement": [
         {
             "Effect": "Allow",
             "Action": [
                 "ssm:DescribeParameters"
             ],
             "Resource": "*"
         },
         {
             "Sid": "Stmt1482841904000",
             "Effect": "Allow",
             "Action": [
                 "ssm:GetParameters"
             ],
             "Resource": [
                 "arn:aws:ssm:us-east-1:<account-id>:parameter/prod.app1.*",
                 "arn:aws:ssm:us-east-1:<account-id>:parameter/general.license-code"
             ]
         },
         {
             "Sid": "Stmt1482841948000",
             "Effect": "Allow",
             "Action": [
                 "kms:Decrypt"
             ],
             "Resource": [
                 "arn:aws:kms:us-east-1:<account-id>:key/<key-id-for-prod-app1-key>"
             ]
         }
     ]
 }

Save the above policy as a file in the local directory with the name app1-secret-access.json:

aws iam create-policy --policy-name prod-app1 --policy-document file://app1-secret-access.json

Replace <account-id> with your account ID in the following command:

aws iam attach-role-policy --role-name prod-app1 --policy-arn "arn:aws:iam::<account-id>:policy/prod-app1"

Step 3: Add the testing script to an S3 bucket

Create a file with the script below, name it access-test.sh and add it to an S3 bucket in your account. Make sure the object is publicly accessible and note down the object link, for example https://s3-eu-west-1.amazonaws.com/my-new-blog-bucket/access-test.sh

#!/bin/bash
#This is simple bash script that is used to test access to the EC2 Parameter store.
# Install the AWS CLI
apt-get -y install python2.7 curl
curl -O https://bootstrap.pypa.io/get-pip.py
python2.7 get-pip.py
pip install awscli
# Getting region
EC2_AVAIL_ZONE=`curl -s http://169.254.169.254/latest/meta-data/placement/availability-zone`
EC2_REGION="`echo \"$EC2_AVAIL_ZONE\" | sed -e 's:\([0-9][0-9]*\)[a-z]*\$:\\1:'`"
# Trying to retrieve parameters from the EC2 Parameter Store
APP1_WITH_ENCRYPTION=`aws ssm get-parameters --names prod.app1.db-pass --with-decryption --region $EC2_REGION --output text 2>&1`
APP1_WITHOUT_ENCRYPTION=`aws ssm get-parameters --names prod.app1.db-pass --no-with-decryption --region $EC2_REGION --output text 2>&1`
LICENSE_WITH_ENCRYPTION=`aws ssm get-parameters --names general.license-code --with-decryption --region $EC2_REGION --output text 2>&1`
LICENSE_WITHOUT_ENCRYPTION=`aws ssm get-parameters --names general.license-code --no-with-decryption --region $EC2_REGION --output text 2>&1`
APP2_WITHOUT_ENCRYPTION=`aws ssm get-parameters --names prod.app2.user-name --no-with-decryption --region $EC2_REGION --output text 2>&1`
# The nginx server is started after the script is invoked, preparing folder for HTML.
if [ ! -d /usr/share/nginx/html/ ]; then
mkdir -p /usr/share/nginx/html/;
fi
chmod 755 /usr/share/nginx/html/

# Creating an HTML file to be accessed at http://<public-instance-DNS-name>/ecs.html
cat > /usr/share/nginx/html/ecs.html <<EOF
<!DOCTYPE html>
<html>
<head>
<title>App1</title>
<style>
body {padding: 20px;margin: 0 auto;font-family: Tahoma, Verdana, Arial, sans-serif;}
code {white-space: pre-wrap;}
result {background: hsl(220, 80%, 90%);}
</style>
</head>
<body>
<h1>Hi there!</h1>
<p style="padding-bottom: 0.8cm;">Following are the results of different access attempts as expirienced by "App1".</p>

<p><b>Access to prod.app1.db-pass:</b><br/>
<pre><code>aws ssm get-parameters --names prod.app1.db-pass --with-decryption</code><br/>
<code><result>$APP1_WITH_ENCRYPTION</result></code><br/>
<code>aws ssm get-parameters --names prod.app1.db-pass --no-with-decryption</code><br/>
<code><result>$APP1_WITHOUT_ENCRYPTION</result></code></pre><br/>
</p>

<p><b>Access to general.license-code:</b><br/>
<pre><code>aws ssm get-parameters --names general.license-code --with-decryption</code><br/>
<code><result>$LICENSE_WITH_ENCRYPTION</result></code><br/>
<code>aws ssm get-parameters --names general.license-code --no-with-decryption</code><br/>
<code><result>$LICENSE_WITHOUT_ENCRYPTION</result></code></pre><br/>
</p>

<p><b>Access to prod.app2.user-name:</b><br/>
<pre><code>aws ssm get-parameters --names prod.app2.user-name --no-with-decryption</code><br/>
<code><result>$APP2_WITHOUT_ENCRYPTION</result></code><br/>
</p>

<p><em>Thanks for visiting</em></p>
</body>
</html>
EOF

Step 4: Create a test cluster

I recommend creating a new ECS test cluster with the latest ECS AMI and ECS agent on the instance. Use the following field values:

  • Cluster name: access-test
  • EC2 instance type: t2.micro
  • Number of instances: 1
  • Key pair: No EC2 key pair is required, unless you’d like to SSH to the instance and explore the running container.
  • VPC: Choose the default VPC. If unsure, you can find the VPC ID with the IP range 172.31.0.0/16 in the Amazon VPC console.
  • Subnets: Pick a subnet in the default VPC.
  • Security group: Create a new security group with CIDR block 0.0.0.0/0 and port 80 for inbound access.

Leave other fields with the default settings.

Create a simple task definition that relies on the public NGINX container and the role that you created for app1. Specify the properties such as the available container resources and port mappings. Note the command option is used to download and invoke a test script that installs the AWS CLI on the container, runs a number of get-parameter commands, and creates an HTML file with the results.

Replace <account-id> with your account ID, <your-S3-URI> with a link to the S3 object created in step 3 in the following commands:

aws ecs register-task-definition --family access-test --task-role-arn "arn:aws:iam::<account-id>:role/prod-app1" --container-definitions name="access-test",image="nginx",portMappings="[{containerPort=80,hostPort=80,protocol=tcp}]",readonlyRootFilesystem=false,cpu=512,memory=490,essential=true,entryPoint="sh,-c",command="\"/bin/sh -c \\\"apt-get update ; apt-get -y install curl ; curl -O <your-S3-URI> ; chmod +x access-test.sh ; ./access-test.sh ; nginx -g 'daemon off;'\\\"\"" --region us-east-1

aws ecs run-task --cluster access-test --task-definition access-test --count 1 --region us-east-1

Verifying access

After the task is in a running state, check the public DNS name of the instance and navigate to the following page:

http://<ec2-instance-public-DNS-name>/ecs.html

You should see the results of running different access tests from the container after a short duration.

If the test results don’t appear immediately, wait a few seconds and refresh the page.
Make sure that inbound traffic for port 80 is allowed on the security group attached to the instance.

The results you see in the static results HTML page should be the same as running the following commands from the container.

prod.app1.key1

aws ssm get-parameters --names prod.app1.db-pass --with-decryption --region us-east-1
aws ssm get-parameters --names prod.app1.db-pass --no-with-decryption --region us-east-1

Both commands should work, as the policy provides access to both the parameter and the required KMS key.

general.license-code

aws ssm get-parameters --names general.license-code --no-with-decryption --region us-east-1
aws ssm get-parameters --names general.license-code --with-decryption --region us-east-1

Only the first command with the “no-with-decryption” parameter should work. The policy allows access to the parameter in Parameter Store but there’s no access to the KMS key. The second command should fail with an access denied error.

prod.app2.user-name

aws ssm get-parameters --names prod.app2.user-name –no-with-decryption --region us-east-1

The command should fail with an access denied error, as there are no permissions associated with the namespace for prod.app2.

Finishing up

Remember to delete all resources (such as the KMS keys and EC2 instance), so that you don’t incur charges.

Conclusion

Central secret management is an important aspect of securing containerized environments. By using Parameter Store and task IAM roles, customers can create a central secret management store and a well-integrated access layer that allows applications to access only the keys they need, to restrict access on a container basis, and to further encrypt secrets with custom keys with KMS.

Whether the secret management layer is implemented with Parameter Store, Amazon S3, Amazon DynamoDB, or a solution such as Vault or KeyWhiz, it’s a vital part to the process of managing and accessing secrets.

EC2 Systems Manager – Configure & Manage EC2 and On-Premises Systems

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-systems-manager-configure-manage-ec2-and-on-premises-systems/

Last year I introduced you to the EC2 Run Command and showed you how to use it to do remote instance management at scale, first for EC2 instances and then in hybrid and cross-cloud environments. Along the way we added support for Linux instances, making EC2 Run Command a widely applicable and incredibly useful administration tool.

Welcome to the Family
Werner announced the EC2 Systems Manager at AWS re:Invent and I’m finally getting around to telling you about it!

This a new management service that include an enhanced version of EC2 Run Command along with eight other equally useful functions. Like EC2 Run Command it supports hybrid and cross-cloud environments composed of instances and services running Windows and Linux. You simply open up the AWS Management Console, select the instances that you want to manage, and define the tasks that you want to perform (API and CLI access is also available).

Here’s an overview of the improvements and new features:

Run Command – Now allows you to control the velocity of command executions, and to stop issuing commands if the error rate grows too high.

State Manager – Maintains a defined system configuration via policies that are applied at regular intervals.

Parameter Store – Provides centralized (and optionally encrypted) storage for license keys, passwords, user lists, and other values.

Maintenance Window -Specify a time window for installation of updates and other system maintenance.

Software Inventory – Gathers a detailed software and configuration inventory (with user-defined additions) from each instance.

AWS Config Integration – In conjunction with the new software inventory feature, AWS Config can record software inventory changes to your instances.

Patch Management – Simplify and automate the patching process for your instances.

Automation – Simplify AMI building and other recurring AMI-related tasks.

Let’s take a look at each one…

Run Command Improvements
You can now control the number of concurrent command executions. This can be useful in situations where the command references a shared, limited resource such as an internal update or patch server and you want to avoid overloading it with too many requests.

This feature is currently accessible from the CLI and from the API. Here’s a CLI example that limits the number of concurrent executions to 2:

$ aws ssm send-command \
  --instance-ids "i-023c301591e6651ea" "i-03cf0fc05ec82a30b" "i-09e4ed09e540caca0" "i-0f6d1fe27dc064099" \
  --document-name "AWS-RunShellScript" \
  --comment "Run a shell script or specify the commands to run." \
  --parameters commands="date" \
  --timeout-seconds 600 --output-s3-bucket-name "jbarr-data" \
  --region us-east-1 --max-concurrency 2

Here’s a more interesting variant that is driven by tags and tag values by specifying --targets instead of --instance-ids:

$ aws ssm send-command \
  --targets "Key=tag:Mode,Values=Production" ... 

You  can also stop issuing commands if they are returning errors, with the option to specify either a maximum number of errors or a failure rate:

$ aws ssm send-command --max-errors 5 ... 
$ aws ssm send-command --max-errors 5% ...

State Manager
State Manager helps to keep your instances in a defined state, as defined by a document. You create the document, associate it with a set of target instances, and then create an association to specify when and how often the document should be applied. Here’s a document that updates the message of the day file:

And here’s the association (this one uses tags so that it applies to current instances and to others that are launched later and are tagged in the same way):

Specifying targets using tags makes the association future-proof, and allows it to work as expected in dynamic, auto-scaled environments. I can see all of my associations, and I can run the new one by selecting it and clicking on Apply Association Now:

Parameter Store
This feature simplifies storage and management for license keys, passwords, and other data that you want to distribute  to your instances. Each parameter has a type (string, string list, or secure string), and can be stored in encrypted form. Here’s how I create a parameter:

And here’s how I reference the parameter in a command:

Maintenance Window
This feature allows specification of a time window for installation of updates and other system maintenance. Here’s how I create a weekly time window that opens for four hours every Saturday:

After I create the window I need to assign a set of instances to it. I can do this by instance Id or by tag:

And  then I need to register a task to perform during the maintenance window. For example, I can run a Linux shell script:

Software Inventory
This feature collects information about software and settings for a set of instances. To access it, I click on Managed Instances and Setup Inventory:

Setting up the inventory creates an association between an AWS-owned document and a set of instances. I simply choose the targets, set the schedule, and identify the types of items to be inventoried, then click on Setup Inventory:

After the inventory runs, I can select an instance and then click on the Inventory tab in order to inspect the results:

The results can be filtered for further analysis. For example, I can narrow down the list of AWS Components to show only development tools and libraries:

I can also run inventory-powered queries across all of the managed instances. Here’s how I can find Windows Server 2012 R2 instances that are running a version of .NET older than 4.6:

AWS Config Integration
The results of the inventory can be routed to AWS Config  and allow you to track changes to the applications, AWS components, instance information, network configuration, and Windows Updates over time. To access this information, I click on Managed instance information above the Config timeline for the instance:

The three lines at the bottom lead to the inventory information. Here’s the network configuration:

Patch Management
This feature helps you to keep the operating system on your Windows instances up to date. Patches are applied during maintenance windows that you define, and are done with respect to a baseline. The baseline specifies rules for automatic approval of patches based on classification and severity, along with an explicit list of patches to approve or reject.

Here’s my baseline:

Each baseline can apply to one or more patch groups. Instances within a patch group have a Patch Group tag. I named my group Win2016:

Then I associated the value with the baseline:

The next step is to arrange to apply the patches during a maintenance window using the AWS-ApplyPatchBaseline document:

I can return to the list of Managed Instances and use a pair of filters to find out which instances are in need of patches:

Automation
Last but definitely not least, the Automation feature simplifies common AMI-building and updating tasks. For example, you can build a fresh Amazon Linux AMI each month using the AWS-UpdateLinuxAmi document:

Here’s what happens when this automation is run:

Available Now
All of the EC2 Systems Manager features and functions that I described above are available now and you can start using them today at no charge. You pay only for the resources that you manage.

Jeff;