SSM Parameter Store: Tutorial & Best Practices

SSM Parameter Store: Tutorial & Best Practices

AWS Systems Manager (SSM) Parameter Store, initially designed to manage application configuration data, has now evolved into a secrets management solution due to its cost-effectiveness compared to other paid alternatives in AWS. In 2019, AWS further enhanced its capabilities with advanced parameters, offering increased size, limits, and security support.

Parameter Store now holds a special position within the AWS ecosystem for handling secrets, along with other paid services like AWS Secrets Manager, as it now supports storing values as encrypted data.

This article will explore how secrets management in AWS has evolved over time, the best practices for using SSM Parameter Store, the limitations of Parameter Store and its current standing in relation to AWS Secrets Manager, and how integration with third-party tools can optimize secrets management strategies.

Summary of SSM Parameter Store concepts

The table below summarizes the key concepts that will be covered in this article.

Common use cases of Parameter Store

Parameter Store is widely used to store the following types of information:

• Application configuration data: Parameter store is often used to hold configuration parameters such as database connection strings and feature flags, allowing applications to retrieve settings dynamically.
• Environment variables: Parameter Store can be used to dynamically adjust application behavior by storing environment-specific values such as API endpoints, Lambda function ARNs, IAM role ARNs, Amazon Certificate Manager certificate ARNs, etc.
• Secrets: Parameter Store can be used to store sensitive data such as passwords and tokens using the SecureString data type.

Best practices for using Parameter Store

Use a hierarchical structure for parameter naming

Using a hierarchical structure for the parameter naming convention helps facilitate easier management and categorization of parameters. Following a strict naming convention helps users enhance readability and maintainability.

Limit access to the Parameter Store

Setting up proper access control for your Parameter Store is crucial when it comes to the security considerations of your secret. Users should be granted minimal permissions to the services that require access to your parameter, using strict IAM policies.

1{
2	"Version": "2012-10-17",
3	"Statement": [
4		{
5			"Sid": "prodMongo",
6			"Effect": "Allow",
7			"Action": [
8				"ssm:GetParameter"
9			],
10			"Resource": ["arn:aws:ssm:ap-south-1:123123123123:parameter/prod/myapp/mongou/uri"]
11		}
12	]
13}

Use the SecureString data type to store sensitive information

Parameter Store supports storing values as plaintext or encrypted data. Always store sensitive information such as API Keys, tokens, passwords, etc., using the SecureString data type.

Note that Parameter Store will default to storing your secrets as plaintext unless otherwise specified.

Rotate secrets regularly using a custom Lambda function or CodeBuild

Since Parameter Store does not have built-in secrets rotation capabilities, users will have to set up a custom Lambda function or rotation logic inside a CodeBuild to automate the rotation of secrets. This is to ensure that the secrets are not long-lived and become obsolete in case they’re leaked.

The following code demonstrates how you can set up a Lambda function to rotate your secrets. This function can be attached to an EventBridge trigger that runs on a specific interval schedule to automate the rotation.

1import boto3
2import secrets
3import json
4
5def generate_random_string(length=20):
6    return secrets.token_hex(length // 2)
7
8def lambda_handler(event, context):
9    region = 'ap-south-1'
10    ssm_client = boto3.client('ssm', region_name=region)
11    parameter_name = '/prod/myapp/password'
12
13    try:
14        response = ssm_client.get_parameter(Name=parameter_name, WithDecryption=True)
15
16        current_value = response['Parameter']['Value']
17        new_value = generate_random_string()
18
19        ssm_client.put_parameter(
20            Name=parameter_name,
21            Value=new_value,
22            Type='SecureString',
23            Overwrite=True
24        )
25        print(f"Rotated secret for parameter '{parameter_name}' successfully.")
26
27        response_payload = {
28            'status': 'success',
29            'message': f"Rotated secret for parameter '{parameter_name}' successfully."
30        }
31
32    except Exception as e:
33        print(f"Error rotating secret for parameter '{parameter_name}': {str(e)}")
34        response_payload = {
35            'status': 'error',
36            'message': f"Error rotating secret for parameter '{parameter_name}': {str(e)}"
37        }
38
39    response_json = json.dumps(response_payload)
40    print(response_json)
41    return {
42        'statusCode': 200,
43        'body': response_json
44    }

Use advanced parameters for larger parameters

Standard parameters in Parameter Store allow for a maximum size of 4 KB per parameter. To store a parameter larger than 4 KB, consider going with advanced parameters, which support a maximum size of 8 KB.

This article by AWS explains the differences between standard and advanced parameters.

Set up AWS CloudTrail to monitor your secrets

Since Parameter Store does not have built-in logging capability, users will have to set up AWS CloudTrail to monitor audits and access logs for your secrets.

Limitations of Parameter Store

SSM Parameter Store has the following limitations when it comes to secrets management:

• Secrets not encrypted by default: Parameter Stores are not encrypted by default, so you need to choose the SecureString type parameter if you need encryption.
• No support for random secret generation: Parameter Store cannot generate secrets. Users will have to enter a value while creating a Parameter Store entry.
• No built-in auditing capability: Parameter Store does not have any built-in auditing capability. Users will have to set up AWS CloudTrail (at an additional cost) to monitor their secrets.
• No built-in secrets rotation: Parameter Store does not offer built-in rotation of secrets. Users will have to set up the rotation logic manually.
• No support for automatic cross-region replication: Unlike Secrets Manager, Parameter Store does not have automatic cross-region replication support. However, users can manually do this using the console or a Lambda function.

How can Doppler improve Parameter Store?

Doppler is a centralized secrets management platform that integrates with multiple platforms, including AWS, Azure, GCP, GitHub, GitLab, and Kubernetes, among others. Doppler can improve some of the limitations associated with Parameter Store in the following ways:

• Encryption by default: All the secrets stored using Doppler are encrypted at rest and in transit, eliminating the need to choose the SecureString parameter type from AWS manually.
• Random secret generation: Doppler provides a secure and easy way to generate random secrets. It offers a user-friendly interface for generating and managing secrets.
• Built-in auditing capability: Doppler offers robust auditing and logging capabilities. It provides a centralized dashboard where users can easily track access logs and changes made to the secrets.
• Support for cross-region parameters: Users can add multiple integrations for Parameter Store to multiple regions and sync secrets to these regions all at once using the Doppler dashboard.

Final Thoughts: Elevating AWS Secrets Management

AWS Systems Manager Parameter Store has evolved from its original purpose of managing configuration data to become a cost-effective solution for secrets management within the AWS ecosystem. Despite its advantages, Parameter Store has certain limitations, such as the absence of built-in secrets rotation and auditing capabilities as well as challenges with default encryption and cross-region replication.

Integrating Parameter Store with third-party tools like Doppler can address the gaps in secrets management by improving security, automation, and overall efficiency in handling sensitive information. By leveraging the strengths of both Parameter Store and Doppler, organizations can achieve a more robust and comprehensive solution to secrets management within their AWS environments.