sample-aiml-security-assessment

AI/ML Security Assessment Framework - Developer Guide

Table of Contents

Architecture Overview

The AI/ML Security Assessment Framework is a serverless, multi-account security assessment solution for AWS AI/ML workloads. It performs 71 core security checks across Amazon Bedrock, Amazon SageMaker AI, and Amazon Bedrock AgentCore, plus 27 always-on Agentic AI Security checks, with optional 64-check Financial Services GenAI risk and 12-check OWASP Top 10 for LLM assessments, generating interactive HTML reports with findings and remediation guidance.

Security Design Principles

Architecture Diagrams

Phase 1: Deployment Setup (AWS CloudFormation)

Deployment Phase

Phase 2: Assessment Execution (AWS CodeBuild)

Execution Phase

Service-Level Assessment Architecture

Service-Level Architecture

Two-Phase Architecture

Phase 1: Infrastructure Deployment

Step 1: Member Account Roles (1-aiml-security-member-roles.yaml)

Step 2: Central Infrastructure (2-aiml-security-codebuild.yaml)

Phase 2: Assessment Execution (AWS CodeBuild Orchestration)

AWS CodeBuild Execution Flow

  1. Account Discovery: In multi-account mode, lists active accounts from AWS Organizations or uses MultiAccountListOverride
  2. Role Assumption: In multi-account mode, assumes AIMLSecurityMemberRole in each target account
  3. AWS SAM Deployment: Deploys or updates the AI/ML assessment stack through AWS SAM
  4. Assessment Execution: Triggers AWS Step Functions workflow in each account, passing enableFinServ and enableOWASP from the deployment parameters
  5. Results Consolidation: Syncs per-account reports to the infrastructure bucket and creates a consolidated report for multi-account runs

Project Structure

sample-aiml-security-assessment/
├── aiml-security-assessment/
│   ├── functions/security/
│   │   ├── bedrock_assessments/      # Bedrock security checks (33)
│   │   ├── sagemaker_assessments/    # SageMaker security checks (25)
│   │   ├── agentcore_assessments/    # AgentCore security checks (13)
│   │   ├── finserv_assessments/      # Optional Financial Services GenAI risk checks (64)
│   │   ├── owasp_assessments/        # Optional OWASP Top 10 for LLM checks (12)
│   │   ├── finserv_tests/            # FinServ-specific unit and coverage tests
│   │   ├── iam_permission_caching/   # AWS IAM permissions cache
│   │   ├── cleanup_bucket/           # Amazon S3 cleanup
│   │   ├── resolve_regions/          # Multi-region resolution Lambda
│   │   └── generate_consolidated_report/  # HTML/CSV report generation
│   ├── statemachine/                 # AWS Step Functions definition
│   ├── images/                       # SAM application images
│   ├── template.yaml                 # AWS SAM template (single-account)
│   ├── template-multi-account.yaml   # AWS SAM template (multi-account)
│   ├── samconfig.toml                # SAM deployment configuration
│   ├── envvars.json                  # Environment variables for local testing
│   └── testfile.json                 # Test event file for local invocation
├── deployment/                       # AWS CloudFormation templates
├── docs/                             # Documentation
│   ├── DEVELOPER_GUIDE.md            # This guide
│   ├── SECURITY_CHECKS.md            # Security checks reference (core + Agentic)
│   ├── SECURITY_CHECKS_FINSERV.md    # FinServ GenAI risk checks reference
│   ├── SECURITY_CHECKS_OWASP.md      # OWASP Top 10 for LLM checks reference
│   ├── SECURITY_CHECKS_FINSERV_SEVERITY_METHODOLOGY.md  # FinServ severity model
│   ├── SECURITY_CHECKS_FINSERV_SEVERITY_REGISTER.md     # FinServ per-finding severities
│   ├── TROUBLESHOOTING.md            # Troubleshooting guide
│   ├── CLEANUP.md                    # Resource removal guide
│   ├── diagrams/                     # Architecture diagrams
│   └── icons/                        # AWS service icons
├── sample-reports/                   # Sample assessment reports
│   ├── scripts/                      # Screenshot capture scripts
│   ├── *.html                        # Sample HTML reports
│   └── *.png                         # Report screenshots
├── tests/                            # Unit tests for assessment functions
│   └── requirements.txt              # Test dependencies
├── .github/workflows/                # PR lint, test, SAM validate, and security scans
├── buildspec.yml                     # AWS CodeBuild orchestration
└── consolidate_html_reports.py       # Multi-account report consolidation

Member Account Resources (Deployed by AWS SAM)

Assessment Execution Workflow

AWS CodeBuild Orchestration

# buildspec.yml execution flow
1. Get active accounts from AWS Organizations
2. For each account:
   - Assume AIMLSecurityMemberRole
   - Deploy AI/ML assessment stack through AWS SAM
   - Start AWS Step Functions execution
3. Wait for completion and consolidate results

AWS Step Functions (Per Module)

{
  "Comment": "AI/ML Assessment Module",
  "StartAt": "Cleanup S3 Bucket",
  "States": {
    "Cleanup S3 Bucket": {
      "Type": "Task",
      "Next": "IAM Permission Caching"
    },
    "IAM Permission Caching": {
      "Type": "Task",
      "Next": "Resolve Target Regions"
    },
    "Resolve Target Regions": {
      "Type": "Task",
      "Comment": "Resolves target regions from TARGET_REGIONS env var",
      "Next": "Scan Regions"
    },
    "Scan Regions": {
      "Type": "Map",
      "ItemsPath": "$.ResolvedRegions.regions",
      "MaxConcurrency": "${MaxRegionConcurrency}",
      "ItemProcessor": {
        "ProcessorConfig": {"Mode": "INLINE"},
        "StartAt": "Run Security Assessments",
        "States": {
          "Run Security Assessments": {
            "Type": "Parallel",
            "Branches": [
              {"StartAt": "Bedrock Security Assessment", "States": {...}},
              {"StartAt": "Sagemaker Security Assessment", "States": {...}},
              {"StartAt": "AgentCore Security Assessment", "States": {...}},
              {
                "StartAt": "FinServ Enabled?",
                "States": {
                  "FinServ Enabled?": {
                    "Type": "Choice",
                    "Comment": "Runs FinServ when enableFinServ or enableOWASP is true and RegionIndex is 0"
                  },
                  "FinServ Security Assessment": {"Type": "Task", "Resource": "arn:aws:states:::lambda:invoke", "End": true},
                  "FinServ Assessment Skipped": {"Type": "Pass", "End": true}
                }
              }
            ],
            "End": true
          }
        }
      },
      "Next": "Generate Consolidated Report"
    },
    "Generate Consolidated Report": {
      "Type": "Task",
      "End": true
    }
  }
}

Assessment Structure

The framework includes 71 core security checks across three AI/ML services, plus 27 always-on Agentic AI Security checks, 64 optional Financial Services GenAI risk checks when EnableFinServAssessment is enabled, and 12 optional OWASP Top 10 for LLM checks when EnableOWASPAssessment is enabled. For the complete list of checks with descriptions, see the Security Checks Reference.

AWS Lambda Functions

Each core service assessment AWS Lambda function:

  1. Receives execution context and target region from AWS Step Functions (via the Map state)
  2. Verifies the service is available in the target region (returns N/A finding if not)
  3. Reads cached AWS IAM permissions from Amazon S3
  4. Creates regional boto3 clients with explicit region_name parameter
  5. Performs security checks against AWS APIs in the target region
  6. Generates CSV report with findings (includes Region column)
  7. Uploads results to Amazon S3 with region-suffixed filename
  8. Returns findings summary to AWS Step Functions

The Financial Services assessment Lambda is different. It is deployed in both SAM templates, but Step Functions invokes it only from the first region iteration (RegionIndex == 0) when the execution input includes "enableFinServ": "true" or "enableOWASP": "true". The OWASP path uses FinServ findings as hidden source rows unless FinServ was explicitly enabled. FinServ receives the full TargetRegions list and emits findings with Region values so the report can display the same regional filters as the core services.

Additional Functions:

Adding New AI/ML Service Assessments

To add a new AI/ML service (for example, Amazon Comprehend, Amazon Textract):

Step 1: Create Service Assessment Function

  1. Create Function Directory (One function per service):
# Example: Adding Comprehend security assessment
mkdir -p aiml-security-assessment/functions/security/comprehend_assessments
cd aiml-security-assessment/functions/security/comprehend_assessments
  1. Create Function Files:
# app.py
import boto3
import os
import json
from botocore.config import Config
from botocore.exceptions import ClientError, EndpointConnectionError
from schema import create_finding

boto3_config = Config(retries=dict(max_attempts=10, mode="adaptive"))


def lambda_handler(event, context):
    """Main assessment handler for new service"""
    all_findings = []

    # Extract target region from Step Functions Map state
    region = event.get("Region", os.environ.get("AWS_REGION", "us-east-1"))

    # Verify service availability in this region
    try:
        test_client = boto3.client("comprehend", config=boto3_config, region_name=region)
        test_client.list_endpoints(MaxResults=1)
    except (EndpointConnectionError, Exception) as e:
        if "Could not connect to the endpoint URL" in str(e):
            # Service not available — return N/A finding
            ...
            return {"statusCode": 200, "body": {"message": f"Service not available in {region}"}}

    # Get cached permissions
    execution_id = event["Execution"]["Name"]
    permission_cache = get_permissions_cache(execution_id)

    # Run assessment checks (pass region to each)
    findings = check_new_service_security(permission_cache, region=region)
    all_findings.append(findings)

    # Generate and upload report (include region in S3 key)
    csv_content = generate_csv_report(all_findings)
    bucket_name = os.environ.get("AIML_ASSESSMENT_BUCKET_NAME")
    s3_url = write_to_s3(execution_id, csv_content, bucket_name, region=region)

    return {
        "statusCode": 200,
        "body": {
            "message": "New service assessment completed",
            "findings": all_findings,
            "report_url": s3_url,
        },
    }


def check_new_service_security(permission_cache, region: str = ""):
    """Implement your security checks here"""
    findings = {
        "check_name": "New Service Security Check",
        "status": "PASS",
        "details": "",
        "csv_data": [],
    }

    # Create regional client
    client = boto3.client("comprehend", config=boto3_config, region_name=region)

    # Your assessment logic here
    # Pass region= to all create_finding() calls

    return findings
  1. Create Requirements File:
# requirements.txt
boto3>=1.26.0
botocore>=1.29.0
  1. Create Schema File:
# schema.py
from enum import Enum


class SeverityEnum(str, Enum):
    HIGH = "High"
    MEDIUM = "Medium"
    LOW = "Low"
    INFORMATIONAL = "Informational"


class StatusEnum(str, Enum):
    FAILED = "Failed"
    PASSED = "Passed"
    NA = "N/A"


def create_finding(
    check_id, finding_name, finding_details, resolution, reference, severity, status, region=""
):
    """Create standardized finding format

    Args:
        check_id: Unique check identifier (for example, SM-01, BR-01, AC-01)
        finding_name: Name of the finding
        finding_details: Detailed description
        resolution: Steps to resolve (empty string for N/A status)
        reference: Documentation URL
        severity: SeverityEnum value
        status: StatusEnum value (Failed, Passed, or N/A)
        region: AWS region where the finding was identified
    """
    return {
        "Check_ID": check_id,
        "Finding": finding_name,
        "Finding_Details": finding_details,
        "Resolution": resolution,
        "Reference": reference,
        "Severity": severity,
        "Status": status,
        "Region": region,
    }

Step 2: Update AWS SAM Template

Add your new function to both SAM templates:

  ComprehendSecurityAssessmentFunction:
    Type: AWS::Serverless::Function
    Properties:
      FunctionName: !Sub 'aiml-security-${AWS::StackName}-ComprehendAssessment'
      CodeUri: functions/security/comprehend_assessments/
      Handler: app.lambda_handler
      Runtime: python3.12
      Timeout: 600
      MemorySize: 1024
      Environment:
        Variables:
          AIML_ASSESSMENT_BUCKET_NAME: !Ref AIMLAssessmentBucket
          TARGET_REGIONS: !Ref TargetRegions
      Policies:
        - S3CrudPolicy:
            BucketName: !Ref AIMLAssessmentBucket
        - Statement:
            - Sid: ComprehendReadPermissions
              Effect: Allow
              Action:
                - comprehend:List*
                - comprehend:Describe*
                - comprehend:Get*
              Resource: '*'

Step 3: Update AWS Step Functions Definition

Add the new service to the Run Security Assessments parallel branch inside the Scan Regions Map state in aiml-security-assessment/statemachine/assessments.asl.json. Also add the function ARN substitution and LambdaInvokePolicy for the new function in both SAM templates.

{
  "Parallel Service Assessments": {
    "Type": "Parallel",
    "Branches": [
      {
        "StartAt": "Bedrock Security Assessment",
        "States": {"Bedrock Security Assessment": {"Type": "Task", "Resource": "arn:aws:states:::lambda:invoke", "End": true}}
      },
      {
        "StartAt": "SageMaker Security Assessment",
        "States": {"SageMaker Security Assessment": {"Type": "Task", "Resource": "arn:aws:states:::lambda:invoke", "End": true}}
      },
      {
        "StartAt": "AgentCore Security Assessment",
        "States": {"AgentCore Security Assessment": {"Type": "Task", "Resource": "arn:aws:states:::lambda:invoke", "End": true}}
      },
      {
        "StartAt": "Comprehend Security Assessment",
        "States": {"Comprehend Security Assessment": {"Type": "Task", "Resource": "arn:aws:states:::lambda:invoke", "End": true}}
      }
    ]
  }
}

Step 4: Update AWS IAM Permissions

Add required permissions to every role that may deploy or run the new service assessment:

In deployment/1-aiml-security-member-roles.yaml:

- Effect: Allow
  Action:
    - comprehend:List*
    - comprehend:Describe*
    - comprehend:Get*
  Resource: '*'

In deployment/aiml-security-single-account.yaml (for single account mode):

- comprehend:List*
- comprehend:Describe*
- comprehend:Get*

In deployment/2-aiml-security-codebuild.yaml (for management-account multi-account mode):

- comprehend:List*
- comprehend:Describe*
- comprehend:Get*

Also add runtime permissions to the new Lambda role statements in both SAM templates if the new service function needs service-specific access at execution time.

Step 5: Test Locally

Test your new assessment function locally:

cd aiml-security-assessment
sam build --template template.yaml
sam local invoke ComprehendSecurityAssessmentFunction --event testfile.json

Assessment Best Practices

1. Security Check Implementation

2. Performance Optimization

3. Error Handling

try:
    # Assessment logic
    result = aws_client.describe_service()
except ClientError as e:
    # Access-denied and region-unsupported paths resolve to N/A, not Failed:
    # the check could not run, which is not a confirmed misconfiguration.
    if e.response["Error"]["Code"] in ACCESS_DENIED_ERROR_CODES:
        logger.warning(f"Access denied for service check: {str(e)}")
        return create_finding(
            finding_name="Permission Check",
            finding_details=describe_api_error(e, "Service check", region),
            resolution="Grant required permissions to assessment role",
            reference="https://docs.aws.amazon.com/service/permissions",
            severity="Medium",
            status="N/A",
            region=region,
        )
    else:
        # Handle other AWS errors
        logger.error(f"AWS API error: {str(e)}")
        raise
except Exception as e:
    # Handle unexpected errors
    logger.error(f"Unexpected error: {str(e)}", exc_info=True)
    raise

Testing Your Extensions

1. Local Testing

# Test an individual SAM function
cd aiml-security-assessment
sam build --template template.yaml
sam local invoke NewServiceSecurityAssessmentFunction --event test-event.json

2. Integration Testing

# Deploy to test account
sam deploy --stack-name aiml-security-test --capabilities CAPABILITY_IAM

# Execute AWS Step Functions
aws stepfunctions start-execution \
  --state-machine-arn arn:aws:states:region:account:stateMachine:TestStateMachine \
  --input '{"accountId":"123456789012","enableFinServ":"false","enableOWASP":"false"}'

3. Multi-Account Testing

  1. Deploy member roles to test accounts using AWS CloudFormation StackSets
  2. Deploy central infrastructure with test parameters
  3. Monitor AWS CodeBuild logs for deployment and execution status
  4. Verify results in central Amazon S3 bucket

Monitoring and Debugging

For detailed troubleshooting guidance, common issues, and debugging tips, see the Troubleshooting Guide.

Development Roadmap

Current Status

Potential Additions

Development Pattern

Report Generation Architecture

Shared Template Module

Report generation uses a single shared template (report_template.py) for both deployment modes:

aiml-security-assessment/functions/security/generate_consolidated_report/
├── app.py              # Lambda handler (single-account)
├── report_template.py  # Shared HTML/CSS/JS template
└── ...

consolidate_html_reports.py  # CodeBuild script (multi-account)

How It Works

Component Mode Description
app.py (AWS Lambda) mode='single' Generates per-account HTML reports during AWS Step Functions execution
consolidate_html_reports.py mode='multi' Consolidates all account reports in AWS CodeBuild post-build phase

Both call generate_html_report() from report_template.py with different parameters.

Modifying the Report Template

To update report styling, layout, or features:

  1. Edit report_template.py only - changes apply to both single and multi-account reports
  2. Run the report generator tests from the report package directory: python -m pytest test_generate_report.py -v
  3. Key functions:
    • get_html_template() - HTML/CSS/JS structure
    • generate_table_rows() - Finding row generation
    • generate_html_report() - Main entry point with mode parameter (‘single’ or ‘multi’)

Adding a Compliance Standard (OWASP-style)

The “By Compliance Standard” sidebar section is data-driven. Adding a new standard such as NIST AI RMF or the EU AI Act follows the OWASP pattern end-to-end. Concrete steps:

  1. Choose a 2–3 letter prefix that satisfies ^[A-Z]{2,3}-\d{2}$:
    • OWASP → OW- (already implemented)
    • NIST AI RMF → suggested NR-
    • EU AI Act → suggested EU-
  2. Create a new Lambda package under aiml-security-assessment/functions/security/<slug>_assessments/:
    • Copy owasp_assessments/schema.py verbatim.
    • Copy owasp_assessments/requirements.txt.
    • Author app.py following the OWASP pattern: read per-service CSVs from S3, apply your <STANDARD>_CHECK_MAPPINGS dict, run any native checks, write <slug>_security_report_<execution>_<region>.csv.
  3. Wire the CloudFormation opt-in parameter in seven places, following the EnableOWASPAssessment model:
    • deployment/aiml-security-single-account.yaml (parameter definition, parameter group, CodeBuild env var)
    • deployment/2-aiml-security-codebuild.yaml (parameter definition, parameter group, CodeBuild env var)
    • buildspec.yml (ENABLE_<STANDARD> export and the three aws stepfunctions start-execution calls)
    • aiml-security-assessment/template.yaml (new function resource, LambdaInvokePolicy, definition-substitution entry)
    • aiml-security-assessment/template-multi-account.yaml (same)
  4. Add IAM grants for any AWS APIs the new Lambda calls in all five policy locations per CLAUDE.md. Scope each grant correctly:
    • In aiml-security-assessment/template.yaml and aiml-security-assessment/template-multi-account.yaml, add the grant to the specific function’s Policies block that actually makes the call — not to another function’s policy.
    • In deployment/1-aiml-security-member-roles.yaml (cross-account member role), deployment/2-aiml-security-codebuild.yaml (local MemberRole mirror), and deployment/aiml-security-single-account.yaml (single-account role), append the actions to the assessment permissions policy. Diff 2-aiml-security-codebuild.yaml’s local MemberRole against 1-aiml-security-member-roles.yaml to confirm parity — this pair drifts easily.
  5. Add the Step Functions Choice state in aiml-security-assessment/statemachine/assessments.asl.json:
    • Add a <Standard> Enabled? Choice state that reads $.OriginalInput.enable<Standard>.
    • Add the <Standard> Security Assessment Task state that invokes the new function.
    • Chain: Run Security Assessments (Parallel) → OWASP Enabled? → … → <Standard> Enabled? → … → back to the region map end.

  6. Register the standard in the report by appending a new dict to COMPLIANCE_STANDARDS in report_template.py. Each entry needs slug, name, prefix, icon, icon_small, reference_url, section_title, and scope_text. Choose an icon colour that does not clash with --warning (used by “By Lens”), --accent (used by “By Industry”), or --success (used by OWASP).

  7. Caller routing is data-driven. The report generator (aiml-security-assessment/functions/security/generate_consolidated_report/app.py) and the multi-account consolidator (consolidate_html_reports.py) both iterate COMPLIANCE_STANDARDS to initialise service_stats / service_findings and to route by Check_ID prefix, so appending a new entry in step 6 is sufficient — no edits needed in these files.

  8. Update docs: add a SECURITY_CHECKS_<STANDARD>.md in the OWASP style, bump the check count in README.md and docs/SECURITY_CHECKS.md.

  9. Add tests: mapping emission, native-check behavior, routing, and report-template rendering. See tests/test_owasp_checks.py and tests/test_report_template_owasp.py as templates.

Documentation and Screenshots

Updating Sample Reports

When you modify the report template or add new features, update the sample reports and screenshots:

1. Generate New Sample Reports

After making changes to report_template.py, regenerate sample reports from a fresh assessment run or from the local report test fixtures. The existing test_generate_report.py file is a pytest/unittest test module, not a standalone --mode/--output CLI.

# Generate local viewable reports from fixtures
cd aiml-security-assessment/functions/security/generate_consolidated_report
python -m pytest test_generate_report.py -k "generate_viewable_report or generate_multi_account_report" -s

The fixture reports are written under aiml-security-assessment/functions/security/generate_consolidated_report/test_reports/. Use them to validate report rendering before refreshing the canonical files in sample-reports/.

2. Capture Screenshots

The repository includes an automated screenshot capture tool:

# Activate virtual environment
source .venv/bin/activate

# Install dependencies (first time only)
pip install -r sample-reports/dev-requirements.txt
playwright install chromium

# Capture and optimize screenshots
python sample-reports/scripts/capture_screenshots.py

What the script does:

What gets generated:

The script captures 4 screenshots:

All screenshots are automatically optimized (target: 200-300KB each, ~600KB total).

Customization:

Edit sample-reports/scripts/capture_screenshots.py to customize:

# Viewport size
VIEWPORT_WIDTH = 1440
VIEWPORT_HEIGHT = 900

# Image quality
JPEG_QUALITY = 85  # Range: 1-100
PNG_OPTIMIZE = True

# Add new screenshots to SCREENSHOTS list
SCREENSHOTS = [
    {
        "name": "my-screenshot",
        "file": "security_assessment_single_account.html",
        "description": "My Custom View",
        "actions": [
            {"type": "wait", "selector": ".element", "timeout": 2000},
            {"type": "click", "selector": ".button"},
            {"type": "scroll", "position": 500},
        ],
        "clip": {"x": 0, "y": 0, "width": 1440, "height": 800},
    }
]

Available action types:

Troubleshooting:

Issue Solution
playwright not installed pip install playwright && playwright install chromium
Sample reports not found Run from repository root
Screenshots too large Lower JPEG_QUALITY or reduce viewport size
Browser launch fails Run playwright install-deps (Linux only)

3. Update README

After generating new screenshots, update the README to reference them:

### Sample Assessment Reports

**Preview:**

![Executive Dashboard](/sample-aiml-security-assessment/sample-reports/dashboard-overview-light.png)
*Executive summary with severity counts and assessment-area breakdown*

![Findings Table](/sample-aiml-security-assessment/sample-reports/findings-table.png)
*Interactive findings table with filtering capabilities*

Documentation Best Practices

CI/CD Workflows

GitHub Actions workflows run automatically to validate code quality and security on every pull request.

PR Checks

Workflow File What It Checks
Python Code Quality .github/workflows/python-lint.yml ruff check (lint) and ruff format --check (formatting) on changed .py files
Python Tests .github/workflows/python-tests.yml Runs upstream tests, FinServ tests, and report-pipeline tests in separate pytest sessions
CloudFormation Lint .github/workflows/cfn-lint.yml Validates deployment and SAM templates with cfn-lint
SAM Validate & Build .github/workflows/sam-validate.yml Runs sam validate --lint and sam build on SAM templates
ASH Security Scan .github/workflows/ash-security-scan.yml Scans changed files for secrets, dependency vulnerabilities, and IaC misconfigurations

Additional workflows run post-merge or on schedule:

Workflow File Trigger
ASH Full Repository Scan .github/workflows/ash-full-repository-scan.yml Push to main, monthly schedule, manual
Labeler .github/workflows/label.yml Auto-labels PRs by changed paths (bedrock, sagemaker, agentcore, deployment, docs)

cfn-lint suppressions are configured in .cfnlintrc at the repository root for IAM actions not yet in cfn-lint’s database (for example, bedrock-agentcore actions).

Running Checks Locally

Before pushing, run these checks locally to catch issues early:

# Install tools (first time only)
pip install ruff cfn-lint
pip install -r tests/requirements.txt
pip install "pydantic>=2.0.0"

# Python lint and format
ruff check aiml-security-assessment/functions/security/
ruff format --check aiml-security-assessment/functions/security/

# Unit tests. Run these as separate pytest sessions because multiple
# assessment packages use top-level app.py imports.
export AIML_ASSESSMENT_BUCKET_NAME=test-assessment-bucket
export AWS_DEFAULT_REGION=us-east-1
export AWS_ACCESS_KEY_ID=testing
export AWS_SECRET_ACCESS_KEY=testing

python -m pytest tests/ -v --tb=short
python -m pytest aiml-security-assessment/functions/security/finserv_tests/ -v --tb=short
python -m pytest tests/test_consolidate_finserv.py -v --tb=short

cd aiml-security-assessment/functions/security/generate_consolidated_report
python -m pytest test_generate_report.py -v --tb=short
cd -

# CloudFormation lint
cfn-lint deployment/*.yaml
cfn-lint aiml-security-assessment/template.yaml
cfn-lint aiml-security-assessment/template-multi-account.yaml

# SAM validate and build
cd aiml-security-assessment
sam validate --template template.yaml --lint
sam validate --template template-multi-account.yaml --lint
sam build --template template.yaml
sam build --template template-multi-account.yaml

Support and Resources

Documentation

This developer guide provides the foundation for extending the AI/ML Security Assessment Framework. As you add new AI/ML services and security checks, please update this documentation to help future contributors understand and build upon your work.

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