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Security: aws-samples/sample-agentic-value-accelerator

Security

SECURITY.md

Security Policy

Reporting a Vulnerability

We take security seriously at AVA. If you discover a security vulnerability, please report it responsibly.

How to Report

Please do NOT report security vulnerabilities through public GitHub issues.

Instead, please report them via one of the following methods:

  1. Email: Send details to the repository maintainers
  2. GitHub Security Advisories: Use the "Report a vulnerability" button in the Security tab

What to Include

When reporting a vulnerability, please include:

  • Description of the vulnerability
  • Steps to reproduce the issue
  • Potential impact assessment
  • Any suggested fixes (optional)

Response Timeline

  • Initial Response: Within 48 hours
  • Status Update: Within 7 days
  • Resolution Target: Based on severity (Critical: 7 days, High: 30 days, Medium: 90 days)

Security Best Practices

AWS Credentials

This project is designed to use AWS IAM roles and the default credential chain. Never hardcode AWS credentials in configuration files or source code.

Recommended credential methods:

  • IAM roles for Amazon EC2
  • IAM roles for AWS Lambda
  • IAM roles for Amazon ECS tasks
  • Environment variables (for local development only)
  • AWS CLI configuration profiles

Deployment Security

When deploying this solution:

  1. Network Security: Deploy in private subnets where possible; use VPC endpoints for AWS services
  2. IAM Policies: Review and customize IAM policies to follow least privilege principles
  3. Encryption: Enable encryption at rest and in transit for all data stores
  4. Logging: Enable CloudTrail and access logging for audit purposes
  5. Authentication: Implement authentication for API endpoints (not included by default)

AI/ML Security Considerations

This project uses Amazon Bedrock for AI/ML capabilities. Important considerations:

  1. Human Review: AI outputs are advisory and should be reviewed by qualified personnel
  2. Input Validation: Validate and sanitize inputs before sending to AI models
  3. Output Filtering: Review AI outputs before using in production workflows
  4. Prompt Injection: Be aware of prompt injection risks; implement appropriate safeguards
  5. Data Privacy: Ensure customer data handling complies with applicable regulations

Risk Assessment Summary

Architecture Overview

The AVA deploys a multi-agent AI system with the following components:

  • Compute layer (Amazon EC2, AWS Lambda, or AgentCore Runtime)
  • AI layer (Amazon Bedrock with Claude models)
  • Data layer (Amazon S3 for customer data)

Identified Risks and Mitigations

Risk Category Risk Mitigation
Authentication API endpoints lack built-in auth Deployers must implement authentication
Data Security Customer data in S3 Encryption at rest, TLS enforcement, access logging
IAM Overly permissive policies Scoped policies with least privilege
AI/ML Prompt injection Input validation, output review
AI/ML Biased outputs Human review required for financial decisions

Compliance Considerations

For financial services deployments, consider:

  • Fair lending regulations (ECOA, Fair Credit Reporting Act)
  • Data privacy regulations (GDPR, CCPA)
  • Industry-specific requirements (PCI-DSS if handling payment data)

Supported Versions

Version Supported
1.x ✅

Security Updates

Security updates are released as patch versions. We recommend:

  • Subscribing to repository notifications
  • Regularly updating dependencies
  • Reviewing release notes for security-related changes

Acknowledgments

We appreciate the security research community's efforts in responsibly disclosing vulnerabilities.

There aren't any published security advisories