AI-Powered Threat Modeling

Threat Technique Catalog for AWS: AWS Samples - Threat Technique Catalog

Threat modeling should be integrated directly into the design phase of the Secure Software Development Lifecycle (SSDLC).

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Requirements ➔ Design (Threat Modeling Happens Here) ➔ Development ➔ Testing ➔ Deploy ➔ Monitor


SSDLC Threat Modeling Timeline

1. Before Design

  • Identify trust boundaries.
  • Classify data sensitivity.
  • Spec out security requirements.

2. During Design (Core Threat Modeling)

  • Model System Architecture: Map components and interactions.
  • AI-Assisted Threat Analysis: Let LLMs help discover edge-case threats.
  • Map STRIDE per Component: Group threats into the STRIDE categories:
    • Spoofing identity
    • Tampering with data
    • Repudiation
    • Information disclosure
    • Denial of service
    • Elevation of privilege
  • Define Mitigations Early: Document counter-measures before code construction begins.

3. After Design

  • Threats Become Test Cases: Write automated security tests based on threats.
  • Integrate into PR Reviews: Use threat models to guide pull request evaluations.
  • Feed into Penetration Testing: Provide the threat catalog to penetration testers to focus their scope.

Tooling: Threat Composer AI & Kiro

  • Threat Composer AI:
    • Powered by Bedrock Claude 3.5 Sonnet (or Sonnet 4) with pay-as-you-go token pricing (Beware of the cost!).
    • Uses 8 specialized agents working in parallel.
    • Equipped with a CLI and an MCP (Model Context Protocol) server.
    • Automatically parses CloudFormation templates or Terraform code to produce architecture and dataflow diagrams.
  • Kiro CLI:
    • If you have an existing architecture diagram, you can upload it to Kiro to perform visual threat modeling and analyze data flows.

TIP

Start Small: A quick, 15-minute threat modeling session during design is infinitely better than no threat model at all. Don’t let perfection be the enemy of security.