AI Secure Development Lifecycle (AI-SDLC)

Best time to learn: After your projects have automated tests and CI, before treating any project as production-ready.

Prerequisites - Git and code review. - Unit/integration tests and CI. - Docker image builds. - Model, prompt, dataset, and evaluation versioning.

### Threat model

Security must persist through changes to code, dependencies, model versions, prompts, tools, datasets, infrastructure, and providers. A secure release process needs to detect changes that invalidate prior security evidence.

Study: - SAST and security-focused code review. - Software composition analysis (SCA), lockfiles, and dependency pinning. - Secret scanning and rotation. - Container image scanning and provenance. - Infrastructure-as-code scanning where IaC is used. - API security testing and DAST concepts. - Model, prompt, tool-schema, dataset, and evaluation versioning. - Security regression gates for model/provider changes. - SBOM and model-artifact inventory concepts. - Release approval, rollback, and incident response.

### Labs

• Pipeline inventory (1h): Map code, dependencies, containers, models, prompts, tools, datasets, and deployment configuration.

• Static and dependency checks (2h): Add a SAST or lint security check, dependency vulnerability scan, and secret scan to CI.

• Artifact checks (1h): Scan built container images and record model artifact versions/hashes.

• AI regression gate (2h): Run a compact security evaluation suite when prompt, model, provider, tool schema, or retrieval code changes.

• Release drill (1h): Simulate a vulnerable model/provider update; block or roll back the release and preserve evidence.

### Project integration

• All three projects: A pull request should run relevant unit tests, authorization tests, prompt-injection regression tests, dependency checks, and a compact model evaluation.

• Release process: A model/provider change is a security-relevant change, not merely a configuration edit.

### Measurable completion criteria

• CI detects a deliberately introduced secret or vulnerable dependency in a controlled test.

• Model and prompt versions are recorded with evaluation results.

• A failed security regression blocks a release or triggers a documented approval path.

• Rollback procedure is documented and tested.

• Security evidence is tied to a specific build and model configuration.

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