GitHub Actions for AI Safety Scanning

GitHub Actions integrates safety validation directly into your development workflow. Every pull request that modifies policies, agent configurations, or prompt templates gets validated automatically before merge.

Basic Policy Validation Workflow

Create a workflow file that triggers on pull requests affecting your policy directory. The workflow installs Node.js, runs npx authensor policy validate against changed policy files, and npx authensor policy test against your test suite.

The key steps are: checkout the repository, set up Node.js 20, install dependencies, and run the validation commands. Use paths filtering to trigger only when relevant files change.

Red Team Scanning Workflow

A separate workflow runs your red team harness against the full safety stack. This takes longer than policy validation, so trigger it on pushes to main and on a nightly schedule rather than on every PR.

The workflow starts Authensor's control plane in test mode, loads your production policies, and runs authensor test red-team which replays your attack pattern library and reports results.

Secret Management

Store any required API keys or database credentials as GitHub Actions secrets. Reference them as environment variables in your workflow. Never hardcode credentials in workflow files.

For testing, use Authensor's in-memory mode which does not require a database connection. This simplifies CI setup and speeds up test execution.

Caching

Cache the node_modules directory between workflow runs using GitHub's cache action. Authensor's dependencies are stable between versions, so cache invalidation on lockfile changes works well.

Status Checks

Configure the policy validation workflow as a required status check for your main branch. This prevents merging PRs that break safety policies.

Add a badge to your repository showing the safety validation status. This signals to contributors that safety is taken seriously.

Scheduled Scanning

Run a weekly workflow that pulls the latest attack patterns from public prompt injection databases and tests them against your safety configuration. This catches drift between your defenses and the evolving threat landscape.

Generate a markdown report summarizing scan results and create an issue if any new patterns bypass your defenses. This feeds directly into your policy maintenance workflow.