Full course coming soon

Designing AI Safety Gates for Product and Engineering Teams

7 chapters · 30 lessons

1. 1. Mapping AI Capabilities to Risk Profiles

   4 lessons

   Learn to assess what your AI system can do and identify the failure modes and misuse scenarios that matter most.

2. 2. Structuring Safety Evaluations That Test Real Risks

   5 lessons

   Design evaluation protocols that go beyond benchmarks to test the specific harms your system could cause.

3. 3. Building Decision Frameworks for Release Readiness

   4 lessons

   Create structured processes that turn safety evidence into clear deployment decisions with defined thresholds.

4. 4. Implementing Independent Review and Separation of Concerns

   4 lessons

   Structure review processes that separate shipping incentives from safety judgment and ensure accountability.

5. 5. Deploying with Controls and Monitoring Infrastructure

   5 lessons

   Learn to ship AI systems with runtime safeguards, usage limits, and observability that extends safety beyond the gate.

6. 6. Responding to Incidents and Feeding Lessons Back

   4 lessons

   Create incident response protocols that detect failures quickly, contain harm, and improve future gates.

7. 7. Adapting Gates for Agents, APIs, and Evolving Systems

   4 lessons

   Extend deployment gate frameworks to handle AI agents, third-party integrations, and systems that change post-release.