A comprehensive, twenty-part field guide to designing, governing, validating, and operating AI systems that regulators, risk functions, and customers can trust.
Lessons
- The Case for Regulated AI
- The Regulatory Landscape
- Risk Classification: Tiering AI by Impact
- Governance Foundations: Roles and Accountability
- Model Risk Management for AI
- Designing for Explainability from Day One
- Data Governance and Lineage
- Privacy, Lawful Basis, and Data Minimisation
- Fairness and Bias: Measurement and Mitigation
- Human-in-the-Loop Design
- Documentation and the Audit Trail
- Testing and Validation of AI Systems
- Agentic AI: Autonomy Under Guardrails
- Tooling, Permissions, and Blast-Radius Containment
- Security and Adversarial Robustness
- Deployment, Change Management, and Versioning
- Monitoring, Drift, and Continuous Validation
- Incident Response and Model Failure
- Third-Party and Foundation-Model Risk
- The Operating Model: Putting It All Together