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Deploying AI Inside Regulated Environments

7 chapters · 32 lessons

1. 1. Control Boundaries and Data Custody Patterns

   4 lessons

   Understand how to define and enforce boundaries that keep sensitive data under organizational control while enabling AI capabilities.

2. 2. Architecting Inference Pipelines for Controlled Environments

   5 lessons

   Design inference systems that operate within strict boundaries while maintaining performance and reliability.

3. 3. Policy Enforcement and Access Governance

   5 lessons

   Implement technical controls that govern what data AI systems can access and what actions they can perform.

4. 4. Retrieval and Knowledge Systems Under Compliance

   4 lessons

   Build retrieval-augmented generation and knowledge systems that respect data boundaries and regulatory requirements.

5. 5. Audit Trails and Explainability for Regulated AI

   5 lessons

   Create logging, monitoring, and explainability systems that satisfy regulatory and operational requirements.

6. 6. Operational Resilience and Disaster Recovery

   4 lessons

   Ensure AI systems remain available, recoverable, and compliant under operational stress and failure scenarios.

7. 7. Real-World Deployment Patterns and Case Studies

   5 lessons

   Apply sovereign AI principles to fraud detection, risk modeling, customer service, and other regulated use cases.