AI governance

Embedding oversight, risk controls, and compliance logic directly into AI system architecture.

Core structural requirements for AI systems operating under material risk and regulatory oversight.

Fairness &

Non Discrimination

Mitigate systemic bias through validated evaluation frameworks and documented fairness controls.

Transparency &

Explainability

Ensure traceable decision pathways and audit-ready model documentation.

Privacy &

Security

Embed secure architecture and compliance-aligned data governance.

Human Oversight &

Accountability

Establish escalation mechanisms and clearly defined responsibility structures for high-impact systems.

Simulate how design decisions impact oversight, risk exposure, and regulatory alignment in high-stakes environments.

This interactive model illustrates how trade-offs across fairness, transparency, privacy, and accountability alter institutional risk.

Small adjustments in architecture can materially shift compliance posture.

Use the controls to stress-test governance assumptions. Then ask:

Would this system withstand regulatory scrutiny?
Would it pass a model risk review?
Would it remain defensible under audit?