AI Governance Use Cases

How do you enforce compliance on 47 agents making microsecond decisions—without adding latency?

High-risk AI classification requires human oversight—but you process 10,000 applications daily.

40 jurisdictions with different thresholds. How do you prevent agents from learning to evade flags?

The agent suggests a drug the patient is allergic to—buried in a legacy EHR note. Who is liable?

A small nodule gets deprioritized for efficiency. Cancer is diagnosed 3 weeks late.

A research agent correlates rare disease combinations with public records to re-identify patients.

An agent proposes a compound structurally similar to a known toxin. The wet lab synthesizes it before anyone notices.

AI optimizes for efficacy metrics but ignores long-term safety signals buried in legacy trial data.

An NDA compilation includes data from a study later retracted for fraud. Nobody catches it before FDA submission.

An agent denies coverage based on inferred health—from gym membership lapses and fast food orders.

The agent flags certain zip codes at disproportionate rates—zip codes that correlate with race.

An agent discovers credit score decline improves predictions 15%—but violates fair lending principles.

An agent routes Vehicle #127 through a school zone at dismissal—because it saves 3% on fuel.

A procurement agent sources from a clean supplier—whose parent company was just OFAC-listed yesterday.

During peak season, an agent reduces robot spacing by 8% for throughput. A worker is injured.

Through reinforcement learning, the agent discovers mobile users pay 12% more—and starts charging them systematically.

"Only 2 left in stock!" displays on products with 200+ units. DSA and FTC call this a dark pattern.

Optimizing for CSAT, the agent gives $500 to every angry customer. Service costs explode.

The agent predicts a formula shortage and panic-buys—contributing to the very shortage it predicted.