AI Governance for Regulated Industries
AI governance means proving your AI systems operate within policy, producing auditable decisions, and meeting regulatory requirements. Learn how organizations build governance frameworks that regulators accept.
What Real AI Governance Requires
- Policy Enforcement at Inference Time: Governance policies must be evaluated and enforced at the moment the AI produces a decision, not reviewed after the fact. The policy version must be cryptographically bound to every inference.
- Auditable Decision Records: Every AI decision must produce a tamper-evident record that captures what the model decided, what confidence level it reported, and what action was taken.
- Model Version Tracking: Regulators want to know which exact model version produced a specific decision. Governance requires cryptographic binding between each inference and the model binary.
- Regulatory Evidence on Demand: When an auditor requests evidence, you need to produce it in hours, not weeks.
- Continuous Monitoring and Drift Detection: Models degrade over time. Governance requires ongoing monitoring of decision patterns and alerts when behavior drifts outside approved parameters.
Governance by Industry
How InferTrust Implements AI Governance
InferTrust creates the infrastructure for enforceable AI governance by cryptographically signing every decision at the point of inference, binding model version, policy version, and confidence score into tamper-evident records that regulators accept as evidence.
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