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AI Governance

From policy to proof.

From static compliance to cryptographic runtime governance. Provable governance for defense, critical infrastructure, and enterprise AI.

Background

The evolution of AI governance.

AI governance began as model governance: documenting training data provenance, validating bias metrics, and publishing model cards. That model assumed a human reviewed every output before it reached production. It worked when models were tools. It breaks when models become agents.

The shift from governing outputs to governing actions is the defining inflection point. Agentic AI systems invoke tools, modify state, chain decisions across sessions, and operate without human confirmation loops. Governance must now cover what an agent does, not just what it says.

Regulatory frameworks reflect this shift. The NIST AI Risk Management Framework requires continuous monitoring and measurable governance controls. The EU AI Act mandates risk management systems that operate throughout the AI lifecycle, not just at deployment. CISA’s Secure by Design principles call for demonstrable runtime controls, not self-reported compliance.

The industry is moving from governance-at-rest to governance-in-motion: from policy documents that describe intent to cryptographic mechanisms that prove what actually happened. A document repository cannot produce that proof on demand.

The Gap

Why current AI governance falls short at runtime.

Policy documents describe governance intent. Periodic audits sample compliance at a point in time. Dashboard monitoring observes and alerts. None of these mechanisms is designed to prove what actually occurred at the moment it mattered.

The gap between governance policy and provable compliance widens with every increase in agent autonomy. An AI agent that invokes thirty tools across five services in a single session generates no cryptographic evidence that its actions stayed within approved boundaries. Logs record what happened. They do not prove what was permitted to happen.

This is an architectural gap, not a tooling gap. Current governance architectures were designed for systems where humans were in the loop. Autonomous agents require governance that operates at machine speed, generates machine-verifiable evidence, and renders decisions without waiting for human review.

The Answer

Cryptographic runtime governance.

Cryptographic Runtime Governance (CRG) closes the gap between governance policy and provable compliance. Built on Attested Governance Artifacts (AGA), CRG operates in three phases.

01Seal

Governance parameters are cryptographically sealed into a tamper-evident, signed artifact before the subject executes.

02Capture

Runtime measurement continuously compares live state against the sealed reference and renders and seals the artifact-specified decision; the handlers that effect it are wired per deployment.

03Prove

Every measurement cycle produces a signed receipt chained into tamper-evident, offline-verifiable evidence.

The result is a cryptographic proof chain, not a compliance report, and anyone can verify it, including in air-gapped environments with zero connectivity to the issuing system.

Verticals

AI governance by vertical.

Six deployment domains. The same cryptographic primitives meet the requirements of each.

Next

Move from policy to proof.

Governance you can cryptographically verify is governance you don't have to take on trust. Explore agentic governance or find the solution for your vertical.