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SecureGRC Blog: AI Compliance & Evidence Integrity

Longer-form writing on how AI compliance evidence is produced, signed and kept verifiable.

This is where SecureGRC works through the architectural arguments behind the platform at more length than a product page allows — why compliance evidence should be derived from metadata rather than model access, what an audit trail has to survive to remain useful in ten years, and where the current generation of AI governance tooling stops short. Posts are infrequent and long rather than frequent and short.

Metadata-only compliance

How metadata-only architecture proves AI compliance without ingesting model weights or training data — ML-BOMs, TCCE, and Dilithium-signed verifiable evidence.

Related reading

If you are new to the material, the reference pages cover the same ground more systematically: ISO/IEC 42001 explained, what an ML-BOM is, quantum-safe compliance, and the glossary for individual terms. The FAQ answers the questions that come up most often.

See it against your own AI systems

Request a demo and we will walk through an ML-BOM, a TCCE run and a signed evidence bundle for a system you actually operate.

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