Why Auditability Fails in Current Generative AI Systems: A Governance Perspective

Abstract

Generative AI systems are deployed faster than governance infrastructure can verify them. This paper examines structural gaps in auditability — opaque model behavior, weak evidence chains, and compliance claims that cannot be reproduced — and outlines how policy-enforced evidence with deterministic verdicts supports enterprise-grade governance.

Key insights

• Current generative AI stacks often produce outputs without append-only, policy-bound evidence that auditors can replay.
• Compliance posture degrades when verdicts depend on reconstructed logs rather than a single authoritative compliance projection.
• EU AI Act and ISO/IEC 42001 alignment require traceable controls — not marketing claims disconnected from runtime enforcement.
• Deterministic VALID · INVALID · BLOCKED outcomes, tied to hash-chained records, reduce ambiguity for security and compliance reviewers.

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