Enterprise AI adoption has reached 78% globally, yet governance infrastructure lags behind.
The paper identifies an “attestation deficit”: organizations keep policies but cannot produce auditable, tamper‑evident evidence within regulatory timelines.
Three empirical sources are cited: Stanford 2026 AI Index (362 incidents); IBM/Ponemon 2026 breach cost study (average $4.99 M, 92% lacking access controls); EY/AIUC‑1 survey (38% end‑to‑end monitoring, 17% agent‑to‑agent coverage).
These figures indicate that governance failure is primarily organizational and architectural, not purely technical.
To close the gap, the authors propose AGIL (Adaptive Governance Intelligence Layer), a five‑layer architecture that leverages machine learning for real‑time AI governance.
The layers are: 1) Autonomous Discovery – behavioral fingerprinting to spot shadow AI; 2) Behavioral Risk Classification – unified scoring for security, hallucination, privacy, accountability; 3) Policy Enforcement Gateway – inline permit/deny/modify decisions under 100 ms latency; 4) Continuous Attestation Engine – generates tamper‑evident audit trails as a by‑product of enforcement; 5) Adaptive Policy Intelligence – ML‑driven policy evolution across jurisdictions.
AGIL remains a theoretical framework; controlled deployment and empirical validation are left for future work.
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