AGIL: An Adaptive Architecture for Real-Time AI Policy Enforcement

Summary

This arXiv paper examines what it calls the “attestation deficit”: organizations may have AI governance policies but lack auditable, tamper-evident evidence that those policies were enforced within regulatory timelines. It cites data from the 2026 Stanford AI Index, the 2026 IBM/Ponemon Cost of a Data Breach study, and an EY/AIUC-1 Consortium survey to describe gaps in incidents, access controls, monitoring, and agent-to-agent coverage. The authors argue that the resulting governance failure is organizational and architectural, rather than solely a technical problem. They propose AGIL, or Adaptive Governance Intelligence Layer, as a conceptual five-layer architecture using machine learning for real-time enforcement. Its layers cover discovery of shadow AI through behavioral fingerprinting, unified behavioral risk classification, an inline gateway for permit, deny, or modify decisions at sub-100ms latency, continuous generation of tamper-evident audit trails, and machine-learning-assisted policy evolution across jurisdictions. AGIL is presented as a theoretical framework and architectural proposal, not a validated product. Controlled deployment and empirical validation remain future work.