NEWS · RESEARCH · #155
arXiv preprint proposes AGIL, a five‑layer ML architecture for real‑time AI policy enforcement and attestation
This arXiv cs.AI preprint identifies an "attestation deficit" in enterprise AI governance and proposes AGIL (Adaptive Governance Intelligence Layer), a theoretical five-layer architecture that uses ML for real-time policy enforcement, inline permit/deny/modify decisions at sub-100 ms latency, and continuous tamper-evident attestation; the authors draw on reported industry data (e.g., 78% AI adoption, 362 incidents cited) but note that empirical validation through controlled deployment is future work.
KEY POINTS
- This arXiv cs.AI preprint identifies an "attestation deficit" in enterprise AI governance and proposes AGIL (Adaptive Governance Intelligence Layer), a theoretical five-layer architecture that uses ML for real-time policy enforcement, inline permit/deny/modify decisions at sub-100 ms latency, and continuous tamper-evident attestation; the authors draw on reported industry data (e.g., 78% AI adoption, 362 incidents cited) but note that empirical validation through controlled deployment is future work.
- If validated, AGIL could address a widely reported gap in auditable, tamper-evident enforcement of AI policies in enterprises by combining real-time controls with attestation, though the proposal is currently conceptual.
- Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement
WHY IT MATTERS
If validated, AGIL could address a widely reported gap in auditable, tamper-evident enforcement of AI policies in enterprises by combining real-time controls with attestation, though the proposal is currently conceptual.