[Submitted on 11 Sep 2026]
Title:Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement
View a PDF of the paper titled Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement, by Sandeep Bokkasam and B. Durgalakshmi
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Abstract:Enterprise AI adoption has reached 78% of organizations globally, yet the infrastructure to govern that adoption has not kept pace. This paper identifies and characterizes the attestation deficit, a structural condition in which organizations maintain governance policies but cannot produce auditable, tamper-evident evidence of enforcement within regulatory timelines. Drawing on empirical data from the Stanford 2026 AI Index Report (362 documented incidents), the IBM/Ponemon 2026 Cost of a Data Breach study (USD 4.99M average cost, 92% lacking access controls), and the EY/AIUC-1 Consortium survey (38% end-to-end monitoring, 17% agent-to-agent coverage), this paper demonstrates that the governance failure is organizational and architectural rather than technical. To address this deficit, we propose AGIL (Adaptive Governance Intelligence Layer), a conceptual five-layer architecture designed to use machine learning for real-time AI governance enforcement. The proposed layers include: (1) Autonomous Discovery for shadow AI detection via behavioral fingerprinting, (2) Behavioral Risk Classification unifying security, hallucination, privacy, and accountability scoring, (3) a Policy Enforcement Gateway for inline permit/deny/modify decisions at sub-100ms latency, (4) a Continuous Attestation Engine generating tamper-evident audit trails as a byproduct of enforcement, and (5) Adaptive Policy Intelligence for ML-driven policy evolution across jurisdictions. AGIL is presented as a theoretical framework and architectural proposal; empirical validation through controlled deployment remains a direction for future work.
Comments: 9 pages, 3 tables, 20 references
Subjects:
Artificial Intelligence (cs.AI)
ACM classes: K.6.5; K.4.1; I.2.1
Cite as: arXiv:2609.13466 [cs.AI]
(or arXiv:2609.13466v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.13466
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sandeep Bokkasam Mr [view email] [v1] Fri, 11 Sep 2026 19:35:00 UTC (13 KB)
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