Adaptive architecture governs AI use in real time with audit trails
Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement
Artificial Intelligence
Summary
Many organizations use AI but struggle to prove their systems follow rules quickly and clearly. The authors point out that the problem is not the technology itself but how companies organize and build their AI oversight. They suggest a new layered system called AGIL that detects hidden AI use, assesses risks, enforces policies instantly, creates secure audit records, and adapts rules across different places over time. This framework is a concept and has not yet been tested in real environments.
What this means in practice
- •For enterprise ai compliance teams: Ensure AI systems comply with rules by automatically detecting unauthorized AI use and enforcing policies within milliseconds.
- •For cloud security teams: Create tamper-evident audit logs for AI policy enforcement to support regulatory reporting and incident investigations.
A position paper. It proposes an approach and reports no results.
Authors
Sandeep Bokkasam, B. Durgalakshmi
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.