A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI
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The paper introduces a design‑science framework for ensuring legacy, governance, and decision integrity in enterprise AI systems. It defines a normalized Legacy Score based on knowledge retention, governance, oversight, adaptability, feedback learning, and jurisdictional fidelity, along with Decision Confidence and Decision Risk models, authority‑aware retrieval, Decision Memory, Regulatory Change Velocity, and a federated regulatory knowledge‑graph architecture. The authors also propose eight AI Decision Integrity Rules, an evaluation protocol, and a reproducible computational demonstration using stress tests and Monte Carlo simulations to illustrate the framework’s properties.
The paper "Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement" highlights a gap in enterprise AI governance, where 78% of organizations lack auditable evidence of policy enforcement. It introduces AGIL, a five-layer adaptive governance architecture that uses machine learning for real-time detection, risk classification, sub-100ms policy enforcement, continuous attestation, and policy evolution. The authors argue that the failure is organizational and architectural, not technical, and call for future empirical validation of AGIL.
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