PREDICTIVE COMPLIANCE AUTOMATION USING NLP AND POLICY-AS-CODE

Abstract: Modern enterprises operate under an expanding set of regulatory, security, and internal governance obligations while simultaneously adopting cloud-native architectures, continuous delivery pipelines, and decentralized engineering ownership models. This convergence has transformed compliance from a periodic audit function into a persistent systems reliability problem. Traditional compliance mechanisms characterized by manual policy interpretation, static control checklists, and retrospective audits are fundamentally misaligned with the velocity, scale, and dynamism of contemporary infrastructure and software systems. As a result, organizations experience delayed violation detection, excessive operational toil, fragmented accountability, and elevated systemic risk. This paper introduces a Predictive Compliance Automation Framework (PCAF) that reframes compliance as a continuous, anticipatory control plane embedded within enterprise platforms. The framework integrates Natural Language Processing (NLP) techniques for structured interpretation of regulatory and policy text with Policy-as-Code (PaC) for executable enforcement, and augments these capabilities with predictive risk modeling to identify likely future compliance drift before violations occur. Unlike existing approaches that rely on reactive rule evaluation or static guardrails, PCAF enables proactive compliance posture management by correlating policy semantics, infrastructure change events, telemetry signals, and historical drift patterns. The primary contribution of this work is a systemic architecture that unifies regulatory cognition, automated enforcement, and predictive governance within a single operational framework. We describe the design principles, layered architecture, lifecycle flows, and governance mechanisms required for safe enterprise deployment, emphasizing human-in-the-loop oversight, auditability, and failure containment. Operational evaluation demonstrates meaningful reductions in mean time to detection (MTTD), compliance drift duration, and manual audit effort, while preserving accountability and regulatory interpretability. This work positions predictive compliance as a first-class reliability function, analogous to availability and security, and outlines a path toward scalable, resilient governance in complex distributed systems.