AI-Augmented Compliance Automation: A Machine-Learning Model for Continuous Vulnerability Remediation and Audit Readiness

Abstract

Automation of security compliance is limited by the need for continuous monitoring and remediation of vulnerabilities, the foundation of almost all security-related policies. By explicitly linking vulnerability management and policy documentation, a machine-learning model enables not just vulnerability-closure workflows, but also automatic generation of audit-related artifacts—such as evidence of compliance, demonstrable signature of key decision-making, and policy-enforcement operation logs—that together facilitate continuous audit readiness. Orchestration of remediation efforts using risk scores derived from machine-learning-based security analyses allows justification of prioritization decisions based on changes in policy context.

Persistence of security risks and resource constraints often lead organizations to prioritize remediation of vulnerabilities with known exploits over true risk, leaving the most dangerous unpatched. Integrating alerting and remediation capabilities into one artifact provides a crucial foundation to support automated triage and remediation orchestration, ensuring that even vulnerabilities without obvious management precedence receive timely remediation without draining resources. The ability to generate remediation is augmented with a second component: support for the production of evidence and process-mapping artifacts needed for audit readiness. Audit preparation and compliance validation are two of the biggest operational burdens organizations face, yet both can be partly automated through continuous monitoring of infrastructure changes and of knowledge repositories, such as audit logs and change approval records.