AI-Enhanced Endpoint Compliance and Automated Vulnerability Management Framework for Essential Government Infrastructure

Public sector IT infrastructures that underpin essential services, such as transportation and law enforcement, are becoming progressively susceptible to advanced cyber attacks and encounter heightened regulatory demands, especially in accordance with CJIS and NIST standards. Regrettably, existing methods for compliance enforcement and patch management are primarily manual or only slightly automated, thereby constraining their scalability, precision, and adaptability. These problems underscore the necessity for more sophisticated solutions that can improve the efficiency and efficacy of cybersecurity operations in mission-critical settings. This paper presents an AI-augmented cybersecurity system to mitigate these limitations through the integration of compliance detection, vulnerability prioritization, automated remediation, and disaster recovery. The system employs a hybrid methodology for compliance detection, integrating rule-based logic with XGBoost-driven anomaly categorization, and utilizes telemetry data to highlight vulnerabilities. It automates patch deployment with SCCM and PowerShell, and integrates predictive disaster recovery orchestration with real-time audit dashboards. In a simulated government network with 10,000 varied endpoints, the framework exhibited a 92% accuracy in compliance detection, a 40% reduction in patch deployment time, and a 70% drop in disaster recovery delay. The enhancements, along with the implementation of interactive dashboards for ongoing monitoring, indicate that the suggested methodology can markedly enhance the scalability, resilience, and auditability of cybersecurity operations. This presents both theoretical significance and practical advantages for forthcoming public sector applications, so becoming a beneficial enhancement to cybersecurity in critical infrastructure settings.