An Ai-Powered Automation Framework For Real Time Cybersecurity Risk Governance And Resilience

Abstract

The escalating sophistication of cyber threats, combined with the convergence of Information Technology (IT) and Operational Technology (OT), has rendered traditional security measures inadequate for real-time enterprise protection. This research develops and evaluates an AI-powered automation framework for real-time cybersecurity risk governance and resilience, addressing fragmentation in current tools, lack of explainability, and challenges in compliance and adaptability.
Grounded in Design Science Research (DSR) methodology and informed by decision theory, control theory, socio-technical systems theory, game theory, and complexity theory, the study integrates advanced AI models—Support Vector Machines (SVM), Random Forests, and Recurrent Neural Networks (RNN)—with explainability mechanisms such as SHAP and LIME. The framework unifies threat detection, automated response, continuous learning, and governance dashboards, supporting hybrid IT/OT environments and aligning with standards such as NIST CSF, ISO/IEC 27001, and GDPR.
Quantitative evaluation leveraged benchmark datasets (NSL-KDD, CICIDS2017, UNSW-NB15) and synthetic OT logs to test detection accuracy, latency, and resilience under stress conditions. Results show high detection accuracy (F1-scores ≥ 0.95) with reduced mean time to detect (MTTD) and mean time to respond (MTTR) compared to conventional systems. Qualitative insights from cybersecurity experts validated architectural scalability, explainability, and governance readiness, highlighting reductions in alert fatigue and improved decision confidence.
Key findings include: (i) orchestration of AI models in microservices reduces response latency and improves adaptability; (ii) modular architecture supports integration of IT and OT pipelines; (iii) feedback-driven retraining mitigates concept drift and enhances model longevity; and (iv) governance dashboards deliver real-time compliance and risk insights, fostering trust and executive oversight.
This study contributes to theory by integrating socio-technical and governance perspectives into AI cybersecurity and advancing continuous learning approaches. Practically, it offers a deployable framework that reduces operational workload, enhances resilience, and aligns cybersecurity with enterprise strategy. Policy implications include operationalizing ethical AI in cybersecurity and informing standards for AI-driven governance in critical infrastructures.