A Data-Driven Architecture for Preemptive Cyber Defense Using AI-Based Governance and Autonomous Remediation
DOI:
https://doi.org/10.15662/IJEETR.2021.0306010Keywords:
Preemptive cybersecurity, AI governance, security data fabric, agentic AI, cyber risk prediction, autonomous remediationAbstract
Modern cybersecurity programs remain largely reactive despite extensive investment in detection and response technologies. Fragmented tooling, delayed reporting cycles, and limited executive visibility prevent organizations from managing cybersecurity as a predictive and governable system. This paper proposes a data-driven preemptive cyber defense architecture that integrates heterogeneous security telemetry into a unified security data fabric and applies artificial intelligence (AI) for continuous governance scoring, risk forecasting, and autonomous remediation orchestration. The framework introduces formal threat modeling, knowledge-graph–based correlation, reinforcement learning optimization, explainable AI governance, uncertainty modeling, and safety-bounded agentic automation. Experimental evaluation demonstrates improvements in risk visibility, governance efficiency, and decision latency compared with traditional SIEM, SOAR, and GRC approaches. The results suggest a structural shift from reactive cybersecurity operations toward predictive and partially autonomous cyber defense.
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