International Journal of Science, Engineering and TechnologyAuthors: Aparna L. Menon
Abstract: The increasing sophistication and frequency of cyber threats have exposed the limitations of traditional, reactive cybersecurity measures, which often rely on human intervention and static defenses. Organizations face dynamic risks, including malware, ransomware, zero-day exploits, insider threats, and advanced persistent threats, all of which can compromise system integrity, confidentiality, and availability. Self-healing cybersecurity frameworks offer a proactive and autonomous approach to risk mitigation by continuously monitoring systems, detecting anomalies, analyzing threats, and initiating corrective actions without manual intervention. By integrating artificial intelligence, machine learning, and automation, these frameworks can isolate compromised components, deploy patches, restore configurations, and adapt security policies dynamically, thereby reducing the attack surface and minimizing potential damage. This review examines the influence of self-healing cybersecurity frameworks on enterprise risk mitigation, exploring their conceptual foundations, architectural designs, operational benefits, applications, limitations, and emerging trends. Case studies and empirical research highlight measurable improvements in threat containment, system availability, and operational resilience. The review also addresses challenges, including scalability, false positives, AI reliability, and organizational adoption barriers. Finally, it discusses future directions, emphasizing AI-driven predictive security, integration with zero-trust architectures, cloud-native deployments, and explainable AI for regulatory compliance. Overall, self-healing cybersecurity frameworks represent a transformative approach to enterprise security, enabling adaptive, intelligent, and resilient systems capable of mitigating evolving cyber risks efficiently and effectively.
DOI: https://doi.org/10.5281/zenodo.17798392
