AI Driven Self-Healing Cybersecurity Systems with Agentic AI for Adaptive Threat Response and Resilience

This study looks into the development of a selfhealing security system which is integrated with Agentic AI, enabling it to have an autonomous detection, mitigation and adaptation to ever changing cyber threats. The study uses advanced methodologies, spanning from Machine Learning (ML) to help in anomaly detection, Deep Reinforcement Learning (DRL) to handle adaptive threat modelling and response and autonomous security orchestration to help with real time incident management approaches. The results from the study indicate significant changes in cyber security operations with increased threat detection and rate of 96.8%, 75% improvement in system recovery time and 60% reduction in system downtime. The results indicate that the use of AI enabled security system advanced over the manual response mechanisms. Using an AI enabled self-healing security system reduces the incidence of false positives and negatives with a margin of 40% to 35% indicating a change of operational efficiency and reliability. Thus, this study concludes that the inclusion of Agentic AI is a transformative factor in handling cybersecurity resilience by offering efficient and scalable solutions contrary to traditional approaches. Moreover, this study posits that future research should look into improving transparency in decision making, working on hybrid working models and ensuring robust nature of selfhealing security systems.