Human‐Centric Security

Building on the foundational concepts of generative AI's role in enhancing security systems through user behavior analysis, this presents a comprehensive exploration into the transformative potential of AI technologies in crafting personalized and adaptive security frameworks. Through the in-depth analysis of behavioral biometrics integrated with generative AI models, this chapter highlights several outcomes: notably, a significant reduction in false positive rates in anomaly detection systems, enhanced detection of sophisticated cybersecurity threats through continuous learning and adaptation to new user behaviors, and an improvement in user experience by minimizing intrusive security measures. This work examines ways of lowering the false positive rates and enhancing the detection of advanced cybersecurity threats while applying behavioral biometrics coupled with Generative Adversarial Networks (GANs) and reinforcement learning models. Experimental analysis shows that the proposed approach reduces false positives in anomaly detection systems by 45%, thereby maintaining generative models on constant update as original user behaviors change over time. Also, the system boasted a 38% enhanced capability in detecting APT comprising of phishing, and social engineering because of its learning ability to analyze constant interaction by the end users. The integration of differential privacy also guarantees privacy preservation of users while achieving a 95% of the threats’ detection rate. Further, the use of artificial intelligence in designing the security framework promotes optimal and personal user experience with a minimum disruption to 20% in real-time security intervention. The potential of these AI-driven systems to understand and predict user actions with high accuracy suggests a paradigm shift towards more anticipatory security measures, which are discussed extensively. Furthermore, the chapter questions how these technologies might be best positioned in relation to ethical guidelines for security policies that support user autonomy and privacy and offers a conceptual model to mediate them.