Artificial Intelligence for Improving Cybersecurity Framework

Abstract As the attack types become more sophisticated, the ones in use today are losing their touch due to various reasons. Chief among these include zero-day exploits, AI-driven phishing, and polymorphic malware. This study explores incorporating artificial intelligence (AI) in cyber security frameworks to counter such threats, thereby proposing to shift the focus from reactive to proactive and adaptive mechanisms. It employs machine learning (ML) algorithms, neural networks, and natural language processing (NLP) to show how AI can better threat detection, automate incident response, and predict vulnerabilities in real-time. A new AI-based framework marries supervised learning for anomaly detection, reinforcement learning for adaptive protocol optimization, and generative adversarial networks (GANS) to simulate and counter advanced persistent threats (APTs).A set of examples is provided that validates the real-life functionality of the proposed framework in NIDS and cloud security environments and reveals a 40% speed improvement in threat identification and a 35% decrease in false positives compared to rule-based systems. Simultaneously, the study also deals with other ethical and operational issues such as adversarial attacks on AI models, privacy of valid data, and the "black box" problem of ML in decision-making. Using explainable AI (XAI) techniques and federated learning for distributed data processing, the proposed framework contends with the balancing act between transparency and robust security.This study presents the potential of AI to craft self-healing, context-sensitive cyber security infrastructures and summons standard regulatory guidelines governing AI on critical systems. The findings performed aim to empower departments to adopt intelligent, scale able defenses as the cyber warfare continues escalating. Keywords: AI-Driven Cyber security, Proactive Threat Detection, Adaptive Security Frameworks, Explainable AI (XAI), Machine Learning in Intrusion Detection