The rapid expansion of digital infrastructures, cloud computing, Internet of Things (IoT), and interconnected enterprise systems has significantly increased the attack surface for cyber threats. Traditional security mechanisms are no longer sufficient to handle the sophistication, speed, and scale of modern cyberattacks. AI-driven Intrusion Detection and Prevention Systems (IDPS) have emerged as a next-generation cybersecurity solution capable of identifying, analyzing, and mitigating threats in real time.This study explores the role of artificial intelligence and machine learning in enhancing intrusion detection and prevention capabilities. AI-based systems leverage anomaly detection, behavioral analysis, deep learning models, and predictive analytics to identify both known and unknown attack patterns. Unlike rule-based systems, AI-driven IDPS continuously learn from network traffic data, enabling adaptive and proactive threat mitigation.The research investigates how organizations deploy AI-driven security frameworks to protect critical infrastructure, enterprise networks, and cloud environments. It also examines challenges such as adversarial AI attacks, data privacy concerns, false positives, and computational overhead. A mixed-method approach is proposed to evaluate system effectiveness across different cybersecurity environments.Findings suggest that AI-driven IDPS significantly improves detection accuracy, reduces response time, and enhances overall cyber resilience. The study concludes that integrating artificial intelligence into cybersecurity frameworks is essential for protecting next-generation digital ecosystems from evolving cyber threats
