The integration of Artificial Intelligence (AI) into cybersecurity has transformed the way digital threats are detected, analyzed, and mitigated. AI-driven security systems offer rapid threat detection, realtime response mechanisms, and predictive analytics to counteract evolving cyber threats. However, as AI enhances cybersecurity, it also introduces new vulnerabilities that cybercriminals exploit through AI-powered attacks, such as deepfakes, adversarial attacks, and automated phishing campaigns. This paper explores the dual role of AI in cybersecurity-its potential to strengthen digital defense mechanisms while also being weaponized by adversaries.Key areas of focus include the implementation of machine learning-based intrusion detection systems (IDS), AI-powered threat intelligence, data privacy concerns, and the security of cyber-physical systems (CPS) and smart cities. The study also highlights the importance of explainable AI (XAI) for transparency in cybersecurity decisions, privacy-preserving AI techniques, and hybrid security models combining traditional and modern AI methods. As AI continues to evolve, it is crucial to develop ethical AI frameworks that enhance security while minimizing risks. Future research must emphasize adaptive, scalable, and resilient AI-driven cybersecurity solutions to ensure a secure digital landscape in an era where AI plays a central role in both cyber defense and cybercrime.
Cybersecurity, AI, Machine Learning, Cybercrime, Big Data, Intrusion Detection, Adversarial Attacks, Threat Intelligence, Data Privacy, Network Security.