This chapter explores the complex relationship between artificial intelligence (AI) and cybersecurity, examining how AI can enhance security measures while also introducing new vulnerabilities. As AI systems increasingly process vast amounts of sensitive data, ensuring their security and robustness becomes critical. We focus on state-of-the-art techniques that safeguard AI applications while maintaining the integrity, confidentiality, and availability (CIA) triad. The chapter begins by reviewing conventional security mechanisms, such as encryption, access controls, and intrusion detection systems. We also explore innovative approaches like differential privacy, which integrates mathematical obfuscation into machine learning models to enhance data protection without compromising analytical utility. However, AI’s mathematical foundations can also be exploited. Adversarial attacks manipulate input data to deceive models, compromising system security and trust. AI-powered attacks can optimize these exploits, targeting weaknesses in security defenses. Through adversarial machine learning, attackers can craft subtle perturbations that lead to incorrect model predictions. By analyzing both the benefits and risks of AI in cybersecurity, this chapter underscores the necessity of continuous research and development to mitigate emerging threats. Strengthening AI-driven defenses is essential to counteract adversarial tactics and ensure resilient cybersecurity frameworks.