AI-Driven Cybersecurity Resilience: Enhancing Threat Detection and Prevention Mechanisms

Abstract

As cyber threats evolve in complexity, artificial intelligence (AI) is redefining cybersecurity by enabling real-time threat detection, automated incident response, and adaptive security mechanisms. This research explores AI-driven intrusion detection systems (IDS), security orchestration automation (SOAR), and deep learning-based malware detection to enhance resilience against cyberattacks. Case studies highlight AI’s role in fraud prevention at PayPal, threat intelligence at IBM, and malware defense at Tesla, demonstrating significant reductions in security breaches and response times. The paper also discusses ethical AI considerations, explainability in threat detection, and the ongoing battle against AI-generated cyber threats. By integrating AI into cybersecurity frameworks, organizations can fortify their defenses against adversarial attacks and ensure the protection of critical digital infrastructures.