Cyber defence strategies against adversarial attacks in AI systems

Artificial Intelligence advances and alters industries and creates unique cybersecurity vulnerabilities pertaining to its integration into critical systems. The paper thoroughly discusses adversarial attacks directed against an AI system: adversarial perturbations, data poisoning, model inversion, and transfer learning attacks, focusing on their implications on AI security. Unlike the existing papers, the contribution critically looks at the modern defenses—adversarial training, robust optimization, anomaly detection, and differential privacy—while also discussing their shortcomings with respect to adaptability, scalability, and threat coverage. A comparative study in this paper exposes key gaps in currently available defenses; therefore, the unique framework suggested consists of dynamic threat modeling and collaborative cybersecurity approaches. The findings justify the need for such security frameworks that are both standardized and adaptable to ensure that AI systems can be deployed resiliently and responsibly. Over and above these challenges, this work solidifies AI's defense mechanism against adversary threats evolving and its counteract in a cyberspace concern.