Cybersecurity Further Threats in Artificial Intelligence: A Comprehensive Analysis

The rapid integration of artificial intelligence (AI) systems into critical infrastructure, healthcare, finance, and national security sectors has introduced unprecedented cybersecurity challenges. This paper examines emerging threats that exploit AI systems and leverage their capabilities for malicious purposes. Adversarial machine learning attacks, AI-powered cyber threats, model poisoning, privacy vulnerabilities, and the weaponisation of generative AI are analysed. The current threat landscape is synthesised, attack vectors specific to AI architectures are examined, and defensive strategies are evaluated. Critical gaps in existing security frameworks are identified, and a multi-layered defence approach is proposed, incorporating adversarial robustness, secure AI development practices, and adaptive threat detection. The findings demonstrate that traditional cybersecurity paradigms are insufficient for AI systems, necessitating novel security architectures that address the unique attack surfaces of machine learning models.