Securing Artificial Intelligence Systems: Emerging Threats, Vulnerabilities, and Defense Strategies

Integration of Artificial Intelligence (AI) into critical infrastructures introduces unique vulnerabilities exploitable through sophisticated attacks. This paper examines a broad spectrum of AI-specific threats, including adversarial machine learning (evasion and poisoning), AI-amplified social engineering (deepfakes, phishing), and AI-enhanced malware. It analyzes inherent vulnerabilities in AI frameworks, covering datarelated issues (bias, privacy) and model-related challenges (overfitting). Infrastructure weaknesses in complex deployments are also assessed. The paper explores defensive strategies such as advanced encryption, adaptive self-learning systems, and robust authentication. Sector-specific applications in energy and finance illustrate practical considerations. Ethical and legal dimensions, including privacy regulations, are discussed to ensure compliance and trust. Finally, emerging research directions like AIblockchain integration and generative AI for initiative-taking defense are proposed. This synthesis aims to inform efforts to secure AI-enabled systems against evolving threats while balancing operational effectiveness with ethical obligations.