Assessing the Effectiveness of Artificial Intelligence Cybersecurity Controls: Practitioner Perspectives on Risk Mitigation in AI Systems

The rapid integration of artificial intelligence (AI) into organizational operations has intensified the need for effective cybersecurity controls. However, the extent to which existing security measures adequately protect AI systems against both conventional and AI-specific threats remains poorly understood from a practitioner's perspective. This qualitative study explores how AI professionals perceive the effectiveness of current cybersecurity strategies in mitigating security and privacy risks in AI systems. Semi-structured interviews were conducted with 12 AI and cybersecurity professionals across financial services, healthcare, cloud computing, and technology sectors. Thematic analysis of the data identified four principal dimensions of effectiveness perception: (1) foundational controls are viewed as effective for conventional threats but insufficient for AI-specific risks; (2) AI-specific defenses are recognized as necessary but remain immature in most organizational settings; (3) monitoring capabilities exhibit a persistent gap between infrastructure-level and model behavioral detection; and (4) organizational and governance factors significantly moderate technical control effectiveness. Participants consistently described a maturity gap between the pace of AI deployment and the sophistication of protective measures. These findings have important implications for practitioners, policymakers, and researchers working to strengthen AI security frameworks and governance structures.

AI cybersecurity effectiveness, risk mitigation, AI security controls, practitioner perceptions, qualitative research, machine learning security, AI governance, data protection, adversarial threats, information security