Managing Adversarial AI Risks Through Governance, Threat Hunting and Continuous Monitoring in Production Systems

The deployment of artificial intelligence in production enterprise systems has introduced a new class of adversarial risks that extend beyond traditional cybersecurity threats. At a broad level, AI-enabled systems increasingly operate in dynamic and exposed environments, making them attractive targets for malicious actors seeking to manipulate data, exploit model behavior, or abuse system capabilities. Adversarial techniques such as data poisoning, model extraction, prompt exploitation, and evasion attacks pose significant risks to system integrity, reliability, and trust, particularly when AI systems are integrated into critical business and decision-making processes. Conventional security controls, when applied in isolation, are often insufficient to address these evolving threats. This paper narrows its focus to managing adversarial AI risks through the coordinated application of governance structures, threat hunting practices, and continuous monitoring in production systems. It positions AI governance as the organizing layer that defines accountability, risk tolerance, and escalation pathways, while threat hunting provides proactive identification of adversarial behaviors targeting AI models and data pipelines. Continuous monitoring mechanisms are examined as essential tools for detecting anomalous patterns, model drift, and exploitation attempts in real time. By integrating these elements across the AI lifecycle, organizations can transition from reactive incident response to anticipatory and resilient risk management. The paper argues that a governance-led, intelligence-driven approach is essential for sustaining secure, reliable, and trustworthy AI operations in adversarial production environments.