Artificial intelligence (AI) has redefined both the scope and the speed of modern cybersecurity operations, bringing new efficiencies—and new risks—to the enterprise landscape. As AI systems become integral to detection, decision-making, and automation, auditors must learn to assess not just algorithms but the operational frameworks that govern them. Monitoring, containment, and forensic readiness are no longer peripheral capabilities; they are essential components of trustworthy AI assurance. Understanding how to evaluate these mechanisms equips auditors to verify that AI-driven environments remain safe, explainable, and accountable under real-world conditions.
From continuous monitoring for model drift and abuse detection to structured triage, evidence handling, and escalation across enterprise boundaries, this chapter examines how organizations maintain control when AI systems fail or behave unpredictably. It emphasizes the need for auditable telemetry, well-defined containment mechanisms, and disciplined coordination between AI assurance and traditional incident response teams. The discussion bridges technical controls—such as kill switches, feature flags, and data provenance tracking—with governance expectations that ensure every response action can be traced, verified, and improved upon.
Ultimately, this chapter reinforces that ...
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