Mitigating Rogue AI Behavior: Techniques for Detection, Control, and Containment

artificial intelligence (AI) systems grow more autonomous and embedded in critical decision-making, the potential for “rogue behavior”—defined as significant divergence from human-aligned goals—has become a central concern in AI safety. This paper surveys and integrates contemporary approaches to detecting, mitigating, and containing rogue AI. We introduce a unified taxonomy of techniques, including off-policy evaluation using trusted models, activation steering via trained neural probes, ensemble-based behavioral stabilization, and preemptive sandbagging to delay capability overreach. We also outline novel frameworks like SCAB (Synthetic Consciousness Assessment Battery) and AgenticOps that enable real-time governance and post-deployment observability. The paper provides practical and theoretical contributions to the operationalization of AI safety in high-stakes, real-world environments.