The rapid adoption of artificial intelligence by threat actors has introduced a structural shift in the nature of cyberattacks. Modern attacks increasingly rely on valid credentials, authorized APIs, and legitimate cloud services, composing technically valid actions into high-impact attack chains executed at machine speed. In this context, traditional Security Operations Centers (SOCs), designed around static rules, isolated alerts, and human-paced investigation, are no longer sufficient.This work introduces the concept of Cognitive Security Operations, a new operational model that reframes security from event-centric detection to coherence-centric cognition. Central to this approach is the notion of Δ-coherence, defined as the divergence between observed system behavior and its expected operational semantics, even when no explicit policy violations occur.The paper presents an adaptive SOAR architecture, operationalized through Splunk Phantom, that enables progressive, proportional, and fully reversible containment actions under human governance. Rather than relying on binary allow/block decisions, the proposed model applies staged containment—light, functional, and identity-level—based on sustained behavioral misalignment.To ground the model in real-world conditions, the paper analyzes LLMjacking scenarios across AWS, Azure, and Google Cloud Platform, demonstrating how AI-amplified cloud abuse exploits cognitive blind spots rather than technical vulnerabilities. These case studies illustrate why traditional SOC semantics fail and how Cognitive SOCs can disrupt attacks pre-impact, before financial or operational damage escalates.By positioning human analysts as strategic supervisors rather than operational bottlenecks, Cognitive Security Operations enable continuous alignment between systems, identities, and intent. In the era of AI symmetry—where both attackers and defenders operate with automation and intelligence—security effectiveness depends not on faster humans, but on coherent human–AI systems.