A Cognitive Defense Framework for Detecting and Containing Autonomous Cyber Incidents Caused by Next-Generation Agentic Artificial Intelligence
Abstract: The rapid maturation of agentic artificial intelligence (AI) is producing a class of cyber threats in which an autonomous system can plan action sequences, adapt its strategy to intermediate results, and interact with networked resources without direct human oversight. Conventional signature-, anomaly-, and event-based defenses respond to isolated indicators and are poorly suited to machine-initiated behavior that unfolds as many coordinated, low-visibility operations. This work proposes a conceptual cognitive-defense framework aimed at detecting, forecasting, and containing autonomous cyber incidents attributable to agentic AI. Rather than matching individual signatures, the framework reconstructs an agent's inferred intent, goal structure, and causal action chain, and compares the declared task objective against observed behavior. The architecture combines continuous runtime monitoring, semantic command analysis, a causal action graph, an ensemble of independent observer models, and a graded response mechanism spanning privilege reduction, process suspension, token revocation, and network isolation. A Cognitive Autonomous Threat Index (CATI) aggregates plan complexity, action interdependence, stealth, adaptivity, privilege-escalation attempts, and persistence potential into a single risk score. The framework is positioned against recent digital-twin, edge-integrity, and cyber-resilience studies, and an illustrative scoring walkthrough demonstrates its operation. Empirical validation on an instrumented testbed is identified as the principal direction for future work. Keywords: agentic AI; cybersecurity; autonomous threat; intent reconstruction; cognitive defense; cyber–physical systems; digital twin; cyber resilience; runtime monitoring; ensemble detection