Beyond Detection: Generative Artificial Intelligence for Autonomous Cyber Threat Hunting and Incident Response

Abstract

Autonomous cybersecurity requires a transition from isolated alert classification toward coordinated threat investigation, evidence-grounded decisions, and measurable containment. This study integrates a generative artificial intelligence (GenAI) multi-agent threat-hunting architecture with a six-state nonlinear model of secure, exposed, compromised, and recovered assets and the evolving hunting and response capabilities. The framework employs independent evidence verification and policy-controlled response authorization, while model analysis uses a containment threshold, equilibrium stability and parameter sensitivity. Across the three configurations reported in the supplied results, compromise fractions were 42.71% for rule-based defence, 32.48% for machine-learning-supported defence, and 19.16% for GenAI-enabled defence; corresponding containment thresholds were 6.860, 4.857, and 3.275. These submitted outcomes indicate a 55.1% lower modelled compromise fraction for the GenAI configuration than for rule-based defence, although all thresholds remain above unity and therefore do not establish threat elimination. Three author-supplied three-dimensional response surfaces describe the effects of propagation, assisted recovery, adversarial adaptation and hunting activation. The investigation distinguishes these reported model-based outcomes from unreported alert-level accuracy benchmarks and from numerical bifurcations that require recorded AUTO-07p continuation diagnostics. The resulting contribution is a coherent framework linking generative investigation, bounded response capacity and nonlinear cybersecurity resilience, with explicit requirements for reproducible experimental validation and safe operational deployment.