Mitigating AI-Driven Cyber Threats: A Defense Framework for Securing Autonomous Systems from Intelligent Adversaries
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Abstract
The integration of artificial intelligence (AI) into autonomous systems has expanded operational capabilities while simultaneously exposing them to sophisticated AI-motivated adversaries capable of adaptive and evasive cyberattacks. Conventional signature-based and static defense mechanisms are inadequate against such dynamic threats, creating critical vulnerabilities across mission-critical infrastructures. This research proposes a cognitive, multi-layered defense-in-depth framework designed to mitigate AI-driven attacks through adaptive learning, trust calibration, and policy enforcement while maintaining compliance with the EU AI Act and NIST AI Risk Management Framework. The architecture integrates four sequential layers: perception for anomaly detection using Isolation Forests and adversarial filters; cognitive trust scoring via Bayesian and fuzzy logic models; intent inference employing Bayesian networks and Markov Decision Processes (MDPs) for reconstructing adversarial goals; and adaptive policy enforcement through real-time privilege revocation, sandboxing, and Rego-based policy engines. The framework was validated in simulated environments using OpenAIGym and CyberBattleSim against five representative AI-enabled scenarios, including adversarial ML perturbations, GAN-based deepfakes, and reconnaissance agents in smart grids, AI-as-a-Service phishing campaigns, and diagnostic manipulation in healthcare. Experimental results demonstrate 92.3% detection accuracy, a 47% reduction in false positives over static models, and policy enforcement latency averaging 210 ms, ensuring real-time adaptability. The findings underscore the framework’s ability to embed cognitive reasoning, behavioral analytics, and adaptive controls into a modular and scalable architecture, offering a resilient and auditable cybersecurity paradigm for protecting autonomous and critical systems against evolving AI-motivated threats.
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