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[2602.11897] Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy
Summary
This paper presents a novel meta-cognitive architecture for AI in cybersecurity, advocating for a shift from traditional model-centric systems to agentic, multi-agent frameworks that enhance decision-making and governance under uncertainty.
Why It Matters
As cybersecurity threats evolve, traditional AI systems often fail to provide accountable decision-making. This research proposes a new framework that emphasizes governable autonomy, which is crucial for organizations aiming to enhance their security operations and comply with regulatory standards.
Key Takeaways
- Current AI systems in cybersecurity are limited by model-centric approaches.
- The proposed architecture incorporates meta-cognitive functions for better decision-making.
- A shift to multi-agent systems can improve governance and accountability in cybersecurity.
- The framework aims to enhance AI's role in managing uncertainty and risk.
- Implications extend to security operations centers and next-generation cyber defense designs.
Computer Science > Cryptography and Security arXiv:2602.11897 (cs) [Submitted on 12 Feb 2026 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy Authors:Andrei Kojukhov, Arkady Bovshover View a PDF of the paper titled Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy, by Andrei Kojukhov and Arkady Bovshover View PDF Abstract:Contemporary AI-driven cybersecurity systems are predominantly architected as model-centric detection and automation pipelines optimized for task-level performance metrics such as accuracy and response latency. While effective for bounded classification tasks, these architectures struggle to support accountable decision-making under adversarial uncertainty, where actions must be justified, governed, and aligned with organizational and regulatory constraints. This paper argues that cybersecurity orchestration should be reconceptualized as an agentic, multi-agent cognitive system, rather than a linear sequence of detection and response components. We introduce a conceptual architectural framework in which heterogeneous AI agents responsible for detection, hypothesis formation, contextual interpretation, explanation, and governance are coordinated through an explicit meta-cognitive judgement function. This function governs decision readiness and dynamically calibrates system autonomy when evidence is incomplete, conflicting, or operat...
