Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy

• URL: http://arxiv.org/abs/2602.11897v2
• Date: Mon, 16 Feb 2026 19:03:52 GMT
• Title: Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy
• Authors: Andrei Kojukhov, Arkady Bovshover,
• Abstract summary: This paper argues that cybersecurity orchestration should be reconceptualized as an agentic, multi-agent cognitive system.<n>We introduce a conceptual 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.<n>Our contribution is to make this cognitive structure architecturally explicit and governable by embedding meta-cognitive judgement as a first-class system function.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 operationally risky. By synthesizing distributed cognition theory, multi-agent systems research, and responsible AI governance frameworks, we demonstrate that modern security operations already function as distributed cognitive systems, albeit without an explicit organizing principle. Our contribution is to make this cognitive structure architecturally explicit and governable by embedding meta-cognitive judgement as a first-class system function. We discuss implications for security operations centers, accountable autonomy, and the design of next-generation AI-enabled cyber defence architectures. The proposed framework shifts the focus of AI in cybersecurity from optimizing isolated predictions to governing autonomy under uncertainty.

Related papers

• Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability [0.6745502291821954]
  Agentic systems have transformed how Large Language Models can be leveraged to create autonomous systems with goal-directed behaviors.<n>Current interpretability techniques, developed primarily for static models, show limitations when applied to agentic systems.<n>This paper assesses the suitability and limitations of existing interpretability methods in the context of agentic systems.
  arXiv  Detail & Related papers  (2026-01-23T21:05:32Z)
• The Path Ahead for Agentic AI: Challenges and Opportunities [4.52683540940001]
  This chapter examines the emergence of agentic AI systems that operate autonomously in complex environments.<n>We trace the architectural progression from statistical models to transformer-based systems, identifying capabilities that enable agentic behavior.<n>Unlike existing surveys, we focus on the architectural transition from language understanding to autonomous action, emphasizing the technical gaps that must be resolved before deployment.
  arXiv  Detail & Related papers  (2026-01-06T06:31:42Z)
• Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations [20.297163645916157]
  Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence.<n>This chapter argues for a shift from prevention-centric security toward agentic cyber resilience.
  arXiv  Detail & Related papers  (2025-12-28T11:17:36Z)
• The Social Responsibility Stack: A Control-Theoretic Architecture for Governing Socio-Technical AI [0.0]
  This paper introduces the Social Responsibility Stack (SRS), a framework that embeds societal values into AI systems.<n>We show how SRS translates normative objectives into actionable engineering and operational controls.<n>The framework bridges ethics, control theory, and AI governance, providing a practical foundation for accountable, adaptive, and auditable socio-technical AI systems.
  arXiv  Detail & Related papers  (2025-12-18T18:42:16Z)
• AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
  This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
  arXiv  Detail & Related papers  (2025-11-27T16:56:04Z)
• Fundamentals of Building Autonomous LLM Agents [64.39018305018904]
  This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs)<n>The research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities.
  arXiv  Detail & Related papers  (2025-10-10T10:32:39Z)
• A cybersecurity AI agent selection and decision support framework [0.0]
  This paper presents a novel, structured decision support framework that aligns AI agent architectures, reactive, cognitive, hybrid, and learning.<n>By integrating agent theory with industry guidelines, this framework provides a transparent and stepwise methodology for selecting and deploying AI solutions.
  arXiv  Detail & Related papers  (2025-10-02T07:38:21Z)
• Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
  We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
  arXiv  Detail & Related papers  (2025-08-12T09:42:56Z)
• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
  Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
  arXiv  Detail & Related papers  (2025-08-10T16:07:32Z)
• Toward a Theory of Agents as Tool-Use Decision-Makers [89.26889709510242]
  We argue that true autonomy requires agents to be grounded in a coherent epistemic framework that governs what they know, what they need to know, and how to acquire that knowledge efficiently.<n>We propose a unified theory that treats internal reasoning and external actions as equivalent epistemic tools, enabling agents to systematically coordinate introspection and interaction.<n>This perspective shifts the design of agents from mere action executors to knowledge-driven intelligence systems, offering a principled path toward building foundation agents capable of adaptive, efficient, and goal-directed behavior.
  arXiv  Detail & Related papers  (2025-06-01T07:52:16Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.