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This paper proposes a meta-cognitive architecture for AI-driven cybersecurity systems to address limitations in accountable decision-making under adversarial uncertainty. The architecture coordinates heterogeneous AI agents responsible for detection, hypothesis formation, explanation, and governance through an explicit meta-cognitive judgement function. By embedding meta-cognitive judgement as a first-class system function, the framework aims to make the cognitive structure of security operations explicit and governable, shifting the focus from optimizing isolated predictions to governing autonomy under uncertainty.
Forget pipelines: cybersecurity needs agentic AI with a meta-cognitive "brain" to govern decisions and justify actions under uncertainty.
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.