New Study Proposes Agentic AI Framework for Cyber Resilience

Global: New Study Proposes Agentic AI Framework for Cyber Resilience

A preprint posted to arXiv in December 2025 outlines a research effort that argues for a fundamental shift in cybersecurity strategy. The authors contend that large‑language‑model‑driven artificial intelligence now enables autonomous planning, tool orchestration, and strategic adaptation at scale, challenging traditional static defenses. They propose moving from a prevention‑centric model toward an agentic, resilience‑focused approach that anticipates disruption, sustains critical functions, and continuously learns from attacks.

Evolution of Cybersecurity Paradigms

The paper situates its proposal within the historical progression of security thinking, tracing a trajectory from perimeter‑based controls to behavior‑based analytics and, most recently, to AI‑augmented defenses. Each stage reflects an expanding threat surface and a corresponding need for more adaptive protective measures.

From Prevention to Resilience

According to the authors, resilience does not seek perfect protection but rather aims to maintain essential operations during an intrusion and to recover swiftly afterward. This perspective emphasizes anticipation of adversarial actions, real‑time adaptation, and post‑event learning as core capabilities.

Designing Agentic Workflows

The study introduces a system‑level framework for constructing agentic AI workflows. It describes a general architecture in which autonomous agents participate directly in sensing, reasoning, action, and adaptation across both cyber and cyber‑physical environments. The framework outlines how these agents can be orchestrated to support continuous monitoring and rapid response.

Game‑Theoretic Foundations

Attacker and defender processes are modeled as coupled adaptive systems, with game‑theoretic formulations providing a unifying language for allocating autonomy, managing information flow, and sequencing actions over time. The authors argue that equilibrium‑based design can clarify trade‑offs between offensive and defensive capabilities.

Illustrative Case Studies

Three illustrative scenarios are examined: automated penetration testing, AI‑driven remediation, and cyber deception. In each case, equilibrium‑oriented designs demonstrate how autonomous agents can enhance system‑level resilience while preserving operational objectives.

Implications for Future Security Design

The authors conclude that embracing agentic AI may reshape security architectures, encouraging the integration of autonomous components that can both detect and respond to threats without relying solely on human intervention. They suggest that further research should explore standards for safe deployment and mechanisms for continuous verification of agent behavior.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.