A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes
It addresses the critical need for updated security frameworks and governance mechanisms to manage the risks and opportunities of autonomous AI systems in cybersecurity, though it is incremental as a survey.
This survey explores the dual-use implications of agentic AI in cybersecurity, highlighting its potential for both enhancing defensive capabilities like autonomous incident response and amplifying adversarial threats such as accelerated exploitation, while identifying governance gaps and systemic risks.
Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over long-lasting tasks. By integrating memory, tool use, and iterative decision cycles, these systems enable continuous, autonomous workflows in real-world environments. This survey examines the implications of agentic AI for cybersecurity. On the defensive side, agentic capabilities enable continuous monitoring, autonomous incident response, adaptive threat hunting, and fraud detection at scale. Conversely, the same properties amplify adversarial power by accelerating reconnaissance, exploitation, coordination, and social-engineering attacks. These dual-use dynamics expose fundamental gaps in existing governance, assurance, and accountability mechanisms, which were largely designed for non-autonomous and short-lived AI systems. To address these challenges, we survey emerging threat models, security frameworks, and evaluation pipelines tailored to agentic systems, and analyze systemic risks including agent collusion, cascading failures, oversight evasion, and memory poisoning. Finally, we present three representative use-case implementations that illustrate how agentic AI behaves in practical cybersecurity workflows, and how design choices shape reliability, safety, and operational effectiveness.