Agentic AI: Understanding and Securing the Next Frontier of Intelligent Systems

Agentic AI workflows—autonomous pipelines in which large language models (LLMs) plan, decompose, and execute sequences of tasks—are rapidly being adopted in cybersecurity for threat hunting, incident response, and vulnerability management.

However, ungoverned autonomy introduces significant risks: unauthorized actions, data exfiltration, misconfiguration, and stealthy evasion.

In this paper, we (1) survey the architecture of agentic workflows, (2) detail the unique security challenges they pose, (3) present the Context–Protocol–Standard (MCP) Model as a governance framework, and (4) evaluate its effectiveness in a prototype Security Operations Center (SOC) scenario.

We find that MCP reduces unauthorized actions by 85 %, cuts mean detection time by 70 %, and imposes only a modest 12 ms per-call latency. We conclude with best practices and a roadmap for securely integrating agentic workflows into modern cybersecurity operations.