Artificial Intelligence (AI) Agents Companion Guide

Artificial intelligence (AI) agents represent a rapidly emerging class of systems that blend Large Language Models (LLMs) with orchestration logic, tool execution, data retrieval, and automated decision-making. Unlike stand-alone models, AI agents operate across multiple layers of an enterprise’s environment, interacting with internal services, external APIs, sensitive data, and user workflows. This expanded operational footprint introduces new risks, including unauthorized actions, data leakage, and unintended system changes, that require security considerations beyond traditional model-centric safeguards.

CIS Critical Security Controls^® (CIS Controls^®) v8.1 offers foundational, prioritized cybersecurity best practices designed to help enterprises mitigate the most prevalent threats. However, applying these CIS Controls directly to AI agents requires interpreting them through the lens of autonomous and semi-autonomous system behavior. Agent architectures often extend across identity layers, endpoint execution environments, knowledge stores, integration pipelines, and operational monitoring systems, meaning the CIS Controls must be mapped to a broader attack surface than conventional software.

This guide provides practical, actionable guidance for applying CIS Controls v8.1 to the agent layer specifically, the layer where planning, reasoning, tool invocation, and multi-step workflows occur. By aligning established security practices with the unique operational characteristics of AI agents, enterprises can strengthen their security posture while maintaining the flexibility and innovation that AI systems enable. Each section interprets relevant CIS Safeguards in the context of agent behavior, providing clarity on where traditional controls still apply and where new patterns must be considered.

As enterprises adopt AI agents to streamline processes, enhance decision-making, and automate complex tasks, ensuring secure design and operation becomes essential. This guide aims to bridge the gap between standard cybersecurity frameworks and emerging AI agent architectures, helping teams implement controls that reduce risk while supporting responsible, reliable, and resilient use of AI technologies.