Best AI Security markBest AI Security

Guide · Checked against vendor sites, September 2026

What is AI agent security?

The verdict

AI agent security is the set of controls that let an organization see which AI agents are running, decide what each one may do, stop actions that break policy, and keep a record of who did what. It matters because agents act with a person's access: they run commands, read files, call APIs and use credentials.

Part of the University: Basics track

Why is AI agent security different from securing a chatbot?

A chatbot answers. An agent acts. Coding agents such as Claude Code, Codex and Cursor can run shell commands, install packages, edit files and call cloud CLIs. Through MCP servers they reach calendars, databases, ticketing systems and internal APIs. Every one of those actions runs with the permissions of the person who started the agent, often on their own laptop. The risk moves from what the model says to what the agent does.

What should an AI agent security program cover?

Action lane: the four checkpoints an AI agent action passes, and which vendors document eachA horizontal lane runs from an agent action on the left to the endpoint on the right, through four checkpoints: See, Decide, Allow or block, and Log. The third checkpoint is split between Allow and Block.Agent actioncoding agent calls anMCP tool: run shellcommandEndpoint (file system,credentials, network)SeeDiscover the agent, its MCPservers, tools and credentialsDecideEvaluate the action before itruns, with contextAllow or blockAllow, ask the user, or denyAllowBlockLogRecord who triggered it andwhat happenedAction lane: the four checkpoints an AI agent action passes, and which vendors document eachA horizontal lane runs from an agent action on the left to the endpoint on the right, through four checkpoints: See, Decide, Allow or block, and Log. The third checkpoint is split between Allow and Block.Agent actioncoding agent calls an MCPtool: run shell commandSeeDiscover the agent, its MCPservers, tools and credentialsDecideEvaluate the action before itruns, with contextAllow or blockAllow, ask the user, or denyAllow / BlockLogRecord who triggered it and whathappened

Decide

Evaluate the action before it runs, with context

Log

Record who triggered it and what happened

Figure 1. Based on each vendor's public product pages, reviewed September 2026. A vendor missing from a step has not documented it publicly; that does not prove the capability is absent.
  1. See. An inventory of agents, coding assistants, MCP servers, extensions and the credentials they can reach, on every device.
  2. Decide. An evaluation of each action before it runs, using context: who started the agent, what it did earlier in the session, what data it touched.
  3. Allow or block. A response that fits the risk: allow, ask the user, warn, mask data, or deny.
  4. Log. A record that separates what a person did from what an agent did on their behalf, from prompt to system action.

Where do agents run, and where should controls sit?

Agents run in three places: on employee devices (coding agents, desktop assistants, browser AI), inside SaaS platforms (Microsoft 365 Copilot, Salesforce Agentforce, ServiceNow) and in applications a company builds. Endpoint tools such as Bay, Koi and Bloom Security focus on the first. Platforms such as Noma Security, Zenity and Onyx Security cover all three to different depths. Data-first tools such as Harmonic Security and Prompt Security start from employee AI use and extend to agents.

Is AI agent security only a security purchase?

Increasingly not. The same controls decide how fast a company can say yes to AI tools. A CIO who can see every agent and set per-action rules can approve coding agents for a whole engineering team instead of a pilot group. That is why several vendors in this guide sell to both security leaders and IT leaders.

What are the related terms?

  • MCP security: controls on the servers that connect agents to tools and data (MCP security).
  • Shadow AI: AI tools in use without approval (Shadow AI).
  • AI-SPM: AI security posture management, finding misconfigurations and excessive permissions.
  • AIDR: AI detection and response at runtime.

Frequently asked questions

What is the difference between AI security and AI agent security?

AI security covers models, data and AI applications broadly. AI agent security focuses on agents that take actions, and on controlling those actions.

Does EDR cover AI agents?

EDR watches processes and files. It sees a trusted application running, not whether a specific agent tool call should be allowed. See AI agent security and EDR (/guide/ai-agent-security-and-edr).

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