An AI agent security assessment reviews what an agent can access, execute, or leak before it runs against systems that matter. It is not only a chatbot review. It follows the agent through tools, APIs, MCP servers, retrieval systems, permissions, logs, and data paths.
Why this exists
Classic application testing asks whether a user can break the app boundary. Agent security asks a wider question: what can a model-driven workflow do once it has tools, credentials, context, and permission to act?
That difference matters because agents collapse several old control boundaries. A support agent may read tickets, call an internal API, summarize customer data, and write a workflow update. A coding agent may read repositories, run commands, change configuration, and open pull requests. A research agent may retrieve privileged documents and produce outputs that are copied into customer evidence.
Each step can be reasonable in isolation. The risk appears when the chain has more authority than the business intended.
The Access, Execute, Leak model
Aeon CyberGuard uses a simple model for agent security:
- Access: what data, tenants, documents, APIs, roles, and objects can the agent reach?
- Execute: what actions, tool calls, commands, workflow steps, or permission changes can the agent trigger?
- Leak: where can sensitive context, secrets, logs, retrieved data, or customer evidence escape?
This is the practical security model for agents because it mirrors how failures happen in production.
What the assessment covers
An AI agent security assessment can cover tool permissions, API authorization, MCP server boundaries, prompt-to-action paths, RAG access control, tenant isolation, logging, secrets handling, error messages, and data egress.
The output should be useful to engineers and executives: verified findings, reproduction-safe evidence, developer-ready remediation, retest-ready closure notes, and evidence that can support customer diligence or SOC 2 readiness.
How to start safely
The safest first step is public and passive. CyberGuard starts with a public-exposure snapshot and a free threat-model call. Active testing only starts after written authorization, scope, test window, data handling, and stop conditions are agreed.
If your agents are moving from pilot to production, the right question is direct: do you know what they can access, execute, or leak?