Why AI agents need their own audit
AI agents read company data, open files, call APIs, use credentials, modify systems and send messages, often without a human watching each step. They behave like users, but they move faster, run continuously and can touch several systems in one workflow.
Traditional identity and access reviews were designed around people, and application logging was designed around software. Agents sit between the two, so they are easy to miss in both reviews.
Step 1: Build an agent inventory
You cannot audit what you have not found. List every AI agent, assistant and connected application that holds access to your systems, including tools an individual employee authorized on their own.
- Name and vendor of each agent or connected app
- The human or team that owns it
- Who authorized it and when
- Which systems it is connected to
Step 2: Map access and permissions
For each agent, record what it can read, modify, send, delete or administer. Pay special attention to broad scopes such as full mailbox, full drive or admin access, and to combinations of capabilities, for example the ability to read sensitive data and also send external messages.
Step 3: Review activity
Permissions show what an agent could do. Activity shows what it actually did. Build a history of agent access and actions so you can see what data it touched, whether its behavior has changed, and whether its use matches the reason it was approved.
Step 4: Flag and prioritize risk
- Excessive permissions that exceed the agent's stated purpose
- Access to sensitive data such as finance, HR or legal files
- Unusual or changed behavior over time
- Dangerous combinations of capabilities
- Agents with no clear owner
Step 5: Produce evidence
An audit is only useful if someone else can review it. Record findings in a form that security, compliance and audit teams can examine later, including after an incident, rather than in a one-off spreadsheet.
How AgentGuard supports an AI agent audit
AgentGuard by ScreenComply gives you a record of what your AI agents can access, what they are doing and where the risk is. Google Workspace is available now, with GitHub, Microsoft 365 and Slack coming next.
Frequently asked questions
What is an AI agent audit?
A structured review of every AI agent in an environment: its identity, owner, permissions, activity and risk, recorded as evidence that others can review.
How often should AI agents be audited?
Continuously where possible. Agents gain new permissions and change behavior over time, so a one-time review goes stale quickly.
Is an AI agent audit different from an access review?
Yes. An access review covers what an identity is allowed to do. An agent audit also covers what the agent actually did, who owns it and whether its behavior has changed.
Which systems does AgentGuard audit today?
Google Workspace is available now. GitHub, Microsoft 365 and Slack are coming next.