Before You Give an AI Agent Access, Decide What It Can Do

An AI agent that summarizes a document has a limited job. An agent that can search company files, send emails, update records, or run code has a much bigger one.

For IT leaders, that difference should shape the deployment. Before connecting an agent to business systems, your team needs to agree on what it can access, which actions it can take, and when a person needs to approve its next step.

Those decisions can get overlooked when a pilot works well. A team sees the potential to save time, connects another data source, and adds another capability. Soon, the agent has more access than anyone originally intended.

In US Signal’s “Securing AI: Protecting Your Data and Workflows” webinar, our security and AI leaders joined Cato Networks to discuss how to manage that transition. A recurring point was that agents need clear boundaries around their work, just as employees do.

Start with the job the agent needs to do

“We’re essentially providing access as if it’s another human user,” said Ryan Ashcraft, AI security sales engineer at Cato Networks. “The difference here is it’s at machine speed.”

That speed is useful when an agent is doing the right work. It also leaves less time to catch a mistake before it affects other systems.

Start by defining a specific task. An agent helping a support team find answers may need to search approved documentation. That task does not automatically require access to customer billing information or permission to change account records.

Write down what a successful outcome looks like, which systems the agent needs, and which actions fall outside its role. Give the workflow an owner who can answer questions about its behavior and approve changes to its access.

This makes permission decisions easier to evaluate. Your team can assess each connection against a defined business need.

Check the data before connecting it

An agent’s usefulness depends on the information it can reach. So does much of its risk.

Before introducing AI to company data, identify where sensitive information lives and who should have access to it. Review the permissions on the underlying systems, including shared folders and collaboration platforms.

Existing access problems can become more apparent when AI makes information easier to find. A document that employees rarely encountered through ordinary search may become part of an agent’s response if its permissions allow access.

That is a reason to review the data source before connecting it. Ask whether the agent needs the entire repository or only a specific collection. Consider whether the information belongs in that workflow at all.

The review should also cover what the agent retains. Conversations, memory, and logs may contain information that remains available after the original task ends. Your team should understand how that information is stored and whether another user or session can access it.

Give each agent an identity and a defined set of permissions

When an agent takes an action, IT needs to know which agent did it and under whose authority.

Give each agent its own identity where the platform supports it. Define its permissions around its assigned work, and review those permissions when the workflow changes.

Separate the ability to read information from the ability to change it. An agent may need to retrieve a customer record to answer a question. Updating that record is a separate decision. Deleting it is another.

Treat access to additional tools and APIs with the same care. Each connection expands what the agent can reach or do. A successful pilot should prompt a review of the next proposed capability before your team enables it.

Decide where human approval belongs

Some actions need a person to review them before they happen.

Deleting data, changing system configurations, and other destructive actions deserve explicit approval requirements. Put those requirements into the workflow before deployment so the team knows when the agent must stop and ask.

Make the approval useful. The reviewer should be able to see the proposed action, the systems or records it will affect, and enough context to make an informed decision. A vague confirmation message gives the person little basis for deciding whether to proceed.

Also decide what happens when the agent cannot complete its task. It needs a stopping point and a way to hand the problem back to a person. Repeated attempts can create more work, unexpected changes, or unnecessary model costs.

Make the workflow visible

“It all starts with observability,” Ashcraft said.

Your team needs visibility into the steps between a user’s request and the final result. What information did the agent retrieve? Which tools did it call? What actions followed?

Logging should help the team reconstruct that sequence and determine whether the agent stayed within its assigned role. It should also help identify repeated failures or a process that keeps running without reaching an outcome.

This visibility matters after deployment, too. An agent’s connections and responsibilities may change over time. Reviewing its activity helps your team assess whether the original boundaries still fit the work it is doing.

Plan for mistakes before expanding access

Information an agent retrieves should not automatically become instructions it is authorized to follow. External content deserves particular scrutiny because your organization does not control what it contains.

Your team also needs a way to stop the agent, remove its access, and recover from unwanted changes. Include backups and recovery procedures in the deployment discussion, especially when the agent can modify business data.

Start with a narrow task and inspect how the agent performs it. Use what you learn to decide whether it is ready for more responsibility. Each expansion should come with another look at permissions, approval requirements, and monitoring.

At US Signal, our AI and security teams work together throughout the development of internal AI applications. That collaboration helps us evaluate the business need alongside the data and access decisions that support it.

If your organization is preparing to connect AI agents to business systems, we can help you assess the security questions that come with that access.

Talk with US Signal about securing your AI data and workflows.