5 steps to protect internal AI assistants: warning and practical solution

Limiting permissions, separating action reading, and requiring human approval reduces the risk that a compromised AI assistant causes damage. Recently, a warning from an expert has led many companies to review what they allow their automated agents to do.

When an organization grants access to email, documents, or internal tools, the risk is no longer theoretical: an agent with privileges can send messages or modify files. Reduced permissions and immutable logs are measures proposed by the source as a basic response.

Article based on coverage by La Razón which collects warnings from Chema Alonso, and recommendations from the UK National Cyber Security Centre and OWASP. Below I reveal the concrete solution and explain why it works in simple terms: it is necessary to separate data from commands, limit autonomy, and guarantee audits to be able to contain an incident.

What to do and why it matters

Why is this relevant for your company?

If an assistant reads emails and also has permission to send them, an error or malicious instruction can be transmitted outside the chat and affect third parties. This impacts reputation and privacy and can trigger data leaks or unauthorized orders. Action is needed to prevent this.

What do the source’s recommendations mean?

The source recommends investing in protection, limiting credentials, assigning identities to each agent, and preserving protected logs. These measures minimize the attack surface and facilitate response if there is an intrusion.

How to implement the solution (practical steps)

What specific steps should I follow?

Short answer: 5 simple, ordered steps. Then, more detail for technical teams.

• Review and reduce permissions. Assign each agent only the credentials necessary (principle of least privilege).
• Separate functions: reading information and executing actions must be in different components.
• Require human approval for critical operations (transfers, mass sending, deletions).
• Enable immutable logs and store them out of agents’ reach.
• Prepare contingency procedures including network isolation and credential revocation.

Technical detail: separation prevents an injection of instructions (external text that the model interprets as an order) from automatically translating into an action; logs and identity per agent facilitate reconstruction and containment.

How to do it from the admin panel?

Menu names vary by platform, but the approach is universal: limit permissions, audit, and require human approval.

• Access Settings > Permissions and review assigned roles.
• Name agents and register credentials at Identity > Agents.
• Enable auditing at Logging > Audit Trail and configure retention outside the active system.

How to check if you are already affected and how to contain incidents

How can I tell if an agent is compromised?

Check key signs: unauthorized actions, messages sent without approval, logs with unusual deletions. If there are immutable audits, you will find clues to follow the incident.

What immediate containment measures exist?

If you suspect compromise: cut the agent’s network connections, revoke credentials, and interrupt communications with the model. Stopping only the process may not be enough; isolation and evidence preservation are necessary.

Verification checklist

• Have you assigned a unique identity to each agent?
• Do agents have only the necessary credentials?
• Are audit logs enabled and protected against modification?
• Is human approval required for high-impact actions?
• Is the isolation and credential revocation procedure tested?

From an expert’s point of view, these measures represent architectural changes that are not always costly: sometimes it is enough to disable useless permissions and implement a human approval process. It is a small investment compared to the risk.

Implementing them requires coordination between security, development, and business. The critical points are: not delegating authorization decisions to the model, preserving evidence, and preparing quick stop procedures. Micro actions and macro results. Period.

The operational recommendation is clear: prioritize controls that limit agents’ autonomy and maintain human supervision where any error can have significant consequences. This turns an AI deployment into a manageable capability instead of an uncontrollable attack surface.

Prevention and maintenance tips: update tools, review permissions periodically, enable multi-factor authentication, store backups, and protect audit logs. Detect signs such as unexpected sends, access to unrelated files, or abrupt activity increases.

When to seek professional help: if you cannot identify the origin of an unauthorized action, if revoking credentials does not stop communication, or if evidence can be manipulated, you must contact incident response specialists.

The reality is that limiting permissions, separating functions, and requiring human approval does not eliminate risk but significantly reduces it. Apply these measures, document everything, and prepare a response plan. Reduced permissions, immutable logging, and human supervision are the basic triad to contain incidents with AI assistants.