AI agent security, privacy and the EU AI Act

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An AI agent may read information, choose tools and perform actions. An error can therefore have more serious consequences than an incorrect chat answer. Security must be designed in from the beginning through limited permissions, approved sources, realistic testing, logging and explicit human checkpoints.

This guide is a practical overview, not legal advice for a specific organisation.

Begin with information

Map the information an agent can see. Personal data, trade secrets and security-sensitive material require special care. Review vendor terms, processing locations, retention periods, subprocessors and whether content is used to improve models.

The GDPR requires a defined purpose, a valid legal basis, data minimisation and appropriate security when personal data is processed. Do not collect more than the task requires. Set retention periods and access rights. A data processing agreement may be necessary when a supplier processes personal data on behalf of an organisation.

Limit what the agent can do

Apply least privilege. An agent that needs to read order status should not be able to change payment information. Separate read tools from write tools. Risky actions should display a preview and require explicit approval.

Where possible, permissions should reflect the actual user. A shared superuser account makes accountability difficult and creates unnecessary risk.

Prompt injection and malicious content

Prompt injection occurs when text attempts to make a model ignore its rules. It may come directly from a user or indirectly from a document, email or website that an agent reads. “Ignore previous instructions” is a simple example, but attacks can be subtle.

Treat external content as data, not trusted system instructions. Limit tools, validate outputs, isolate sensitive functions and test with adversarial examples. No single prompt provides complete protection.

The EU AI Act and accountability

The EU AI Act follows a risk-based approach. Requirements depend on a system’s function, use and the organisation’s role. Some practices are prohibited, certain systems are classified as high-risk and general-purpose AI models have specific obligations. Application occurs in phases.

Do not assume that an agent is low-risk because it is built in a common chat tool. Assess the actual use. Internal writing support differs from a system that affects recruitment, credit or another consequential decision. Document purpose, data, responsibility and human oversight, and check current official guidance.

A safer example

A recruitment assistant may help anonymise text or check that a job advert follows a template. It is not permitted to rank candidates or decide who advances. Uploading applications to an external service is blocked until contracts and data protection have been assessed. Both data and decision power are limited.

Incidents and continuity

Decide what happens if an agent exposes information, performs an unauthorised action or produces systematic errors. Tools must be easy to disable and access tokens easy to revoke. Logs should support investigation without storing unnecessary personal data. Back up important instructions and keep version history.

Practical checklist

  • Are purpose and accountable owner documented?
  • Are data and permissions minimised?
  • Have supplier terms and data protection been assessed?
  • Can external text influence tool calls?
  • Do risky actions require approval?
  • Are manipulation, failures and missing data tested?
  • Can access be disabled and incidents investigated?
  • Is the system reviewed when laws, sources or models change?

In summary

Secure agent design begins with data, permission and consequences, not a longer prompt. Minimise access, separate recommendations from actions, test attacks and keep a person accountable. Evaluate GDPR and the AI Act for the actual use case.

Sources

Last reviewed: 16 September 2026.