An AI agent reads data and instructions as the same text. The scope of its actions, the access it holds and the cases that need a confirmation are decided at design time.
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A language model does not separate the data it reads from the instructions it receives: they are the same text. A document, an email or a web page can therefore carry a request that the agent carries out as though it came from you. It is called prompt injection, and it is why an agent is designed starting from its powers.
The method is the one used for any application that touches company data: which actions it may take, on which systems, with which credentials, and when a person has to approve. The choice of model comes afterwards, and it can be changed.
CVE-2025-32711 has a date, two severity scores and an address where anyone can read it. The article that tells it.
The GDPR and the AI Act for processing and for the duties on high-risk systems; for offences and civil liability connected with AI, Italian Legislative Decree 160/2026.
Whoever uses the tools learns to spot a request that should not be carried out: professional use of AI.
Software designed and developed entirely in Europe.
If the data cannot leave the company, that is another chapter: how on-premise AI works. For the people who will use the tools there are the training courses.
Every agent has a closed list of operations available to it: anything outside that list goes through a person.
Credentials that belong to the agent, carrying the permissions of the task it has to do and nothing more.
Where the model runs is a design decision, and it changes who answers for the data the agent reads.
Assistants and automations wired to email, files or the management system, read through one by one.
Which operations the agent performs on its own, and which stay with a person.
Dedicated credentials and minimum permissions for every system the agent touches.
Documents and messages built to push the agent off course, before it goes live.
A log of the actions, a periodic review and quick revocation when something changes.
It is a request hidden inside a document, a message or a web page, which the agent reads and carries out as though it came from you. The defence lies in the powers: if the agent cannot issue a payment or delete an archive, a hidden request gets nothing.
Yes, and it is an architecture decision rather than a setting: which folders it reads, what it may send, which messages need a confirmation. The difference between a trial and a live service lies in those answers.
Yes. The review lists, for each tool, which data it reaches, which actions it takes on its own and where a person's approval is missing. What comes out is a list of fixes, most serious first, with the work each one takes.
No, and the two answer different questions. A local model decides where the data is processed; the boundary decides what the agent may do. An agent running on your own servers can still write into your systems.
From the log: every action leaves the time, the operation and the data it touched. It is also how you measure whether it is doing the job it was put there for.