AI Security in Enterprises: How Data Leaks Occur When Using ChatGPT/Copilot-Type Assistants (and How to Prevent Them)

Generative AI is accelerating productivity in areas such as procurement, legal, finance, and operations. But it is also opening up a new risk: data leaks through normal, well-intentioned use, without the need for a "hack."

Free tool: Shadow AI Checklist. 14 questions to find out if your team is using AI without control and with what data.

This is exactly what AI Trust & Safety (AI Security and Trust) addresses: policies, controls, and monitoring to ensure AI delivers value without becoming a channel for exposure.

An analyst opens the corporate AI assistant and writes: "Summarize this contract and tell me the risks."

policies + DLP + Zero Trust + monitoring + exposure assessment, so that AI adds productivity without opening a silent pathway for information leakage.

To save time, copy and paste the entire document: rates, confidentiality clauses, contact data, appendices, and in some cases internal identifiers or banking information. The summary comes out perfect. The problem occurs afterwards. Depending on the tool's configuration and its integrations, that content may remain:

• stored in the chat history, • logged for support or audit purposes, • indexed for internal searches, • exposed to integrations (tickets, CRM, corporate drive) if there are no restrictions.

There was no attack. Just "productivity" without governance. And that is enough to generate exposure of sensitive information, contractual breaches, and legal risks.

Contracts, prices, agreements with suppliers, customer data, architecture, incidents, credentials "pasted by mistake."

How to prevent data leakage with DLP?

Teams using external tools (or personal accounts) to solve tasks, taking information outside the perimeter without controls.

Documents or web pages may include malicious instructions for the assistant to ignore rules and extract more information than it should.

If your company operates with ISO 27001 / ISO 20000 or contractual obligations, uncontrolled AI use can break information treatment and protection requirements.

If you are adopting assistants like Copilot / Enterprise ChatGPT / internal LLMs, these controls are the starting point:

✅ 1) AI Usage Policy (clear and applicable)

What can be shared, what cannot, and concrete examples by area (legal, finance, HR, sales).

✅ 2) DLP for AI (input and output)

Data leakage prevention: detect and block PII, banking data, contracts, credentials, classified information.

• What is a DLP?

✅ 3) Classification + permissions by context (Zero Trust applied to AI)

The assistant should only see what the user can see and with limits: no "broad access" for convenience.

• Click here for more information on what Zero Trust is

✅ 4) Retention, audit, and traceability

Minimum necessary logs, protected and auditable: knowing what was queried, what was shared, who, and when.

✅ 5) Practical training (with real cases)

Not "generic tips": scenarios like the contract, credentials, commercial proposals, incidents, etc.

If your organization is already using generative AI, the question is not whether it is "worth it". The question is: Can your company demonstrate that it is using AI with security, confidentiality, and compliance controls?

Topics that might interest you:

• What is CTEM (Continuous Threat Exposure Management) • IT Services • What are Non-Human Identities

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