Service 04 / Exfiltration

Data Exposure

We test whether your AI can be made to give up data it should protect.

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04Exfiltration
What we test
  • Cross-customer data access
  • Sensitive data extraction
  • Credential & config leakage
  • Output filtering gaps
Maps to
LLM02LLM06
Typical targets
Any AI touching real data

What it is

AI systems sit on your most sensitive data — customer records, internal documents, credentials, other users' information. We test whether it can be pulled out through ordinary interactions.

The serious case is cross-customer leakage: can one user, with the right request, retrieve someone else's data? We stop the moment real data surfaces — the proof is the behaviour, not the data.

If your AI can reach it, assume an attacker will try to reach it through your AI. We find out first.

The attack path
EntryUser query
WeaknessData access
ImpactData exfiltrated
Real-world example
Public incident

Confidential source code pasted into a public AI tool

At a major electronics company, engineers pasted confidential source code and internal notes into a public AI assistant for help. The sensitive material left the company's control.

The company moved to restrict AI use. Data leaves through AI in ways staff never intend — before an attacker even tries.
Publicly reported incident, shown for illustration — not a DeepSentra assessment.
The risk of not testing

What’s at stake if you skip it.

Data breach

Customer or company data extracted through the AI is still a breach, with all the fallout.

Regulatory exposure

Leaked personal data means GDPR obligations, reporting and potential fines.

IP loss

Confidential business information and code can walk out through a chat box.

Loss of trust

Customers don't distinguish 'the AI did it' from 'you did it.'

Free 30-minute consultation

Is your AI exposed to this?

Book a free, no-obligation consultation and we’ll take a first look at your data exposure risk together.

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