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Data Privacy & Secure AI

Use AI on sensitive data without the compliance risk.

Feeding customer or employee data into a public LLM API is a GDPR risk. We solve this with anonymization pipelines that strip PII before it leaves your environment, and private deployments that keep everything on your infrastructure. Our own work on IT operations data taught us that infrastructure identifiers, ticket numbers and company registry data deserve the same protection as personal data, and our pipelines are built that way.

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What we deliver

Data Privacy & Secure AI services

  • PII Anonymization Pipeline

    Automated detection and removal of personal data before LLM processing.

  • GDPR-Compliant AI Deployment

    Architecture and implementation that keeps personal data out of external APIs.

  • Private LLM Infrastructure

    On-premise or VPC-hosted language models: your data never leaves your environment.

  • AI Security Audit

    Review of an existing AI system's data flows for compliance gaps.

Best for

Any organization that processes personal or confidential data and wants to use LLMs without sending that data outside. Especially relevant in regulated industries: finance, healthcare, HR and legal.

Case studies

Related project

Multi-layer Data Anonymization Before Public LLMs

Client: Qlos

Challenge
Diagnostic data collected from Qlos's and its clients' systems (tickets, logs, documentation) carries personal data (names, e-mail addresses, phone numbers, national ID numbers, postal addresses, usernames), company identifiers (client and provider names, Polish tax and company registry numbers) and infrastructure and operational details (hostnames, IP addresses and ranges, ticket numbers). None of it could be sent to a public LLM as-is.
What we built
An anonymization pipeline with multiple independent detection layers, running inside the environment before any request reaches an external model. Beyond standard personal data, it covers IT-specific identifiers that generic PII tools miss.
Result
Public LLMs can be used on real operational data without exposing personal data or the infrastructure details of Qlos's clients.

“Our clients trust us with their infrastructure. Before any of that data reaches a public model, names, addresses, hostnames and IP addresses are stripped out. That was our condition for using LLMs at all.”

Information Security Officer, Qlos

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Contact

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