Documents are the fuel of AI — but they contain personal data. Synthetic data allows organizations to preserve realism and context while eliminating legal risk. In this article, we explain how to safely prepare documents for use with LLMs.
More and more organizations want to use documents as a knowledge source for AI systems: building RAG architectures, training language models, and developing internal assistants powered by LLMs. In practice, however, nearly every such initiative encounters a fundamental problem — documents contain personal data.
Traditional anonymization (black boxes, removed fragments) often proves insufficient. AI models require linguistic context, structural consistency, and realistic data.
That is why synthetic data in documents is increasingly becoming the preferred solution.
Synthetic data is algorithmically generated data that:
Example transformation:
Original Data -> Synthetic Data
Jan Kowalski -> Michał Nowak
ID 80010112345 -> ID 92030467890
12 Słoneczna St., Warsaw -> 48 Lipowa St., Poznań
After transformation, the document:
As a result, it can be safely used in AI projects.
Traditional document anonymization typically involves redacting personal data using blacked-out sections.
This approach has several limitations:
Language models learn patterns. If gaps appear in the text, model performance deteriorates.
Synthetic data preserves semantic continuity while removing legal risk.
From a GDPR perspective, one key question determines everything: Is identification of a natural person possible?
If data has been effectively replaced with synthetic data:
In practice, this means that documents cease to fall under the personal data regime.
This significantly simplifies:
The biggest challenge lies in unstructured documents:
A secure synthetic data workflow typically includes:
The effectiveness of the process depends on the integration of OCR, NLP, and controlled content transformation.
In practice, a secure architecture may look like this:
Source documents → Personal data detection
→ Replacement with synthetic data
→ Indexing / embeddings
→ RAG system / LLM
This model:
Synthetic data is no longer a technological curiosity — it is becoming a foundation of responsible AI governance.
1. RAG System Development
Documents can be indexed without processing personal data.
2. Testing and PoC
Language models can be safely tested on realistic documents.
3. IT System Demonstrations
Technology vendors can present realistic documents without exposing client data.
4. Research and Development Projects
Preserving linguistic context is crucial for model quality.
5. Training and Educational Materials
Documents maintain a natural appearance while containing no real personal data.
Mycroft Engine enables:
The engine can serve as part of a broader AI data preparation pipeline — acting as a protective layer separating production data from LLM systems.
In the context of growing regulations (GDPR, AI Act, NIS2), organizations need:
Replacing personal data with synthetic data is one of the most effective ways to meet these requirements — particularly in projects involving large language models.
Documents are the fuel of modern AI systems. At the same time, they contain personal data that often should not be exposed to language models.
Synthetic data:
The document detection and transformation technology developed by Mycroft Solutions is built on a secure-by-design paradigm. Mycroft Engine enables automatic replacement of personal data with synthetic data in documents — locally, with full control over the process, and without cloud processing.
