Data & AI
Data Anonymisation Services
Use your data for AI and analytics without exposing the people in it. We find sensitive information, protect it with the right techniques and keep the data useful.
Talk to the foundersWhat data anonymisation is, and what you get with Authect
Data anonymisation, also called de-identification, removes or replaces personal and confidential information in your data, so it can be analysed, shared or sent to an AI model without revealing who or what it's about. Done well, the data stays meaningful; done badly, it's either unsafe or useless.
We design anonymisation that fits how you actually use data: feeding documents to language models, sharing datasets with partners, building test environments or training models. The founders scope it with you and a senior engineer builds it, choosing techniques that protect privacy while keeping the context your teams and models need.
Where AI is involved, our approach can swap sensitive details out before a request leaves your environment and restore them in the response, so you get the benefit of powerful external models without handing over the data that matters most.
Techniques
The data anonymisation techniques we use
No single technique suits every dataset. We combine them based on your data types, the regulations you work under and what the data needs to remain useful for.
Contextual replacement
Sensitive values are swapped for realistic, context-aware substitutes generated by models that run inside your environment, so text still reads naturally and keeps its format.
Pseudonymisation
Direct identifiers become consistent artificial ones. With secure key management, authorised people can re-identify records when there's a legitimate need.
Format-preserving randomisation
Values such as card or account numbers are scrambled irreversibly but keep their original format, so systems and tests keep working.
Synthetic data generation
Entirely artificial datasets that keep the statistical patterns of the original without containing any real person, ideal for testing and model training.
Data perturbation
Controlled noise or small variations are added to values, protecting individuals while preserving the overall trends analysts depend on.
Removal and suppression
Fields or rare values that pose a re-identification risk are removed or generalised, while the rest of the dataset stays intact.
Data masking
Sensitive values are replaced with tags or placeholder characters, such as [Name] or [Date], keeping the structure clear for people and models reading it.
Data swapping
Attribute values are exchanged between records, breaking the link between individuals and their details while keeping totals and distributions accurate.
Choosing an approach
Masking, pseudonymisation or synthetic data?
Three approaches cover most needs. Here's how they differ, so you can see which one fits each use of your data.
| Masking and replacement | Pseudonymisation | Synthetic data | |
|---|---|---|---|
| What happens to the data | Sensitive values are hidden or replaced in the real records. | Identifiers are swapped for consistent stand-ins, with a protected key kept separately. | A new, artificial dataset is generated to mirror the original's patterns. |
| Can it be reversed? | Only if we keep a secure mapping on purpose, for example to restore AI responses. | Yes, by authorised people with access to the key. | No: there are no real people in it to re-identify. |
| What it keeps | The structure and meaning of each record and document. | The ability to link records about the same person across datasets. | Statistical relationships, for testing, analysis and model training. |
| Typical uses | Sending documents and prompts to language models, sharing data with suppliers. | Analytics and research that need to follow individuals over time. | Test environments, demos, model training and sharing data with partners. |
| Things to watch | Free text can hide identifiers in unexpected places, so detection needs testing. | It is still personal data under privacy laws, so access must be controlled. | Rare cases can be underrepresented, so quality must be checked against real data. |
Service benefits
The benefits of our data anonymisation services
- 01
Use AI without leaking data
Send documents and questions to powerful AI models while personal and confidential details stay inside your environment, keeping you within your legal duties and your customers' expectations.
- 02
Keep the meaning in your data
Contextual techniques preserve what the data says, so analysts, models and AI assistants still get useful results instead of a page of blanks.
- 03
Security without taking everything offline
By protecting only the sensitive parts of your data, you avoid the cost of running every AI service privately, while keeping the information that matters fully protected.
- 04
More trust from your customers
When people know their data is protected before it's analysed or shared, they're more willing to use your product and share the information that makes it better.
- 05
Compliance and data quality together
We help you put clear policies and checks in place that support GDPR and similar regulations, while keeping data accurate and fit for purpose.
- 06
Flexible protection that scales
Support for structured tables, free text, documents and logs, with protection levels you can tune per use case and pipelines that handle growing volumes.
Protect sensitive data on its way to an AI model, and back
Many businesses want the quality of the best external language models but can't send them customer names, account numbers or confidential deal terms. We solve that with a protective layer that sits between your systems and the model.
Before a request leaves your environment, the layer detects sensitive details and replaces them with consistent stand-ins. The model works on the protected text, and when its answer comes back the original details are restored for the people who are allowed to see them.
How it works on your project:
- Detection tuned and tested on samples of your own documents
- Replacement that keeps text readable, so model answers stay accurate
- Restoration of the original details only for authorised users
- Logs that show what was protected, without storing the sensitive values
How we work
Our full-cycle data anonymisation process
We treat anonymisation as an engineering problem with a measurable result: sensitive data found, protected and checked, with evidence you can show an auditor.
Want to use your data for AI and analytics without putting privacy at risk? Let's talk.
Contact usWhy Authect
Why choose Authect for data anonymisation
Privacy work needs engineers who understand data, AI and security together. That's how we build every project.
AI, data and security in one team
The same senior engineers who build AI features and data pipelines design your anonymisation, so protection fits the way your data is really used rather than being bolted on afterwards.
End-to-end privacy work
We cover discovery, technique selection, implementation, integration with your systems and ongoing monitoring, so you don't have to stitch together several specialists.
Tailored to your industry and regulations
We adapt techniques to the data and rules you work with, from financial records to health information, and document our choices so your compliance team can review them.
Founder-led and transparent
You talk directly to the founders and the engineer doing the work, and you can see exactly what's protected, how and why.
Your data stays yours
Anonymisation runs in environments you control, and the code, models and configuration are yours from day one, with no lock-in.
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FAQs
Data anonymisation FAQs
What is data anonymisation?
It's the process of removing or transforming personal and confidential information in data, wherever it appears, so the data can be used without revealing who it's about. It covers names and contact details, but also indirect clues such as rare combinations of age, location and job.
What does a data anonymisation service involve?
Finding where sensitive data is, choosing techniques that protect it while keeping it useful, building those techniques into your systems and checking that they work. We also monitor protection over time as your data changes.
What is an example of anonymised data?
A support ticket where the customer's name, email and order number have been replaced with realistic stand-ins, so the problem described is still clear but the customer can't be identified. Another is a synthetic dataset of transactions that behaves like the real one without containing any real customers.
What is the difference between data masking and anonymisation?
Masking is one anonymisation technique: it hides or replaces specific values. Anonymisation is the wider goal of making data impossible to link back to individuals, which often needs several techniques together, including handling indirect identifiers.
How do you anonymise data before using it with AI?
We put a protective layer between your systems and the model. It detects sensitive details in prompts and documents, replaces them with consistent stand-ins, sends the protected text to the model and restores the original details in the response for authorised users.