Relevant Software
Services
Data anonymization services

Data anonymization services

Your staging environment runs on a copy of production, and every engineer with access holds real customer records. Relevant Software anonymizes that copy so your tests keep passing on data that leads back to no customer, with the written assessment to prove it.

13+

Years in businessLarge expertise as a software development company

246

Projects deliveredEngineering excellence that solves industry challenges

99%

On-time deliveryMilestones land when we say they will

92%

Senior talentTop software engineers on our team

98%

Client satisfactionCustomers trust us with new projects

Our data anonymization services

Sensitive data discovery and classification
Anonymization design and technique selection
Data masking for non-production environments
PII and PHI de-identification for regulated datasets
Synthetic data generation
Re-identification risk testing and evidence
Anonymization for AI training and retrieval data

Sensitive data discovery and classification
Anonymization design and technique selection
Data masking for non-production environments
PII and PHI de-identification for regulated datasets
Synthetic data generation
Re-identification risk testing and evidence
Anonymization for AI training and retrieval data

Sensitive data discovery and classification

Scoping starts with an honest list of what identifies a person, and that list reaches well past the obvious columns. Data governance supplies the classification and sensitivity tags the work starts from, and our engineers extend them with the attribute combinations that single someone out on their own.

A field-level inventory naming every system, table, and column in scope
Quasi-identifier combinations ranked by how few people share them
Owner and sensitivity labels written back to your catalog

Anonymization design and technique selection

Each column earns its own treatment, chosen against the question the destination environment has to keep answering. Relevant Software puts the trade-offs on one page and your privacy lead approves it before a job runs.

A technique map per column covering generalization, suppression, perturbation, tokenization, and synthesis
Referential integrity rules that hold one customer together across systems
Utility thresholds agreed with the analysts who query the output

Data masking for non-production environments

Development, QA, and staging need production volumes and distributions to surface real defects. Relevant Software builds the anonymized copy that gives them both, and test data anonymization runs on a refresh schedule your teams set.

Format-preserving values that keep validations, joins, and integration tests passing
One customer resolving to the same record across CRM, billing, and warehouse copies
Refresh jobs your engineers trigger on demand

PII and PHI de-identification for regulated datasets

Relevant Software takes healthcare, financial, and HR records down to the fields a lawful purpose requires, with the route chosen against what your analyses need.

HIPAA Safe Harbor field removal, or Expert Determination support where the data has to stay richer
Date shifting and geographic generalization tuned per cohort
A residual risk figure recorded per released dataset

Synthetic data generation

Generated records carry the statistical behaviour of your production data while belonging to no real person, which suits demos, vendor evaluations, and early model work.

Generation models fitted to your distributions, correlations, and edge cases
Fidelity and utility scoring against the original dataset
Privacy checks confirming each generated row stands apart from every source record

Re-identification risk testing and evidence

Relevant Software puts a number on the claim and produces the document that carries it, structured to the three tests a European supervisory authority applies.

Singling out, linkability, and inference tested as three separate results
Attack simulation using the external datasets an adversary would plausibly hold
A written assessment covering method, thresholds, findings, and the reassessment schedule

Anonymization for AI training and retrieval data

Relevant Software strips direct and quasi-identifiers from training corpora and retrieval indexes, so the model learns the patterns while your identifiers stay inside the source system. Threat modeling for the agents that query that index, guardrail engineering, and the security evals that gate each release run through our AI security services.

Identifier removal from documents, transcripts, and tickets before chunking and embedding
Entity consistency across chunks so retrieval still resolves the right record
Provenance records mapping each corpus back to its source and its treatment

Challenges data anonymization addresses

Getting a data release past legal review

Relevant Software profiles the data, proposes a treatment per column, and produces the written assessment your legal team signs. The release decision rests on evidence.

Getting realistic test data out of production copies

Our engineers build masked environments that keep those shapes under substituted values, refreshed on the schedule your teams already expect.

Training corpora your model can learn from

We remove identifiers before chunking, keep entity references consistent across chunks, and record where each document came from.

Evidence that matches the 2026 EDPB criteria

Our engineers test your output against the three EDPB criteria set in July 2026 and document the result as the assessment behind your claim.

Referential integrity across systems after masking

We design consistent masking so the same source value maps to your CRM, billing database, and warehouse, and cross-system tests keep passing.

Analytics that keep working after the transformation

Thresholds come from the analysts who query the data, agreed before any job runs and recorded per column in the technique map.

Anonymization that keeps pace with schema changes

Our engineers wire monitoring into the pipeline that flags a new column carrying identifiers the day it appears.

Give your test environments data you can defend.

Book a scoping call

Paul Carse, CTO and Co-founder of Life Moments, UK

How a data anonymization project runs with Relevant

Anonymization is a regulated discipline, so the process produces the transformation and the evidence side by side. Each stage closes with an artifact your privacy lead, your engineers, or your auditors can inspect.

A mapped inventory of your sensitive fields
Your first week produces the list: every system, table, and column holding a direct identifier, plus the combinations that identify a person together. Your data owners confirm the scope and the price before any job runs.
An anonymization design your privacy lead approves
Each column gets a technique, each destination gets a utility threshold, and the reasoning sits on one page. Sign-off here is what the final evidence pack points back to.
A working transformation on a secure copy
Jobs run against a copy inside your boundary and produce the first anonymized extract. Your analysts query it while the design is still cheap to change.
Re-identification test results your engineers reproduce
Testing runs the three criteria and the plausible linkage attacks, then hands over the scripts and the thresholds. Anyone on your side reruns the whole set from the repository.
Anonymized environments feeding your teams
Our data engineering team wires the transformation into the pipelines that refresh those environments, so every load lands anonymized on schedule. Monitoring flags a new column carrying identifiers the day it appears.
An evidence pack your auditors accept
Method, thresholds, results, approvals, and the reassessment date arrive as one document set. Handover includes the retest schedule and the commands to run it.

How a <strong>data anonymization project runs</strong> with Relevant

Why teams bring data anonymization projects to Relevant

Certified security and compliance

ISO 27001, GDPR, and HIPAA-aligned delivery governs how our engineers handle your production copies, your credentials, and your repository access from day one. Secure copies stay inside your boundary for the whole engagement.

Evidence built to the 2026 EDPB criteria

Singling out, linkability, and inference arrive as three separate test results with their own thresholds, which is the structure a supervisory authority asks for. Our IT compliance programs for ISO 27001 and SOC 2 sit alongside the same evidence work.

The anonymization runs in the stack you already have

Airflow, dbt, and Spark run the transformation as an ordinary step, written into the layer your team already operates, with the output landing in your existing warehouse.

AI-assisted delivery on every engagement

Our AI-assisted development practice gives you up to 50% faster delivery and up to 20% fewer bugs, with every release still passing senior code review.

A costed roadmap in 14 days

Two weeks from the first call to a plan you can budget, covering the systems in scope, the technique map, and the order the environments come online. Delivery against those milestones runs at 99% on time.

Engineers who stay through the roadmap

With 96% employee retention, the team that writes your technique map in month one is the team running your reassessment in month twelve.

Our case studies

35%reduction in analyst workload

AI platform for banking

Built a compliant AI platform that delivers predictive insights for retail banking products

AI platform for banking

Built a compliant AI platform that delivers predictive insights for retail banking products

4AI modules shipped in the product

Childcare Data Pipeline Automation

Built a daily data pipeline with AI modules for an EdTech operator running 8 childcare centers

Childcare Data Pipeline Automation

Built a daily data pipeline with AI modules for an EdTech operator running 8 childcare centers

What clients say about collaboration with Relevant Software

4.9 is our Clutch
average

10+ years in data engineering. One team from discovery to the evidence pack.

Relevant Software runs the three tests, documents the method, and hands you an anonymization claim your auditors can check.

Request a free consultation

**Nataliia Dynka**Client Partnership Director

**Nelia Morozyk**Head of Sales Operations

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Questions about data anonymization services you may have

Does anonymized data still answer the analytics and model questions?

How is data anonymization different from the PII redaction we already run on prompts?

Does our case call for anonymization or pseudonymization?

What should we count before we talk about budget?

Our policy keeps data inside our country. Does anonymization still apply?

Start with one dataset. We test it, transform it, and leave the evidence that backs the claim.

Our data anonymization services

Sensitive data discovery and classification
Anonymization design and technique selection
Data masking for non-production environments
PII and PHI de-identification for regulated datasets
Synthetic data generation
Re-identification risk testing and evidence
Anonymization for AI training and retrieval data

Sensitive data discovery and classification
Anonymization design and technique selection
Data masking for non-production environments
PII and PHI de-identification for regulated datasets
Synthetic data generation
Re-identification risk testing and evidence
Anonymization for AI training and retrieval data

Why teams bring data anonymization projects to Relevant

Certified security and compliance

Evidence built to the 2026 EDPB criteria

The anonymization runs in the stack you already have

Airflow, dbt, and Spark run the transformation as an ordinary step, written into the layer your team already operates, with the output landing in your existing warehouse.

AI-assisted delivery on every engagement

Our AI-assisted development practice gives you up to 50% faster delivery and up to 20% fewer bugs, with every release still passing senior code review.

A costed roadmap in 14 days

Engineers who stay through the roadmap

With 96% employee retention, the team that writes your technique map in month one is the team running your reassessment in month twelve.

Start with one dataset. We test it, transform it, and leave the evidence that backs the claim.