Research
Privacy Engineering & Data Protection
Turn privacy obligations into controls that work inside real systems.
Purpose & context
Turn privacy obligations into controls that work inside real systems.
Privacy requirements become meaningful when they are implemented in the systems that collect, transform, analyse and share information. We help organisations translate legal, ethical and governance obligations into practical technical controls: data minimisation, purpose-aware access, retention controls, privacy-preserving analysis, automated sensitive-data handling and safer data flows. Where machine learning, retrieval systems or generative models are part of the environment, we extend those same principles to training, prompts, embeddings, retrieval stores, logs and model outputs, with a focus on reducing unnecessary exposure and testing for unintended disclosure rather than treating AI privacy as a separate trend-led service.
A good fit when
- Sensitive information moves through several applications, teams, processors or analytical environments and you need clearer technical controls around it.
- A product, research workflow or data platform needs privacy protections designed into the architecture rather than added as documentation at the end.
- You need useful analytics, model development or data sharing while reducing unnecessary disclosure, retention and re-identification risk.
Services in this area
Practical support for your priorities.
Privacy-by-design architecture
Map personal and sensitive data across collection, transfer, storage, processing, analytics, sharing, retention and deletion. Reduce unnecessary collection, isolate sensitive fields, strengthen access boundaries and document where privacy risk is introduced by integrations or secondary use.
Privacy-preserving data and analytics
Evaluate and implement techniques such as aggregation, synthetic data, differential privacy, controlled de-identification and secure computation where they are appropriate. Test the trade-off between usefulness and disclosure risk instead of assuming that removing names makes a dataset safe.
Sensitive-data controls for model-enabled systems
Where models are genuinely part of the workflow, review what data reaches training, retrieval and inference components. Design PII discovery and redaction, retrieval-store access controls, prompt and logging minimisation, retention rules and tests for unintended memorisation or sensitive-data leakage.
Data protection engineering and assurance
Translate privacy obligations into data-flow controls, technical requirements, DPIA evidence, processor safeguards, retention schedules and review criteria that engineering and governance teams can operate together.
What we cover
Focused work. Useful evidence.
Map personal and sensitive data flows, purposes, processors, retention points and exposure paths
Design data-minimisation, field-level access, segregation, deletion and retention controls
Implement and evaluate synthetic-data generation and differential-privacy workflows where suitable
Assess de-identification, aggregation and re-identification risk for analytical and shared datasets
Design secure-computation or controlled-analysis workflows for sensitive information
Build automated PII discovery, classification and redaction into ingestion and processing pipelines
Review privacy controls around retrieval stores, embeddings, prompts, logs and model outputs when model-enabled systems are in scope
Test for unintended sensitive-data disclosure or memorisation using authorised, representative evaluation cases
Develop DPIA evidence, privacy control requirements and engineering review criteria
Train product, engineering and data teams on practical privacy-by-design decisions
What you take forward
Outputs designed to be used.
The final scope is agreed together. Depending on your needs, the engagement can include:
- Personal-data flow and exposure map
- Privacy architecture and control design
- Data-minimisation, retention and access-control requirements
- Evaluation of synthetic, de-identified or privacy-preserving analytical approaches
- PII handling and redaction workflow design
- Privacy test plan for data and model-enabled systems where applicable
- DPIA technical evidence and residual-risk record
- Implementation backlog with owners, priorities and review points
How we approach it
From the question to a practical next step.
Trace the information and its purpose
Identify what is collected, whose information it is, why each field is needed, where it moves, who can access it and how long it remains available. Include copies created by analytics, exports, logs, backups, integrations and model-enabled workflows where relevant.
Reduce exposure before adding complexity
Start with minimisation, separation, access restrictions, retention and safer defaults. Add synthetic data, differential privacy, secure computation or automated redaction only where they solve a defined risk and their utility limitations are understood.
Test the controls against realistic disclosure paths
Use representative, appropriately authorised data and scenarios to evaluate whether sensitive information can be exposed through datasets, interfaces, logs, retrieval systems or outputs. Record residual risk, operating assumptions and the conditions that should trigger review.
Illustrative use case
What this can look like in practice.
An organisation wants analysts, partners and an internal model-enabled search tool to use the same sensitive information. A privacy-engineering engagement could map the data flows, remove unnecessary fields, separate direct identifiers, design role-based access and retention rules, compare synthetic or aggregated alternatives for analysis, add PII filtering before data enters the retrieval pipeline, test representative queries for unintended disclosure and document the resulting controls for the organisation's DPIA and operational review process.
An example of the work—not a client case study.Reference frameworks
Explore independent guidance that can inform the scope and evaluation criteria for this work. These links do not imply accreditation or endorsement.
Planning an engagement
What should we prepare?
Bring the decision you need to make, a description of the relevant systems or data, known constraints and your timeline. We can agree the appropriate access and confidentiality arrangements before any sensitive information is shared.
How do we define success?
We agree specific questions and useful outputs at the start. Outputs such as personal-data flow and exposure map are reviewed against the decisions you need to make. Limitations, dependencies and follow-up work remain visible in the handover.