Consultancy and Services
Privacy Engineering & Compliance Support
Build privacy requirements into the systems that handle sensitive information.
Purpose & context
Build privacy requirements into the systems that handle sensitive information.
We help organisations move from privacy obligations to implementable controls across applications, data platforms, analytics and sharing workflows. The work combines data-flow mapping, minimisation, access and retention design, privacy-enhancing techniques, technical DPIA support and assurance testing. Where retrieval systems, machine learning or generative models are genuinely part of the client's environment, we extend the same privacy engineering discipline to training data, embeddings, prompts, logs, model outputs and automated PII handling rather than treating those technologies as a separate compliance programme.
A good fit when
- Legal or governance requirements are clear, but engineering teams need concrete technical controls and acceptance criteria.
- Sensitive data is copied across operational, analytical, partner or model-enabled environments and exposure needs to be reduced.
- A DPIA, product launch, data-sharing arrangement or platform redesign needs technical evidence rather than policy statements alone.
Services in this area
Practical support for your priorities.
Data-flow and privacy architecture
Map personal data, purposes, processors, access paths, copies, retention and deletion points, then translate findings into architecture and control requirements.
Minimisation and sensitive-data handling
Reduce unnecessary collection and duplication, design field-level controls, retention and deletion, and introduce automated classification or redaction where operational scale makes manual handling unreliable.
Privacy-preserving analytics and sharing
Assess aggregation, de-identification, synthetic data, differential privacy and controlled-computation approaches against the intended analytical use and realistic re-identification risk.
Model and retrieval privacy where applicable
When model-enabled systems are in scope, review data ingestion, retrieval stores, prompts, logs and outputs; add PII filtering and access boundaries; and test representative cases for unintended sensitive-data leakage or memorisation.
DPIA and compliance engineering
Provide technical evidence, control mappings, residual-risk analysis and implementation requirements that support DPIAs, processor reviews, governance decisions and ongoing privacy assurance.
What we cover
Focused work. Useful evidence.
Personal-data inventory and end-to-end data-flow mapping
Data-minimisation, access, retention and deletion architecture
Technical privacy requirements for applications, APIs and data platforms
Synthetic-data and differential-privacy assessment and implementation support
De-identification and re-identification risk evaluation
Automated PII discovery, classification and redaction design
Sensitive-data controls for retrieval, model and logging pipelines when in scope
Authorised testing for unintended sensitive-data disclosure
Technical DPIA support and processor-control review
Privacy-by-design guidance for engineering and product teams
What you take forward
Outputs designed to be used.
The final scope is agreed together. Depending on your needs, the engagement can include:
- Privacy architecture and personal-data flow map
- Prioritised privacy engineering requirements
- Minimisation, access, retention and deletion control design
- Privacy-enhancing technology evaluation or implementation plan
- PII handling and redaction specification
- DPIA technical annex and residual-risk register
- Privacy assurance test plan and review criteria
- Implementation backlog and governance handover
How we approach it
From the question to a practical next step.
Map what actually happens to the data
Trace collection, transformation, access, sharing, analytics, exports, backups and deletion. Compare the operating reality with the stated purpose, consent, contracts and governance requirements.
Engineer the lowest-exposure workflow
Remove unnecessary fields and copies first, then strengthen access, retention and processing boundaries. Select privacy-enhancing techniques only where they add measurable protection without undermining the required use.
Validate and document the remaining risk
Test important controls and representative disclosure paths, document assumptions and residual risk, and provide evidence that governance, legal and technical teams can use together.
Illustrative use case
What this can look like in practice.
A client is redesigning a reporting platform that combines operational records, partner data and an internal retrieval-based assistant. We can trace personal-data flows, define minimisation and retention rules, design access boundaries and automated PII redaction, evaluate whether analysts can use synthetic or aggregated alternatives, test the retrieval workflow for unintended disclosure and package the evidence into the client's DPIA and implementation backlog.
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 privacy architecture and personal-data flow map are reviewed against the decisions you need to make. Limitations, dependencies and follow-up work remain visible in the handover.