Privacy Engineering

Build privacy into systems before risk becomes expensive

Gatecroft helps product, data, cloud and transformation teams embed privacy-by-design controls into architecture, workflows and operating routines before deployment.

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Privacy by design for products, platforms and data flows

Privacy engineering turns regulatory principles into technical and operational decisions. Gatecroft supports teams with data minimisation, access design, retention rules, consent logic, vendor controls, telemetry reviews and launch-ready privacy evidence.

Typical engineering outputs

Privacy-by-design control map

Data-flow and access architecture review

Retention, deletion and consent design decisions

Implementation evidence for privacy accountability

Privacy engineering modules

Practical support for teams building or modernising systems that process personal, sensitive or regulated data.

Product Privacy Design

Embed privacy requirements into user journeys, consent patterns, data collection, notices, feature approvals and launch criteria.

Product Privacy Design

Embed privacy requirements into user journeys, consent patterns, data collection, notices, feature approvals and launch criteria.

Data-Flow Architecture

Map how data moves across systems, teams, vendors, regions and environments so minimisation and access controls are clear.

Data-Flow Architecture

Map how data moves across systems, teams, vendors, regions and environments so minimisation and access controls are clear.

Cloud & Platform Controls

Review logging, telemetry, access roles, retention defaults, encryption decisions, data residency and configuration risk.

Cloud & Platform Controls

Review logging, telemetry, access roles, retention defaults, encryption decisions, data residency and configuration risk.

Operational Privacy Controls

Design repeatable controls for DSARs, deletion, incident response, vendor reviews, audit evidence and change management.

Operational Privacy Controls

Design repeatable controls for DSARs, deletion, incident response, vendor reviews, audit evidence and change management.

A delivery model for embedding privacy into delivery

01 Map
Clarify processing purposes, data flows, roles, vendors and system touchpoints.

02 Design
Define minimisation, access, retention, transparency, consent and control requirements.

03 Embed
Translate controls into delivery tickets, launch gates, evidence and operating routines.

Building a product, AI system or platform with privacy risk?

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