Data minimisation is one of the most effective risk controls for AI systems: if sensitive data never reaches a model or an external provider, it cannot be leaked, logged, or accidentally echoed back in a response. It also reduces cost because fewer tokens are processed.
Most organisations approach this as a privacy exercise. The best teams treat it as an architecture capability: consistent patterns, reusable components, and auditable enforcement.
Start with “what is necessary?”
Data minimisation begins with intent. For each use case, define the minimum information needed to complete the task. Then design the data flow to provide only that:
- Customer support assistant. Provide issue description and product metadata; avoid full customer profiles unless required.
- Policy QA assistant. Provide policy text and citations; avoid internal IDs and unrelated records.
- Workflow automation. Provide structured fields required for the action; avoid raw free-text where possible.
Use data contracts to document this boundary and keep it stable as teams and vendors change (see data contracts for AI).
Apply minimisation before the model call
Do not rely on “please don’t output sensitive data” prompt instructions. Enforce minimisation upstream:
- Field selection. Whitelist fields per use case rather than blacklisting everything else.
- Redaction. Remove PII/PCI/PHI patterns before they enter the prompt.
- Tokenisation. Replace identifiers with surrogate tokens that can be resolved later by a trusted system.
- Summarisation. Summarise long histories and keep the smallest relevant slice (see context engineering).
A practical pattern is a “privacy gateway” that sits between your application and model provider. It applies minimisation consistently and logs what it changed, without persisting raw sensitive content unnecessarily.
Be careful with logs and caches
Many data exposures come from observability and caching, not from the model itself:
- Observability. Ensure prompts and tool outputs are filtered before they hit logs (see AI observability).
- Caching. Cache intermediate artefacts and avoid caching raw prompts with sensitive data (see caching strategies).
- Secret handling. Keep secrets out of prompts entirely (see secrets management).
Guard outputs too
Even with minimisation, models can infer or reconstruct. Add output controls:
- Output scanning. Detect and block accidental PII disclosure patterns.
- Citation requirements. For RAG, require citations so the model cannot “invent” sensitive details.
- Refusal + escalation. When a user asks for restricted data, refuse and route to approved channels.
Make minimisation measurable
Track “data exposure budgets” alongside cost budgets:
- Tokens sent per request and the percentage removed by redaction/summarisation.
- Incidents or near-misses involving sensitive output.
- Coverage of minimisation policies across products and teams.
Data minimisation is not a single control. It is a system: architecture, policy, observability, and change control. When those align, organisations can move faster with AI because risk is reduced at the source.