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NELLA Labs

Responsible AI

Screen for high-risk processing during discovery, not before launch

A data protection impact assessment discovered two weeks before go-live is a schedule problem. Discovered during discovery, it is a design input.

NELLA Labs Security · Security, privacy and trust6 min read

The pattern is familiar. A project reaches pre-launch review, someone from legal or risk asks whether a DPIA has been completed, and the answer is no. What follows is either a delayed launch or a document produced quickly to satisfy a process — and the second option is worse, because it creates a record asserting that risks were assessed when they were not.

Triage takes an hour and changes the architecture

Screening for high-risk processing is not the assessment itself. It is a short set of questions that determines whether a full assessment is likely to be required, and it is cheap enough to run in every discovery engagement regardless of sector.

  • Does the system evaluate or score people in a way that affects what they receive or are offered?
  • Does it make automated decisions with legal or similarly significant effect?
  • Does it process special-category data — health, biometric, ethnicity, or data about children?
  • Does it monitor a publicly accessible area, or track behaviour systematically?
  • Does it combine datasets that were collected separately, in a way the person would not expect?
  • Does it use a technology whose privacy implications are not yet well understood by the people it affects?

Any yes means a DPIA is likely required. More usefully, each yes points at a specific design decision. Automated decisions need a documented human review route. Special-category data needs its own storage, permissions and retention. Combining datasets needs a lawful basis you can articulate before you build the join.

What to build as a result

The engineering consequences of a positive screen are concrete and cheap when designed in: an append-only audit trail for decisions, a human review path with recorded reasoning, per-category retention with implemented and tested deletion, and access controls that separate special-category data from ordinary application data. Retrofitting any of these into a shipped system costs an order of magnitude more than including them from the start.

We are engineers, not lawyers, and none of this is legal advice. The point is narrower: make sure your counsel is assessing a system that was designed with the question in mind, rather than one where the answer has to be manufactured after the fact.

privacyDPIAgovernancediscovery

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