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DATA & AI

Before an AI pilot, build a reliable evidence workflow.

Start with the use case, data permissions and a way to measure failure.

Define a narrow task

Choose a repeatable activity with clear inputs and a human owner. Examples include evidence retrieval, document classification or internal report drafting. Establish the current baseline so that improvement can be measured rather than assumed.

Check data rights and privacy

Record where the information came from, what use is allowed and whether it contains personal or confidential material. Consider a data protection impact assessment where the processing presents elevated risk. Seek project-specific guidance on the applicable obligations.

Evaluate outputs against real criteria

Build a small test set with known expectations. Examine unsupported claims, omitted evidence, misleading citations and failures across different groups or document types. Record the model and configuration used, because results can change.

Design a responsible handover

Identify who reviews outputs, when the system should abstain and how users report errors. Keep versioned instructions, evidence links and an incident process. Ara’s research-led approach places accountable human judgement at the centre.

Sources & scope

Official background sources accessed for this guide. Recommendations are Ara’s editorial synthesis, not statements issued by these institutions.

Data Protection Commission — Data protection compliance

Published 7 October 2026. Guidance is general; confirm current and project-specific requirements before acting.

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