For AI implementations a DPIA becomes mandatory when the planned processing of personal data is *likely* to result in a high risk to the rights and freedoms of natural persons, before processing starts. According to documentation based on Article 35 GDPR and UK ICO guidance, this threshold often appears with profiling, large-scale processing, or the use of new technologies.
In practice AI most commonly triggers this obligation in three situations: automated evaluation of individuals producing legal or similarly significant effects, large-scale processing of sensitive data, and systematic monitoring of public spaces. In the Polish context discussed by industry sources, national lists of high-risk operations should also be considered and one should not assume that the mere use of AI always means a DPIA is required.
The minimum scope of a DPIA follows from Article 35(7) GDPR and includes four elements: a systematic description of the planned operations and purposes, an assessment of necessity and proportionality, an assessment of risks to individuals, and the measures planned to address those risks. This means the document cannot be a general project description; it must show *how* a specific AI process works and *why* its scale is justified.
According to AI and privacy materials, a well-prepared DPIA should also describe data sources, data categories, recipients, retention, transfers outside the EEA, and human involvement in decision-making. In the area of mitigating measures sources point to data minimization, access restriction, pseudonymization, error control, human oversight, and a review and versioning plan for the assessment.
A practical starting point is a simple test: does the system process personal data, does it do so at large scale, does it use profiling or automated decisions, and can it concern sensitive data. If several of these factors occur together, according to Dutch and Polish practical materials a DPIA is usually necessary.
It is important that the DPIA covers the entire AI chain, not only the model. Sources on generative AI and agents emphasize that the assessment should include training, inference, event logging, impact on individuals, and secondary risks such as misidentification or excessive disclosure of data. This is particularly relevant when AI supports recruitment, customer service, scoring, medicine, or monitoring, because these processes can easily involve a high level of risk.
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Source: https://www.bartbakowski.com/wiedza/encyklopedia-ai/dpia-ocena-skutkow-dla-ochrony-danych-w-ai