Data privacy impact assessments (DPIA) are formal processes used to identify, evaluate, and mitigate privacy risks in projects that involve processing personal data. A DPIA is required when that processing is likely to result in a high risk to individuals’ rights and freedoms, especially in complex systems such as AI.
This matters because AI systems often handle large amounts of sensitive data—from biometrics to behavioral patterns. Without a DPIA, organizations may overlook potential harm, violate data protection laws, or expose individuals to risk. For AI governance and compliance teams, DPIAs are essential tools to ensure accountability, transparency, and lawful use of data, especially under the GDPR and standards like ISO/IEC 42001.
“Only 33% of companies using AI in Europe conduct DPIAs for projects that involve personal data.”
(Source: European Data Protection Board, 2023)
When a DPIA is required for AI systems
Not every AI system needs a DPIA. However, certain types of processing make it mandatory under GDPR Article 35. These include:
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Automated decision-making that has legal or similarly significant effects.
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Large-scale processing of sensitive categories of data.
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Systematic monitoring of publicly accessible areas (e.g., via facial recognition).
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New technologies where privacy risks are not yet well understood.
AI systems often fall into at least one of these categories. A DPIA helps teams assess those risks before the system goes live, not after damage has been done.
Common risks uncovered during a DPIA
A DPIA reveals much more than technical bugs. It focuses on how people could be harmed by the way their data is used.
Typical risks identified include:
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Lack of transparency: Individuals don’t know how their data is being used or cannot contest outcomes.
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Data minimization failure: More data than necessary is collected or retained.
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Bias and discrimination: Inferred characteristics lead to unfair treatment.
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Security vulnerabilities: Weak safeguards lead to unauthorized access or leaks.
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Function creep: Data is reused for purposes beyond the original intent.
Each of these issues can expose an organization to fines, lawsuits, or loss of public trust.
Real-world example
A city government introduced a smart traffic control system using license plate recognition. A DPIA found that drivers were being tracked without a clear legal basis, and the vendor had access to identifiable data without contractual limits. As a result, the city had to redesign the system to anonymize data before storage and restrict third-party access.
In another case, a fitness app using AI for personalized recommendations conducted a DPIA and identified that health-related data was being shared with advertisers. The team stopped this practice before launch and updated consent mechanisms to reflect the actual data flow.
Best practices for conducting DPIAs in AI projects
A DPIA should not be a one-time document but a living assessment that updates as the system changes.
Best practices include:
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Start early: Begin the DPIA during the design phase, not after launch.
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Include diverse stakeholders: Legal, technical, ethics, and product teams should contribute.
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Map data flows: Track data from collection to deletion, including third-party transfers.
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Evaluate alternatives: Look for less risky ways to achieve the same goal.
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Document decisions: Record what risks were found and how they were handled.
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Revisit regularly: Update the DPIA when the model is retrained or new features are added.
Templates and guidance can be found via the UK Information Commissioner’s Office and EDPB guidelines.
FAQ
Is a DPIA only required under GDPR?
No. Many jurisdictions are adopting similar requirements. Canada’s proposed CPPA and Brazil’s LGPD both encourage DPIAs for high-risk data use, especially when AI is involved.
Can a DPIA block a project from going live?
Yes, if risks are unacceptably high and cannot be mitigated. In some cases, the data protection authority must be consulted before proceeding.
Who should write a DPIA?
Ideally, the data controller is responsible, with help from privacy officers, security experts, and project leads. In large companies, a Data Protection Officer (DPO) often manages the process.
How long does a DPIA take?
That depends on the project’s complexity. For most AI projects, expect 1-3 weeks, including stakeholder input and review cycles.
Summary
DPIAs help organizations identify and reduce privacy risks before they become legal or ethical problems. For AI systems that process personal data, a DPIA is often not just good practice but a legal requirement.
Teams that build privacy reviews into their development cycle are better prepared to meet regulations, avoid harm, and build systems people can trust.
