Data protection impact assessment
A data protection impact assessment is a structured evaluation of how a system or data-processing activity may affect individuals’ privacy and data protection, and of measures to address identified risks.
Delivering Machine Learning Projects: From design to deployment
A data protection impact assessment is a structured assessment of how planned personal-data processing may affect individuals and the measures needed to address those impacts.
- How it is used
- Documents potential individual impacts of proposed data use during pre-project ethical and regulatory review.
- How deep it goes
- Works through assessment questions and an initial customer-data risk review, but does not provide a complete DPIA template.
- Examples
- Uses customer churn scoring to examine novel processing, consent questions, and possible financial loss from selective offers.
- What you can do after
- Practice/perform an initial assessment of proposed data use and document questions and risks affecting individuals.
First-Party Data Activation
A formal assessment of processing activities that identifies and mitigates risks to individuals’ personal-data protection.
- How it is used
- Uses structured assessment to govern adoption of privacy-centric marketing technologies and reduce risks to individuals.
- How deep it goes
- Defines risk identification, mitigation, vendor criteria, and monitoring, but does not provide a detailed assessment procedure.
- Examples
- Uses adoption of new marketing technologies and vendor evaluation as the central organizational example.
- What you can do after
- Evaluate and implement new marketing technologies with documented privacy-risk mitigation and cross-functional oversight.
Read how First-Party Data Activation explains data protection impact assessment →
Introduction to Generative AI, Second Edition
An AI privacy impact assessment is a structured evaluation of how an AI system may affect personal privacy and of the measures available to mitigate those effects.
- How it is used
- Deploys privacy assessment as an AI governance mechanism across collection, curation, training, and output risks.
- How deep it goes
- Names the assessment and its governance role but does not provide a procedure, checklist, or implementation detail.
- Examples
- Uses consent, provenance, biased curation, transformed training data, private outputs, and risk scores as assessment concerns.
- What you can do after
- Evaluate privacy risks and guide mitigation through collaboration among technical, legal, policy, ethics, and affected-community stakeholders.
Read how Introduction to Generative AI, Second Edition explains data protection impact assessment →
Related concepts
- GDPRused by · The General Data Protection Regulation (GDPR) is a European Union regulation governing the processing of personal data and protecting individuals’ rights concerning that data.
- data governancepart of · Data governance is the organizational framework of policies, roles, responsibilities, processes, and controls that directs how data is managed, accessed, protected, and used.
- personal datauses · Personal data is information relating to an identified or identifiable person, directly or indirectly, alone or in combination with other information.
- impact assessmentproduced by · An impact assessment is a systematic evaluation of a system’s potential effects, harms, or risks for affected people or society.
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