AI Privacy Risk Assessment (AIPRA) Tool

Welcome to the AI Privacy Risk Assessment Tool (AIPRA Tool)!

The is designed to help organisations evaluate the privacy risks associated with their AI systems. With the convergence of artificial intelligence (AI), new challenges and opportunities emerge in safeguarding personal data. This tool guides you through a series of questions to assess your system’s privacy governance, data management practices, and privacy-preserving techniques.

By using this tool, you can identify potential privacy risks, evaluate the effectiveness of implemented controls, and ensure compliance with data privacy regulations. The tool is structured to support organisations at various stages of development, from fully implemented systems to those still in early stages. It provides a clear risk score to help inform decision-making and prioritise areas that require attention for improved data privacy and security.

Please note: This tool is in its initial development phase and serves as a prototype for guiding privacy risk awareness. Results and recommendations should be interpreted as general guidance, not definitive legal or compliance assessments. Further refinements are ongoing.

What type of system are you assessing?

Governance & Risk Management

How does the governance structure address the integration of data privacy concerns in AI systems?

A

High: Regularly (e.g., quarterly or per release).

B

Moderate: Occasionally (Ad hoc or only for major changes).

C

Low: Rarely or no formal review conducted.

How regularly is your system’s privacy risk assessed or reviewed?

A

High: Clear attribution and understanding of human-AI roles, reducing ambiguity.

B

Moderate: Some attribution clarity, but roles may need further clarification.

C

Low: Ambiguous attribution, leading to uncertainty about human-AI contributions.

Privacy Risk Assessment

Have you identified all personal and sensitive data collected by the system?

A

High: Fully mapped and documented all types of data.

B

Moderate: Some data identified, others are unclear.

C

Low: No formal data mapping.

Have you assessed re-identification, aggregation, or inference risks?

A

High: Actively monitored and managed risks.

B

Moderate: Considered in part, but not consistently.

C

Low: Not yet assessed or managed.

Privacy Controls Implementation

Are privacy-preserving techniques (e.g., differential privacy, encryption, federated learning) implemented?

A

High: Fully implemented and maintained.

B

Moderate: Some techniques in place, but not consistently.

C

Low: No techniques implemented.

Are data minimisation and purpose limitation enforced?

A

High: Strictly enforced with only necessary data collected

B

Moderate: Principles applied inconsistently.

C

Low: Little attention to minimisation.

User-Centric Privacy Considerations

Are users informed about data collection and use (transparency)?

A

High: Clear, easy-to-understand privacy notices available.

B

Moderate: Vague or difficult-to-access information.

C

Low: No clear communication on data use.

Can users control their data (e.g., consent, access, deletion)?

A

High: Self-service tools or clear, accessible processes in place.

B

Moderate: Limited options for users to control their data.

C

Low: No control mechanisms available to users.

Continuous Monitoring & Improvement

How frequently is the system updated to reflect new privacy risks or regulations?

A

High: Regularly, with processes in place to adapt to changes.

B

Moderate: Occasionally, addressed reactively.

C

Low: Rarely, with minimal updates.

Are privacy audits or third-party reviews conducted?

A

High: Regular, documented audits and external reviews.

B

Moderate: Some informal reviews conducted.

C

Low: No audits or external reviews.