When Should an AI Assessment Be Part of Your PIA, and When Should It Stand Alone?
Privacy impact assessments were not designed with AI in mind. Most PIA frameworks were built around data flows, consent, and the handling of personal information. They are good tools for asking whether a system collects the right data, retains it appropriately, and protects it adequately. What they were not designed to assess is whether a system is fair, explainable, or likely to produce discriminatory outcomes.
As AI becomes a standard component of how organizations operate, the question of how to assess it properly has become more pressing. The answer is not the same for every system. Some AI tools sit comfortably inside a PIA. Others require a separate assessment entirely. Getting this wrong in either direction creates problems: underestimating the risks of a consequential AI system, or burdening low-risk tools with governance processes that do not fit them.
The distinction comes down to one question: is privacy the primary risk this AI system creates, or does it introduce risks that go beyond privacy?
When to Integrate the AI Assessment Into Your PIA
For some AI systems, privacy is the main risk domain and the AI is not making or materially influencing decisions about people. The questions worth asking are largely the ones a PIA is already designed to address: what data is collected, where it goes, who can access it, how long it is kept, and whether the people whose data is used have meaningful awareness of that use. Adding a dedicated AI section to the existing PIA is the appropriate approach. It avoids duplicating effort, keeps the assessment proportionate to the actual risk, and is manageable for smaller project teams or organizations with limited resources.
This approach is particularly well suited to tools where the AI function is assistive rather than determinative. Common examples include AI-assisted document summarization, meeting transcription tools, internal search and chatbot systems, email drafting tools, and knowledge management copilots. These tools may handle personal information, but their AI function does not determine outcomes for individuals.
What to Add to the PIA
The following are additional sections a PIA should include if the AI assessment is incorporated into the PIA:
Purpose and Intended Use
What is the AI system designed to do, and what uses are explicitly out of scope? This matters because AI tools are often deployed beyond their original purpose over time.
Data Inputs and Outputs
What personal information does the system process, in what form, and what does it produce? AI systems can generate new inferences or outputs that were not present in the original data, and those outputs need to be assessed.
Human Oversight
What role does a human play in reviewing, approving, or acting on the system's outputs? A tool that makes suggestions a human reviews is different from one whose outputs are acted on automatically.
Accuracy Limitations
What are the known error rates or failure modes of the system, and how are they disclosed to users? AI systems produce incorrect outputs, and the PIA should address how that risk is managed.
Data Retention
How long is personal information retained by the system, and by any third-party vendors involved in processing? This includes training data, query logs, and outputs.
Vendor Governance
If the AI system is provided by a third party, what contractual commitments exist around data use, retention, sub-processing, and security? Many AI vendors use customer data for model training unless contractually restricted from doing so. Completing vendor due diligence assessments are highly recommended for identifying vendor related risks.
Training Data Considerations
What data was used to train the model, and does that data raise any privacy concerns relevant to current use? This is particularly important where training data may include personal information from a different context.
Monitoring and Review
How will the organization monitor the system's performance and privacy compliance over time, and at what intervals will the assessment be revisited?
When to Conduct a Separate AI Impact Assessment
A separate AI Impact Assessment is warranted when the AI system could materially affect people, their rights, or significant organizational outcomes. Privacy is one risk category among several in these situations, and a PIA alone is not structured to address the others.
Examples include employee monitoring tools, student assessment systems, hiring and recruitment platforms, eligibility determination tools, law enforcement applications, predictive analytics that affect service delivery, risk scoring systems, and public-facing generative AI systems.
What the AI Impact Assessment Should Examine
Fairness and Bias
Does the system produce systematically different outcomes for different demographic groups? Has it been tested for disparate impact?
Explainability
Can the system's outputs be explained to the people affected by them, and to the organization deploying it? Black-box outputs are difficult to challenge and difficult to defend.
Transparency
Do affected individuals know that an AI system is involved in decisions about them? Transparency obligations vary by jurisdiction but are increasingly expected.
Human Oversight
Is there a meaningful human review process, or does the system function autonomously in practice? Meaningful oversight requires that the reviewer has the information, time, and authority to override the system.
Contestability
Can individuals challenge decisions made by or through the AI system? This is increasingly a legal requirement in some jurisdictions and a basic fairness expectation in most.
Accuracy and Performance
How accurate is the system, across which populations, and under what conditions? A system that performs adequately on average may perform poorly for specific groups.
Model Drift
How will the organization detect and respond to changes in the system's performance over time? Models trained on historical data can become less accurate as circumstances change.
Security
What are the risks of adversarial attack, manipulation, or unauthorized access to the model or its outputs?
Accountability
Who in the organization is responsible for the system's outcomes, and what happens when something goes wrong?
Legal Compliance
Does the system create obligations or risks under employment law, human rights legislation, sector-specific regulation, or emerging AI governance frameworks?
Reputational Impacts
What would the consequences be if the system's outputs became public, were challenged, or attracted media attention?
Vulnerable Populations
Does the system interact with or make decisions about children, people with disabilities, or other groups that warrant additional protections?
How the PIA and AI Assessment Work Together
The two assessments are complementary and should be conducted together where possible, with shared findings and a unified governance record. The goal is not to create two separate bureaucratic processes but to ensure that neither privacy nor the broader AI risks are missed because the other assessment was assumed to cover them.
A Practical Way to Decide
Before beginning an assessment, ask whether the AI system makes or materially influences decisions about people. If the answer is no, integrate the AI assessment into the PIA. If the answer is yes, a standalone AI Impact Assessment is warranted alongside the PIA.
When in doubt, the more consequential path is the right one to take. The cost of over-assessing a low-risk tool is administrative effort. The cost of under-assessing a high-risk one is organizational, legal, and reputational exposure that is considerably harder to address after the fact.
The Practical Questions to Ask Now
If your organization is deploying or planning to deploy AI systems, ask whether you can answer the following:
Do you have a process for determining whether an AI system warrants a standalone assessment or an integrated one?
For AI tools currently in use, have you completed either a PIA with AI sections or a separate AI Impact Assessment, as appropriate?
Do your PIA templates include dedicated sections covering purpose and use, human oversight, accuracy limitations, vendor governance, and training data?
For consequential AI systems, have you assessed fairness, explainability, contestability, and human oversight, not just privacy?
Are your vendor agreements for AI tools clear on data use, model training, and retention?
Is there a scheduled review cycle for AI assessments, given that models change and organizational use evolves?
If the answer to any of these is uncertain, that is where to start.
If your organization is working through how to assess AI systems, build AI governance frameworks, or update existing privacy programs to address AI risk, we can help. Contact us to start the conversation.