AI-PIA · SaaS & technology
AI Privacy Impact Assessment for AI Startups & LLM App Builders
There is no certification that proves an AI product is trustworthy, so an AI-PIA is what does that work instead: a documented answer to what a specific LLM feature does with personal information, why sending prompts to a US-hosted model is justified, and whether the feature makes a decision about someone that has to be disclosed. The trigger is almost always concrete: a new feature about to ship, a Quebec customer asking the cross-border question directly, or an investor's diligence checklist asking whether this was ever assessed at all.
Reviewed by the Privacy Horizon team · Last reviewed
What you're protecting
What an AI-PIA has to open up for an LLM feature
Not every feature carries the same weight. These are the parts of a feature's design where an unexamined assumption creates the most exposure.
The feature's actual prompt and data flow
Exactly what personal information reaches the model, in what form, and whether that matches what the feature's stated purpose actually requires, traced from the input field to the model API and back.
RAG source documents and their embeddings
Where retrieval-augmented generation is involved, which documents get indexed, whether they contain personal information, and whether the resulting embeddings are treated as personal information once they can be matched back to a person.
Any automated decision the feature makes
Whether the feature approves, scores, ranks or filters something about a person with no meaningful human review, since that determines whether Law 25's disclosure duty applies at all.
Training-data provenance, for model builders
Where a training or fine-tuning dataset came from, whether it includes scraped or third-party content, and whether that provenance can survive the question a customer or journalist will eventually ask.
The cross-border path itself
Which model provider processes the data, where inference actually runs, and what contractual protection, such as zero data retention, governs that transfer once it leaves Canada.
Regulatory map
Why an AI-PIA is the assessment doing certification's job here
No AI-specific statute exists in Canada, so the obligations and the vocabulary buyers expect both come from guidance and provincial law written directly for this category.
Law 25 section 17's cross-border transfer duty
Before personal information is communicated outside Quebec, an assessment of the receiving jurisdiction's protections is required, and sending a prompt to a US-hosted LLM API is precisely the kind of communication section 17 is written to catch.
Law 25 section 12.1 on automated decisions
An individual affected by a decision based exclusively on automated processing must be told, and is entitled on request to an explanation of the principal factors involved, an explanation an AI-PIA is where you work out in advance.
The OPC's generative-AI principles as the assessment structure
Legal authority and consent, necessity and proportionality, openness, accountability and safeguards against prompt injection and model inversion give an AI-PIA a specific structure to work through, rather than a general privacy checklist.
The OpenAI investigation as a preview of regulator questions
The joint OPC investigation into OpenAI, covering consent, openness, access, accuracy and accountability, previews what any Canadian regulator will eventually ask about a comparable LLM product.
What goes wrong
What an unassessed LLM feature actually risks
Each of these connects to a documented regulatory signal or incident pattern in this category, not a hypothetical concern.
An automated decision nobody can explain
Without a documented mapping from inputs to outcome, a genuine section 12.1 request for the principal factors behind a decision becomes a scramble, and a reconstructed answer under deadline pressure is worse than a timely honest one.
A cross-border transfer with no assessment on file
Sending prompts to a US-hosted model without a completed section 17 assessment leaves the transfer unjustified on paper, even if the underlying practice is reasonable, which is exactly the gap a Quebec customer's legal team will ask about.
Bias that surfaces only after a complaint
A model that treats users differently in ways nobody intended is usually discovered through a complaint rather than an internal review, and an AI-PIA's bias and misuse review is built to catch it earlier than that.
Training-data provenance with no good answer
The regulators' joint statement on scraping makes clear that publicly accessible personal data is still protected, and a model builder who cannot explain where training data came from has no good answer once asked directly.
Our ai-pia for ai startups & llm app builders
What the AI-PIA delivers for your product
The assessment follows our standard structure, data-handling review, bias and misuse considerations, regulatory alignment, responsible-use guidance, applied specifically to how your feature actually uses personal information.
Feature-by-feature data handling review
A close look at what personal information each assessed feature uses, whether that use matches its stated purpose, and where the data actually originates, whether typed by a user, retrieved by RAG, or drawn from a training set.
Bias and misuse considerations
A structured review of where a feature's outputs could raise fairness or misuse concerns, with directional guidance on what testing or monitoring would improve confidence going forward.
Automated-decision and cross-border documentation
Working drafts of the section 12.1 disclosure and the section 17 transfer assessment, built from how the feature actually works rather than a generic template filled in after the fact.
Regulatory alignment overview
A broad comparison of current practice against the OPC's generative-AI principles and the frameworks buyers cite, such as the NIST AI RMF and ISO 42001, without asserting or certifying compliance that doesn't exist.
Responsible-use guardrails
High-level principles for how the feature should be developed, monitored and changed going forward, aligned with privacy-first practice rather than left as a one-time sign-off.
A record built to be shown
Documentation structured so it can be handed to an enterprise customer's security team, a Quebec customer's legal counsel, or an investor's diligence process without a rewrite.
How the engagement runs
How we run an AI-PIA on an LLM feature
Scoped to the feature that matters most first, so the highest-stakes part of the product gets documented before anything else.
Step 1
Inventory the features and data flows in scope
We identify which features touch personal information meaningfully, including any that make an automated decision or send data across the border, and prioritize accordingly.
Step 2
Trace the data and the outcome
For the priority feature, we map inputs through to the model's output, documenting the path well enough to explain it to a user, a customer or a regulator.
Step 3
Assess fairness, transparency and transfer justification
We review where outcomes could raise bias or misuse concerns, and where the current disclosure or cross-border assessment, if one exists, falls short of what Law 25 or the OPC's principles expect.
Step 4
Deliver documentation and guardrails
You receive the assessment record, draft disclosure language, and responsible-use guidance your team can act on before the next feature ships or model provider changes.
What it costs
What shapes the price of an AI-PIA for an LLM feature
The main cost drivers are how many features are genuinely in scope, one chatbot versus a chatbot plus an agent with tool access plus a scoring feature, whether any feature makes an automated decision requiring section 12.1 documentation, and how many distinct model providers or cross-border paths need a section 17 assessment. A single well-scoped feature is far more contained than an entire product surface assessed at once.
Tell us which features are live or in development and which involve automated decisions or cross-border inference, and we will scope the assessment against that list and return a fixed price.
AI Startups & LLM App Builders: AI-PIA questions, answered
Not every feature needs the same depth, but any feature that sends personal information to a model, makes a decision about a person, or introduces a new cross-border data path deserves at least a scoped assessment. A low-stakes internal tool needs less than a customer-facing feature that scores or filters applicants.
Yes. Law 25 section 17 requires an assessment of the receiving jurisdiction's protections before personal information is communicated outside Quebec, and inference on a US-hosted model API is exactly that kind of communication. This applies regardless of where your company is headquartered.
If a feature decides something about a person with no meaningful human review, they must be told the decision was automated and, on request, given an explanation of the principal factors and inputs involved. An AI-PIA is where that explanation gets worked out and documented in advance, rather than improvised after a complaint.
Directional testing still counts: reviewing outputs across different user groups or scenarios for patterns worth flagging, documenting what was tested and what its limits are, and being honest about what wasn't tested. A structured, documented review with known gaps is far more credible than a polished summary implying certainty that doesn't exist.
You need to revisit the assessment, though the depth depends on what changed. Moving from one US-hosted provider to another with similar terms is a smaller update; moving to a provider with different retention, training-use or hosting-location terms changes the cross-border and safeguards analysis enough to warrant a fuller review.
If an embedding can reasonably be matched back to the source document or the individual it describes, it's assessed the same way the original data would be, not treated as automatically de-identified because it looks like a vector of numbers. The assessment documents that reasoning explicitly rather than assuming it away.
SOC 2 tests whether your security controls operate as documented; it says nothing about whether a feature's data use is justified, whether an automated decision is properly disclosed, or whether outputs raise fairness concerns. The AI-PIA is where those specifically AI-shaped questions get answered, which is why buyers increasingly ask for both.
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- When do you need an AI Privacy Impact Assessment (AI-PIA)?
- What's involved in a Privacy Impact Assessment: inputs, timeline, and cost?
- PIA vs TRA: which assessment do you need (or do you need both)?
- How do you assess the privacy and security risk of an AI vendor?
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