AI-PIA · SaaS & technology
AI Privacy Impact Assessment for Martech & Adtech Platforms
An AI-PIA examines the models actually doing the targeting work in your product: lookalike modelling, personalization engines, automated bid optimization, and what each one infers about a person along the way. The central question is usually the same one Quebec's Law 25 asks directly: does this function identify, locate or profile a person, and if so, is it shipping opt-in the way section 8.1 requires. Teams commission one before launching a new modelling feature, or when a customer's compliance team asks how the personalization engine actually works.
Reviewed by the Privacy Horizon team · Last reviewed
What you're protecting
What the assessment actually examines
The review follows the data into the model and back out again, since risk hides in both directions.
Seed audiences and training inputs
What personal information feeds a lookalike or personalization model, and whether the consent basis behind that seed data covers being used to train or steer an algorithm, not just to build a static list.
Inferred and derived attributes
The OPC has treated inferred characteristics as personal information in practice, so a model that generates new sensitive-adjacent attributes about a person carries the same weight as collecting them directly.
Automated bid and optimization logic
Whether the signals an optimization engine weighs, such as inferred intent or behavioural patterns, cross into categories the OPC excludes from opt-out-based targeting.
Default state of profiling functions
Whether a personalization or targeting feature is switched on automatically or requires a person to opt in, the specific question Law 25's section 8.1 puts to every product decision.
Regulatory map
The regulatory questions this assessment answers
AI targeting features sit exactly where privacy law's newest and oldest requirements meet.
Law 25 section 8.1's profiling test
Any function that identifies, locates or profiles a person must be inactive by default, and a privacy impact assessment is required before it goes live for Quebec users.
The OPC's treatment of inferences as personal information
Findings against Home Depot and Tim Hortons both treated derived and behavioural data as personal information, a precedent that extends directly to what an AI model infers about someone.
Age assurance and youth-profiling risk
A recent joint finding on ad profiling of underage users highlighted transparency and age-assurance gaps, a directly relevant risk for any personalization engine that does not verify or account for age.
EU AI Act relevance for EU-facing products
Platforms with EU inventory or personalization touching EU users may fall under the EU AI Act's obligations for certain automated decision-making, a narrower but real consideration for internationally operating platforms.
What goes wrong
What an unreviewed AI feature actually risks
These are not abstract AI-ethics concerns; each maps to a specific published finding or statutory test in this space.
A profiling feature shipped on by default
A personalization or lookalike feature launches active for all markets because nobody flagged that Quebec users require the opposite starting state under section 8.1.
A lookalike model built on thin consent
Seed audiences collected for one stated purpose get repurposed to train a modelling feature nobody told the original data subjects about.
Sensitive inferences generated without safeguards
An optimization engine learns to weigh signals that amount to health, financial or other sensitive categories, even though no one explicitly fed it that data as an input.
Youth-directed profiling without age assurance
A personalization engine treats all users identically regardless of age, echoing the exact gap a recent joint regulatory finding flagged around underage ad profiling.
Our ai-pia for martech & adtech platforms
What our AI-PIA covers for a martech or adtech product
A concrete review of the AI-driven features actually shaping targeting decisions, not a general AI-governance exercise.
AI inventory and data-flow mapping
Every lookalike, personalization or bid-optimization feature, mapped to the personal information it consumes, generates and retains.
Data handling review
How each model uses inputs, produces inferences, and shares outputs with downstream partners or bid-stream integrations.
Bias and misuse considerations
Directional review of where targeting outcomes could disadvantage or exclude groups unfairly, with recommendations for improving transparency and oversight.
Regulatory alignment overview
A comparison of the feature against Law 25's profiling test and the OPC's principles on inferred data, mapping where the product stands today.
Default-state and disclosure recommendations
The factual basis for setting the right opt-in or opt-out default and for writing an accurate consent notice, so what ships matches what is disclosed.
How the engagement runs
How the assessment runs
Working through the people who built the model and the systems it touches.
Step 1
Discovery workshop
Sit with product and data-science teams to catalogue every lookalike, personalization or optimization feature in production or development.
Step 2
Model and vendor interrogation
Targeted questions on training data, inference behaviour, retention and any third-party model providers, moving past marketing claims to contractual and technical answers.
Step 3
Analysis and findings
Risks ranked with recommended actions, settings to change, defaults to flip, disclosures to publish, delivered in language product and legal teams can both use.
Step 4
Remediation and re-check
Support implementing priority changes, then a follow-up review as models retrain or new features launch.
What it costs
AI-PIA cost drivers for martech and adtech products
Scope tracks the number of AI-driven features in the product, the depth of technical documentation and vendor cooperation available, whether Quebec traffic brings section 8.1 analysis into play, and how much default-state and disclosure recommendation work is included. Assessing a single lookalike-audience feature is a short engagement; a personalization engine with multiple models feeding a bid stream is substantially larger.
Assessing before a new modelling feature launches is cheaper than assessing after rollout, and gives your team leverage to fix defaults before customers or regulators ever see them. Share your feature list and we will quote a fixed fee.
Martech & Adtech Platforms: AI-PIA questions, answered
Generally yes, if the model uses personal information to build or expand an audience. The assessment checks whether the seed data's original consent basis covers being used to train a modelling feature, and whether the resulting audience carries any sensitive inferences that would require a higher consent standard than the campaign currently applies.
Start by mapping what signals feed the engine and what it infers in return, since the inferred output often carries more privacy weight than the raw inputs. From there, check the engine's default state against Law 25's profiling requirement and compare its data handling against the OPC's principles on sensitive categories and reasonable expectations.
It needs to be, for any function that identifies, locates or profiles a Quebec user. That means the feature ships inactive and the user takes an affirmative action to turn it on, not a passive failure to opt out. An AI-PIA is the mechanism for confirming whether your current implementation actually meets that bar or only claims to in policy language.
In practice, yes. Findings against Home Depot and Tim Hortons both treated derived and behavioural data, not just raw records, as personal information subject to consent requirements. An AI model that infers new attributes about a person, rather than simply collecting them, falls under the same expectation.
A standard PIA maps data flows and risks for a system generally. An AI-PIA adds specific attention to model behaviour: what the model infers beyond its stated inputs, whether those inferences could be discriminatory or unexpected, and whether the feature's automated decision-making meets the higher scrutiny regulators and platform partners increasingly apply to AI-driven targeting.
Yes, since you remain accountable for personal information processed on your behalf even when a vendor's model is doing the work. The assessment includes questions to the vendor on training data, retention and explainability, so you can document what the tool actually does rather than relying on its marketing description.
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