The Compliance Window Is Closing
Canadian organizations that deployed AI systems in 2023 or 2024 under the assumption that privacy law would take time to catch up are running out of runway. Quebec's Law 25 is in full effect. The federal Artificial Intelligence and Data Act has been in legislative process. And the Office of the Privacy Commissioner has published guidance making clear that PIPEDA's existing requirements apply to AI systems — without waiting for new legislation.
The practical implication is this: if your organization has AI systems processing personal information, you are already subject to privacy law requirements. Whether your current implementation meets those requirements is a question worth answering before a regulator or a breach does it for you.
This guide is a practical overview of the Canadian privacy landscape as it applies to AI, with specific guidance on what that means for system design and governance. It is not legal advice — for specific compliance questions, you need qualified legal counsel — but it should help technical and operational leaders understand what the legal requirements actually require them to build.
PIPEDA: The Baseline That Already Applies
The Personal Information Protection and Electronic Documents Act (PIPEDA) applies to most private-sector organizations across Canada for commercial activities involving personal information. If you are using AI to process information about employees, customers, or members of the public, PIPEDA applies.
The key PIPEDA principles that create specific design requirements for AI systems are:
Accountability. Your organization is responsible for personal information under your control, regardless of whether it is processed by an AI system, a third-party vendor, or internal staff. If you use a cloud AI service that processes personal data, you are accountable for how that data is handled. This means your vendor contracts need explicit privacy protections, and "the vendor is responsible" is not a sufficient answer.
Identifying purposes. You must identify the purposes for which you are collecting and using personal information at or before the time of collection. For AI systems, this creates a specificity requirement: "improving our services" is not an adequate stated purpose for training an AI model on customer interaction data. The purpose must be specific enough that individuals can meaningfully understand what they are consenting to.
Consent. Consent must be meaningful. For AI systems that make or influence significant decisions about individuals — credit approvals, hiring assessments, insurance underwriting, medical triage — this raises questions about whether standard terms-of-service consent is adequate. The OPC's guidance on consent in the context of AI is increasingly explicit that meaningful consent requires individuals to understand that AI is involved and how it influences decisions about them.
Accuracy. AI systems that make decisions based on personal data must be designed with accuracy as a design requirement. An AI system that makes systematically biased decisions — for example, a hiring algorithm that disadvantages candidates from certain regions — creates PIPEDA accuracy problems, not just ethical ones.
Safeguards. Technical and organizational safeguards must be appropriate to the sensitivity of the information. For AI systems, this includes data minimization (don't use more personal data than the AI needs), access controls, audit logging, and procedures for identifying and responding to model drift or unexpected outputs.
Openness and individual access. Individuals have the right to know what personal information your organization holds about them and how it is being used. For AI systems, this creates transparency requirements: if an AI system made a decision about an individual, that individual should be able to find out that AI was used and, in general terms, how.
Quebec's Law 25: The Stricter Regime
Quebec's Act respecting the protection of personal information in the private sector — commonly referred to as Law 25 or Bill 64 — came into full effect in September 2023 and applies to any organization that collects, holds, uses, or communicates personal information about Quebec residents, regardless of where the organization is located.
Law 25 is more prescriptive than PIPEDA in several areas that are directly relevant to AI:
Privacy impact assessments (PIAs). Law 25 requires a PIA before any project involving the collection, use, or communication of personal information. This is not optional. If you are deploying an AI system that will process personal information about Quebec residents, you need a documented PIA — not a cursory checkbox exercise, but a structured assessment of the risks and the safeguards implemented to address them.
Automated decision-making disclosure. Law 25 explicitly requires organizations to disclose when a decision based exclusively on automated processing has been made about an individual and to give them an opportunity to present observations and have the decision reviewed by a human. This provision applies directly to AI systems making consequential decisions, and "exclusively automated" should be interpreted conservatively — a human who rubber-stamps AI output without meaningful review does not satisfy the requirement.
Profiling disclosure. If your AI system creates profiles of individuals — combining data from multiple sources to build a model of individual behaviour or characteristics — you must disclose this to individuals and give them the ability to opt out.
Data minimization and storage limitation. Law 25 strengthens requirements around keeping only the data necessary for the stated purpose and deleting it when it is no longer needed. For AI systems trained on historical data, this creates ongoing governance requirements around training data retention.
The enforcement authority for Law 25 is the Commission d'accès à l'information (CAI), which can impose penalties of up to $25 million or 4 percent of worldwide turnover — whichever is higher — for serious violations.
The Artificial Intelligence and Data Act: What to Expect
The Artificial Intelligence and Data Act (AIDA), introduced as part of Bill C-27, represents Canada's first attempt at AI-specific federal legislation. As of early 2026, its path through Parliament has been non-linear, and the final form of any enacted legislation may differ from what has been proposed. However, the direction of travel is clear enough that organizations should be designing AI systems with the AIDA framework in mind.
The core AIDA concepts that matter for system design:
High-impact AI systems. AIDA distinguishes between AI systems in general and "high-impact" AI systems that pose greater risks. The factors that make a system high-impact include: the nature of the decisions or recommendations it makes, the scale of deployment, the sensitivity of the data involved, and the consequences for individuals if the system produces incorrect or biased outputs. Any AI system that influences significant decisions about individuals in regulated sectors — healthcare, finance, employment, government services — is likely to qualify.
Mitigation measures. Organizations deploying high-impact AI systems will be required to identify and implement measures to mitigate risks of harm, including algorithmic bias. This is not a vague aspiration — it is a documented requirement. Organizations will need to demonstrate that they assessed the risks, identified appropriate mitigations, and implemented them.
Transparency requirements. High-impact AI systems will need to be disclosed to affected individuals. The form of disclosure required is still being worked out in the regulatory process, but organizations should be building disclosure mechanisms into AI system design now rather than retrofitting them later.
Incident reporting. AIDA as proposed includes requirements to report incidents involving high-impact AI systems to the AI and Data Commissioner — a new regulatory position being created. The reporting threshold and process are still being defined, but the organizational capability to detect, document, and report AI system incidents needs to be built.
What to Build Into AI Systems Today
The regulatory framework is still evolving, but the direction is consistent enough that organizations can design AI systems today that will be compliant across the expected final landscape. The following are the non-negotiable design requirements.
Document your legal basis before deployment. For every AI system that processes personal information, you need a documented legal basis — consent, legitimate interest, or another applicable basis — that is specific enough to withstand regulatory scrutiny. "We believe users understand their data is being used to improve services" is not a legal basis. This documentation needs to be created before deployment, not reconstructed after a complaint.
Build PIAs into the deployment process. Privacy impact assessments should be a standard step in AI deployment, not an optional add-on. The PIA process forces the questions that catch problems early: What data are we collecting? Why? What are the risks? How are we mitigating them? Who is accountable? Organizations that have standardized PIAs report significantly fewer compliance surprises than those that treat privacy as a legal review at the end of development.
Design for transparency from the start. Build the capability to disclose AI involvement in decisions before regulators require it. This includes: disclosure language for affected individuals, access mechanisms for individuals who want to know what data you hold about them, and human review workflows for automated decisions affecting individuals significantly. Retrofitting transparency onto deployed systems is expensive and technically difficult; designing it in is not.
Implement data minimization actively. AI systems have an appetite for data. The instinct of AI teams is to collect and retain as much data as possible for training and improvement. Privacy law requires the opposite: collect only what is necessary, retain only as long as needed, and delete when the purpose is served. This creates operational tension that needs to be resolved at the governance level, not left to individual technical teams to manage.
Establish AI governance with real authority. Privacy compliance for AI is not a legal department responsibility or a technology team responsibility — it requires coordination across both, plus operations, risk, and executive leadership. Organizations that have established an AI governance function with clear ownership, defined processes, and real authority to require changes to AI systems are the ones getting ahead of compliance requirements rather than reacting to them.
The Sectors With the Most Exposure
Organizations in government, healthcare, finance, and legal services have the highest compliance exposure because they combine sensitive personal data, high-stakes decisions, and regulatory environments that layer sector-specific requirements on top of the general privacy framework.
Healthcare organizations face PIPEDA/provincial health privacy legislation plus the professional and regulatory standards of health professions. AI systems that influence clinical decisions, process patient data, or interact with patients must be designed for a compliance environment that is more demanding than any other sector.
Financial services organizations face PIPEDA plus OSFI guidance on technology and cybersecurity risk, plus sectoral requirements under FINTRAC and banking legislation. AI systems used for credit decisions, fraud detection, and customer risk assessment are high-impact by any definition.
Government organizations have distinct frameworks depending on jurisdiction — the Privacy Act for federal institutions, provincial privacy legislation elsewhere — and the consequences of non-compliance carry reputational and political dimensions beyond the regulatory penalties.
Legal services organizations handle privileged communications and highly sensitive personal and commercial information. AI systems used in legal practice must be designed with solicitor-client privilege implications clearly understood — including how data shared with AI vendors affects privilege.
The Cost of Getting It Wrong
The regulatory consequences of non-compliance are real and growing. The CAI in Quebec has demonstrated willingness to investigate and impose penalties. The OPC has been increasingly assertive. And the financial penalties under Law 25 in particular — up to $25 million — are not theoretical.
Beyond regulatory penalties, AI privacy failures create client relationship damage, reputational harm, and, in some sectors, exposure to civil liability. The organizations treating privacy compliance as a genuine design requirement rather than a legal checkbox are not just managing risk — they are building AI systems that work better, because systems designed with data minimization and transparency requirements tend to be more focused and more trustworthy than systems built without those constraints.
Remolda works with organizations across regulated sectors to design AI systems that are built for Canadian compliance requirements from the ground up. If you are evaluating your current AI deployments against the regulatory landscape, we are happy to discuss where the gaps typically lie.