The Complete Guide to Using AI as a Legal Professional in Canada in 2025

Canada's AI rules for lawyers landed squarely in the national spotlight in 2025: Bill C‑27 - the omnibus bill that contained the proposed Artificial Intelligence and Data Act (AIDA) - died on the Order Paper when Parliament was prorogued on January 6, 2025, leaving firms and regulators to rely on the Government's Government of Canada AIDA companion document and a patchwork of sector guidance while debate continues; a clear reminder that “high‑impact” systems such as hiring or screening tools and biometric ID (explicitly flagged in the companion paper) can carry real legal and ethical stakes for practitioners.

Track the Bill's progress with the Bill C‑27 timeline of developments (Gowling WLG), and consider pragmatic upskilling - for example, the 15‑week AI Essentials for Work bootcamp from Nucamp teaches prompt design, tool use and workplace risk checks to help legal teams deploy AI responsibly: AI Essentials for Work syllabus - Nucamp.

Attribute	Details
Program	AI Essentials for Work
Length	15 Weeks
Courses	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost	$3,582 early bird / $3,942 regular (18 monthly payments)
Register	Register for AI Essentials for Work - Nucamp registration

NOT LEGAL ADVICE. Information made available on this website in any form is for information purposes only. It is not, and should not be taken as, legal advice.

AI matters to Canadian legal professionals because it is already reshaping everyday work - from speeding legal research and citation‑backed drafting to automating e‑discovery and contract review - and the market data shows the shift is well underway: a LexisNexis Canada survey found 93% of lawyers are aware of generative AI and more than half have used it, while sector studies warn business adoption must accelerate if Canada is to stay competitive.

Purpose‑built platforms tailored to Canadian law are moving the needle in practice (Lexis+ AI touts bilingual support, jurisdictional content and faster research), and firms that run focused pilots and align tools with their business case will capture the gains without sacrificing ethics or client confidentiality - exactly the pragmatic approach advocated in emerging guidance on implementing new tech for legal teams.

Practical use cases include fast, defensible e‑discovery, document summarization and redlining, predictive analytics for litigation strategy, and DMS integration that surfaces matter‑specific prompts; the payoff is concrete: imagine turning a 100‑page agreement into a concise, citation‑backed memo in minutes, freeing lawyers to do higher‑value strategy and client work.

Learn more about jurisdiction‑aware legal AI from LexisNexis Canada jurisdiction-aware legal AI solutions and practical implementation tips from Torys LLP legal technology implementation guidance.

Canadian legal professionals have been on the leading edge of adopting generative artificial intelligence (Gen AI) technology.

The Canadian regulatory landscape for AI in 2025 is best described as a high-stakes pause: after Parliament was prorogued and Bill C‑27 (which contained the proposed Artificial Intelligence and Data Act) died on the Order Paper, firms, regulators and courts have been left to navigate a patchwork of guidance rather than a single federal statute - practitioners should keep the Government's AIDA companion document on risk‑based AI governance (Innovation, Science and Economic Development Canada) close at hand because it lays out the risk‑based architecture that would have governed “high‑impact” systems, human oversight, transparency and accountability; track the bill's rocky progress with a clear timeline like the Bill C‑27 timeline of developments (Gowling WLG legal timeline), and watch commentary such as the White & Case Canada AI regulatory tracker and analysis for how Ottawa's plans (including the new AI and Data Commissioner model and the Canadian Artificial Intelligence Safety Institute) aim to align with the EU, OECD and other international frameworks; the practical takeaway for legal teams is immediate: prepare for a risk-based rulebook that focuses on “high‑impact” uses (screening, biometrics, health and law‑enforcement tools), expect phased education‑first enforcement, and treat transparency, recordkeeping and accountability frameworks as the default controls now - because until a statute lands, those controls are the closest thing to legal cover when a client's AI system touches someone's rights or livelihood.

Item	Detail
Bill status	Died on the Order Paper when Parliament was prorogued (Jan 6, 2025)
Regulatory approach	Risk‑based framework targeting "high‑impact" AI systems; human oversight, transparency, accountability
Key institutions	Minister of Innovation & new AI and Data Commissioner; CAISI and international alignment cited in companion materials

Bill C‑27 died on the Order Paper.

Canadian guidance leans heavily on a risk‑based playbook: legal teams should start by asking which uses are “high‑impact” (think screening for jobs or services, biometric ID, health‑ or safety‑critical systems) and then match obligations to where risk can be introduced in the lifecycle, because under the proposed AIDA approach the same system can trigger different duties for designers, deployers and operators.

That means lawyers must help clients map AI assets, classify uses against the AIDA factors (severity of harm, scale of use, opt‑out feasibility, and whether harms fall on vulnerable groups), and document governance that demonstrates human oversight, transparency, bias mitigation, monitoring and proportional accountability - the very building blocks the Government set out in its AIDA companion document for identifying “high‑impact” systems and tailoring obligations by activity.

Practically, this looks like checklists for design (data provenance, interpretability), development (validation, documentation), deployment (user disclosures, limits on use), and operations (logging, ongoing monitoring and notification if material harm is likely), so a single screening tool that touches thousands of applicants overnight is treated as a systems‑level risk rather than an isolated contract issue.

For a clear exposition of the risk factors and lifecycle obligations, see the Government's AIDA companion document and a legal overview of how Canada's patchwork framework compares to international regimes.

Regulated activity	Example measures to assess and mitigate risk
System design	Initial risk assessment; dataset bias checks; interpretability decisions
System development	Document datasets/models; evaluation and validation; build human oversight
Making available for use	Keep documentation; provide user guidance on limitations; risk assessment of deployment
Managing operations	Logging and monitoring outputs; ensure human oversight; intervene as needed

Privacy, security and data handling are non‑negotiable when legal teams deploy AI: start by mapping where PIPEDA or a substantially similar provincial law applies (Alberta, B.C. and Québec have their own regimes) and treat every dataset, model and vendor as a potential trigger for consent, cross‑border transfer rules and breach obligations; the Office of the Privacy Commissioner's summary on Canadian privacy laws explains which activities fall under PIPEDA and when provincial rules take over, and the full PIPEDA statute sets out duties such as accountability, purpose‑limitation, meaningful consent, proportional safeguards and mandatory breach reporting (including keeping records of incidents and notifying affected individuals and the regulator) - remember that a single misrouted, unencrypted email can create a reportable breach and expose a firm to regulatory scrutiny and penalties.

Practical controls for counsel include appointing a privacy lead, running PIAs on AI projects, contractually binding processors on cross‑border protections, documenting consents and retention limits, and matching technical safeguards (encryption, access logs, monitoring) to the sensitivity of the data and the lifecycle risk of the model; for quick reference, consult the Government's overview of the federal privacy framework and review the consolidated PIPEDA text when drafting client advisories or vendor agreements.

Obligation	Practical step for AI use
Scope / applicability	Map jurisdictions (PIPEDA vs provincial laws) and identify FWUBs or cross‑border processing
Consent & purposes	Document purpose at collection, seek meaningful consent for sensitive uses
Security & breach reporting	Encrypt data, log access, run PIAs and notify OPC + individuals if risk of significant harm
Accountability	Appoint a privacy officer and keep records of consents, assessments and vendor contracts

“The email you send to the Privacy Commissioner or that they send to you could be intercepted in transit or sent to the wrong address. If you are concerned about confidentiality, you should send your message by a secure means.”

Transparency and accountability are not optional checkboxes for AI in Canada - they are built into the federal playbook and should shape any lawyer's advice: the Treasury Board Directive on Automated Decision‑Making (Government of Canada) requires plain‑language notice before decisions, a meaningful explanation of how and why a system reached an outcome, and publication of an Algorithmic Impact Assessment (AIA) tool (Government of Canada) and related materials so the public can see how risks were assessed and mitigations applied; lawyers advising public‑sector clients or regulated deployers should therefore insist on preserved software versions, accessible audit rights and a clear logging regime that records outputs, human overrides and corrective actions so a system's lifecycle can be reconstructed if challenged.

The AIA tool itself - a detailed questionnaire that scores impact and drives obligations - must be completed early, published on the Open Government Portal (Government of Canada) and updated when functionality changes, so counsel should build AIA timing and review cycles into procurement and compliance checklists.

Think of the required records as a digital shoebox: every model version, peer review summary, human decision and notice belongs there - the more complete the archive, the stronger the defence against procedural‑fairness and accountability claims.

For the original requirements, see the Treasury Board Directive on Automated Decision‑Making (Government of Canada) and the Government of Canada Algorithmic Impact Assessment (AIA) tool.

Obligation	Practical requirement
Notice & explanation	Plain‑language notice across channels; meaningful, discoverable explanation of how decisions are made (Directive §§6.2.1–6.2.3)
Algorithmic Impact Assessment	Complete AIA early, publish final AIA on Open Government Portal (Government of Canada), and update when system changes (Directive §6.1; AIA tool)
Record‑keeping & provenance	Safeguard released software versions, document decisions/assessments, log outputs and human overrides for audits (Directive §§6.2.5–6.2.7; 6.3.4)
Quality assurance & peer review	Testing, monitoring, peer review and GBA Plus as required by impact level (Directive §6.3; Appendix C)
Recourse & reporting	Inform clients of recourse options and publish reporting on fairness/effectiveness on the Open Government Portal (Government of Canada) (Directive §§6.4–6.5)

Bias, fairness and professional ethics are not optional when lawyers use AI in Canada - they are practice‑critical duties that require an intersectional lens, careful testing and clear documentation: the Department of Justice's emphasis on a GBA Plus approach for legal teams helps ensure decisions powered by models consider gender, age, race, disability and other intersecting factors, and the Treasury Board's generative AI guidance (the FASTER principles) spells out practical steps - from stakeholder engagement to ongoing monitoring - for spotting and mitigating discriminatory outputs; lawyers should therefore insist on GBA Plus reviews, multidisciplinary audits and client‑facing disclosures as part of procurement, and treat model validation and provenance notes as core ethics work rather than a tech checkbox.

Canada's Algorithmic Impact Assessment process now builds gender‑ and age‑related questions into risk scoring, which means counsel can use public AIAs to probe how a system was assessed and whether vulnerable groups were considered; when advising on deployment, ensure prompts, redlines and human oversight are tailored to detect stereotype amplification, and require vendors to support explainability, retraining plans and independent audits.

Think of an unchecked AI bias as a missing page in a court file: it can change the story and the outcome - so embed GBA Plus, follow the Government's generative AI best practices and document every mitigation step to meet professional, privacy and human‑rights obligations (see the Justice GBA Plus Guide, the TBS generative AI guide and independent analysis of Canada's AIA evolution for practical checklists and case examples).

Don't enter sensitive or personal information into any tools not managed by the GC.

Intellectual property and liability are fast becoming the clearest legal potholes for Canadian practitioners advising on AI: stakeholders warned repeatedly in the Government's “What We Heard” consultation that unlicensed text‑and‑data‑mining (TDM) and opaque training inputs threaten creators' rights and could spark more litigation, while high‑profile U.S. rulings such as Judge Alsup's order in Bartz v Anthropic (discussed by Smart & Biggar) are already reshaping expectations about wholesale copying for model training - a dynamic that Canadian courts and policymakers may treat very differently because Canada's fair dealing regime and authorship doctrines diverge from U.S. “fair use” logic.

Practically, counsel should steer clients toward three concrete habits the materials stress: (1) demand transparency about training datasets and negotiate clear licensing or indemnity language with vendors, (2) document and preserve human creative inputs (prompting, curation, selection) to support claims of human authorship where needed, and (3) plan for tort‑style liability across the AI value chain by mapping contributors (developers, owners, deployers and end users) and securing contractual warranties/limits on liability.

For a snapshot of the policy debate and options on TDM, consult the Government's consultation report and the Baker McKenzie practice guide on Canadian AI/IP trends - both useful primers when drafting vendor clauses or litigating alleged infringement.

Risk	Practical mitigation (supported in sources)
Unlicensed TDM / training data	Seek licences, transparency and recordkeeping of inputs (Government “What We Heard” consultation report on copyright and generative AI)
Uncertain authorship of AI output	Document human skill/judgment and ownership clauses; monitor CIPO/Federal Court challenges (MLT Aikins: authorship in AI-generated works)
Cross‑value‑chain liability	Allocate risk through warranties, indemnities and vendor due diligence (Baker McKenzie guidance)

“Anthropic's LLMs have not reproduced to the public a given work's creative elements … Yes, Claude has outputted grammar, composition, and style that the underlying LLM distilled from thousands of works. But if someone were to read all the modern‑day classics because of their exceptional expression, memorize them, and then emulate a blend of their best writing, would that violate the Copyright Act? Of course not.”

Procurement, standards and governance should be the backbone of any AI strategy for Canadian firms and public bodies: embed risk assessments, vendor due diligence and human oversight into every contract, prefer pre‑qualified channels (Vendor of Record or PSPC's Artificial Intelligence Source List) for predictable compliance, and demand documentation on training data, security posture and opt‑out features so suppliers can be audited; the Government of Canada's practical Government of Canada guide on the responsible use of generative AI explains why testing, change management and the FASTER principles (Fair, Accountable, Secure, Transparent, Educated, Relevant) belong in procurement templates.

Recent federal reviews also call for system‑level fixes - a Chief Procurement Officer, vendor performance management and standardized rules - to stop fragmentation that lets risky buys slip through the cracks, and the Office of the Procurement Ombud's report lays out those five priority reforms for federal procurement.

Practical buying rules for legal teams: insist on independent audits and versioned models, require opt‑out and data‑residency guarantees, include clear IP/indemnity language, and use AI‑assisted RFP tools and VOR pathways to bid smarter; remember that Canada's procurement market is large (roughly $37B a year) so a single missing security approval can delay access to major opportunities - plan contracts and compliance with that scale in mind (see practical market tips in the Government Contracts Canada: AI & VOR Guide).

Time for Solutions

Procurement lever	Practical action
Central leadership	Establish a Chief Procurement Officer to drive standards and accountability (Office of the Procurement Ombud "Time for Solutions" report)
Pre‑qualification	Use VOR/AI Source List to streamline compliant buys and vendor checks
Contract controls	Require data provenance, opt‑out settings, audits, warranties and IP clauses
Risk & standards	Apply government guidance, testing, and FASTER principles before deployment (Treasury Board of Canada Secretariat guide on the responsible use of generative AI)

Wrap the guide into an actionable, Canada‑focused checklist: (1) stand up a multidisciplinary AI governance team and ongoing training program and use published checklists (for a concise legal risks checklist see LexisNexis'

Artificial Intelligence (AI) Technology Legal Risks Checklist

and for vendor due diligence consult Practical Law's AI Tool Vendor Due Diligence Checklist) to keep obligations front and centre; (2) map each AI use against risk‑levels (high‑impact screening, biometric ID, health or employment decisions), complete an Algorithmic Impact Assessment where applicable, and preserve versioned models, audit logs and human‑override records so a single audit entry can act like a breadcrumb that explains a disputed decision; (3) tighten contracts - define IP for inputs/outputs, require training‑data transparency, warranties/indemnities and service levels (see Goodmans' AI Agreements Checklist summaries) - and insist on strong data‑protection and breach procedures that align with PIPEDA or provincial regimes; (4) build bias‑mitigation and GBA Plus review into procurement, test datasets for representativeness, and require explainability and retraining plans from vendors; and (5) pilot narrowly, measure outcomes, and document every step so compliance becomes evidence, not an afterthought.

For practical upskilling and hands‑on prompt and tool training that helps operationalize these steps, consider the 15‑week AI Essentials for Work bootcamp: AI Essentials for Work syllabus (Nucamp) and AI Essentials for Work registration (Nucamp) to turn checklist items into repeatable firm practices.

Program	Length	Courses	Cost	Register
AI Essentials for Work	15 Weeks	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills	$3,582 early bird / $3,942 regular (18 monthly payments)	Register for AI Essentials for Work (Nucamp)