AI-Generated Evidence in Canadian Legal and Investigative Proceedings: A Practitioner's Guide

When a piece of digital evidence arrives in a legal matter or an insurance investigation, two questions now run simultaneously where previously only one did.

The first question has always been there: what does the evidence show.

The second question is new: is the evidence what it claims to be.

Artificial intelligence has made the second question necessary for every piece of digital media that enters a proceeding, an investigation, or a claims file. Video that looks authentic. Photographs that appear genuine. Audio recordings that sound real. Documents that seem official. Each of these can now be fabricated at consumer-grade cost, with tools available by subscription, in minutes.

Canadian courts are responding. The legal framework is developing. The evidentiary standards are being tested. And the practitioners who understand what is happening, what the courts have said, and what the authentication process involves are the ones who will be prepared when the next file lands.

This guide covers every media type, every relevant Canadian decision, the full applicable legal framework, and what investigators and litigators in each practice context need to do.

What AI-Generated Evidence Is: The Four Media Types

AI-generated evidence is not a single category. It encompasses four distinct media types, each produced through different technical methods and each carrying different forensic indicators. Understanding the differences matters because the authentication methodology, the triage protocol, and the forensic examination process differ for each.

Video. AI-generated video includes deepfake video in which a real person's face or body has been composited onto a different video, fully synthetic video generated by diffusion model tools, and manipulated video in which existing footage has been edited to alter events, timing, or participants. In Mendones v. Cushman and Wakefield (Cal. Super., September 9, 2025), the first documented case of a deepfake submitted as authentic court evidence, the fabrications were identified through looping video feeds, absent facial expressions, monotone delivery, and a metadata contradiction in which the file claimed an iPhone 6 as the recording device but required an iPhone 15 Pro to produce the content.

Image. AI-generated images include photographs produced entirely by generative AI tools, real photographs altered by AI compositing to add or remove subjects or damage, and manipulated images designed to fabricate or misrepresent injury, property damage, or scene conditions. Forensic image authentication examines pixel-level statistical patterns, compression artifact consistency, lighting and shadow coherence, and metadata integrity.

Audio. AI-generated audio includes voice cloning in which a specific person's voice is reproduced synthetically, entirely fabricated voice recordings attributed to a named person, and manipulated recordings in which existing audio has been edited to alter content, sequence, or speaker identity. Forensic audio authentication examines spectral characteristics, cadence and prosody patterns, background noise consistency, Electrical Network Frequency signatures, and file metadata.

Documents. AI-generated documents include invoices, receipts, clinical records, legal correspondence, financial statements, and identity documents produced by generative AI or altered by AI editing tools. Document authentication involves different analytical pathways than media authentication but applies the same foundational framework of metadata examination, format consistency analysis, and structural integrity assessment.

The Two-Sided Problem

The AI evidence problem in legal and investigative proceedings does not run in one direction. It presents two distinct failure modes, and every practitioner needs to understand both.

The first is fabricated evidence that is not identified and enters the record as authentic. A claimant submits a manipulated injury photograph. A party tenders a synthetic voice recording as an authentic admission. A forged document is produced in discovery. Each of these represents evidence that should not be in the record and that, if undetected, influences the outcome of a proceeding.

The second is authentic evidence that is challenged as fabricated. A party whose genuine dashcam footage is inconvenient raises a deepfake allegation to cast doubt on it. An insurer's legitimate surveillance recording is challenged on the grounds that modern AI tools could have created it. This is what researchers Chesney and Citron identified as the Liar's Dividend in 2019: the existence of the technology itself provides cover for bad-faith denial of authentic evidence.

Both failure modes are now active in Canadian proceedings.

The Canadian Legal Framework: What Governs AI Evidence Right Now

There is no AI evidence statute in Canada. Bill C-27, which included Canada's proposed Artificial Intelligence and Data Act, died on the Order Paper in January 2025. Canada currently has no federal AI law. What governs AI-generated evidence is the existing evidentiary framework, applied to a new category of challenge.

Section 31.1 of the Canada Evidence Act places the authenticity burden on the party tendering digital evidence. That party must produce evidence capable of supporting a finding that the document is what it purports to be. The standard is not onerous but it is not satisfied by assertion alone. A deepfake allegation that puts authenticity squarely in issue requires the tendering party to provide a positive foundation for the evidence's genuineness.

Provincial authentication provisions operate on the same framework across Canada. Ontario Evidence Act, RSO 1990, c E.23, s 34.1. Alberta Evidence Act, RSA 2000, c A-18, ss 41.1-41.8. Manitoba Evidence Act, CCSM c E150, ss 51.1-51.8. Saskatchewan Evidence Act, SS 2006, c E-11.2, s 54. Nova Scotia Evidence Act, RSNS 1989, c 154, ss 23A-23H. Every jurisdiction in Canada requires a party tendering digital evidence to establish its authenticity, with the same low threshold and the same vulnerability to a forensically grounded challenge.

The Mohan and White Burgess framework governs the admissibility and reliability of expert evidence. R v Mohan [1994] 2 SCR 9 requires relevance, necessity, absence of an exclusionary rule, and a properly qualified expert. White Burgess Langille Inman v Abbott and Haliburton Co, 2015 SCC 23 adds impartiality and gatekeeping discretion. A forensic authentication expert must demonstrate methodology reliability, qualification, and independence from the retaining party.

Ontario Regulation 384/24, in force since December 1, 2024, requires experts filing reports in Ontario civil proceedings to certify the authenticity of every authority, document, or record cited in the report, subject to specific exceptions for documents whose authenticity is called into question within the report. This regulation directly creates demand for forensic authentication analysis at the claims and pre-trial stage.

The Federal Court Practice Notice on AI, issued May 7, 2024 and updated June 20, 2025, requires parties to declare any use of AI in preparing filed materials, mandates accuracy verification, and explicitly identifies deepfakes as a Court concern. Manitoba, Yukon, Nova Scotia, and Quebec's Court of Appeal have issued parallel notices. The Ontario Superior Court of Justice issued new AI practice directions for both civil and family law proceedings in November 2025.

The Ontario Civil Rules Committee AI Subcommittee is actively consulting on proposed amendments to the Rules of Civil Procedure that would give litigants formal tools for challenging the authenticity of evidence alleged to be AI-generated or AI-modified. The Law Commission of Ontario submitted to this subcommittee in 2026. The consultation remains open. Any resulting rule changes will create a structured procedural pathway for exactly the kind of challenge that currently proceeds on an ad hoc basis under s 31.1.

What Canadian Courts Have Said: The Case Record

Five Canadian decisions have now addressed AI-generated evidence directly. Together they establish the current judicial framework and signal where the law is developing.

R v Medow, 2025 ONCJ 661. The foundational Canadian decision. Justice Brock Jones of the Ontario Court of Justice took judicial notice at paragraphs 54 to 55 of the widespread proliferation of AI technology capable of producing realistic deepfake videos, describing them as highly deceptive and a potentially serious concern to the integrity of the justice system. At paragraph 73, Justice Jones stated that as falsified digital evidence becomes more convincing, courts must ensure that the authentication voir dire required for digital evidence is not rendered meaningless. At paragraph 61, the court ultimately declined to infer that the video in question had been digitally altered to falsely incriminate the accused. This decision establishes both the judicial recognition of the threat and the maintenance of the existing authentication standard as the operative framework.

Head v John Doe, 2026 BCSC 184. A 2026 BC Supreme Court decision cited by the International Bar Association in its April 2026 analysis of Canadian deepfake evidence law alongside R v Medow. The IBA cites paragraph 50 of this decision in the context of courts declining to infer digital manipulation without evidentiary foundation. The full decision should be reviewed on CanLII for complete procedural context before being cited in proceedings.

R v Aslami, 2021 ONCA 249. Justice Nordheimer of the Ontario Court of Appeal stated at paragraph 30 that certain types of digital evidence may require expert authentication because there are too many ways for an individual, who is of a mind to do so, to make electronic evidence appear to be something other than what it is. This observation, made before the current generation of consumer-grade AI tools existed, has become significantly more applicable since it was written.

Breton v Ministry of Health and Social Services, 2025 QCCAI 280. The Commission d'accès à l'information du Québec acknowledged at paragraphs 18 to 20 that AI-generated evidence can mislead courts and stakeholders, compromising the integrity of the judicial process. At paragraphs 27 to 35, the Commission nonetheless admitted certain AI-generated documents on the basis of corroborating source documents, the probative value of the materials, and the flexible evidentiary rules applicable to administrative proceedings. This outcome illustrates the variable standard that different Canadian adjudicative bodies are applying.

R v Kapoor, 2025 ONCJ 542. An Ontario court found that distributing a deepfake intimate image fell outside section 162.1 of the Criminal Code as currently drafted. The decision is not directly an evidence authentication case, but it confirms both the pace at which deepfake cases are reaching Canadian courts and the gap between the current statutory framework and the technology it needs to address.

The Four Media Types: Forensic Indicators by Type

Each media type carries different forensic indicators. The triage checklist and the forensic examination methodology differ accordingly. The following section covers what to look for in each.

What Practitioners in Each Context Need to Do

The specific actions required differ by practice context, but the underlying principle is the same across all of them: authentication is pre-trial work, not trial work.

SIU investigators and claims professionals. The two most common failure modes in the claims context are approving a claim on the basis of fabricated media and denying a claim on the basis of a visual judgment that is not supported by forensic documentation. Both are avoidable with a simple intake protocol.

Preserve the original file with its metadata intact before any review. Generate a hash value on receipt and record it in the file. Commission a Tier 1 forensic screen on any file where the triage indicators raise questions before making a coverage decision. The FSRA Fraud Reporting Service Rule requires a documented analytical basis for fraud determinations at the reasonable grounds level. A forensic authentication report provides that basis. A visual review does not.

Civil litigators. The deepfake defense is active in Canadian civil proceedings. Both sides of the file are exposed. Authentic evidence needs to be hardened before disclosure. Challenged evidence needs forensic support before a formal challenge is raised. The Ontario Civil Rules Committee's proposed AI evidence challenge mechanism, under consultation since 2025, will formalise this process. Before that mechanism exists, section 31.1 of the Canada Evidence Act and the Mohan framework are the operative tools.

A Tier 1 forensic screen on your client's key media evidence before disclosure costs 48 to 72 hours and provides a documented foundation that pre-empts a bare deepfake allegation. Raising a formal authenticity challenge against opposing evidence without forensic support risks the costs and credibility consequences visible in AI-related conduct cases across Canadian courts.

Private investigators. Digital evidence authentication is a specialist function that most PI firms do not maintain in-house. A forensic authentication report formatted for inclusion in a PI investigation report provides the documented, reproducible analysis that the investigator's client needs to act on the findings. The 48 to 72 hour turnaround on Tier 1 screens works within standard PI investigation timelines.

The Authentication Protocol: What Every File Should Have

Regardless of media type, the foundational authentication protocol is the same. These steps apply to every piece of digital evidence that may be challenged or that will be relied upon in a proceeding, investigation, or claims determination.

At intake: receive the original file, not a copy, screenshot, or compressed version. Document the file name, size, format, and the date, time, and method of receipt. Generate a hash value of the original file immediately and record it. Do not open the file in any application that modifies access metadata before the hash is generated. Store the original unmodified.

Before disclosure or tendering: commission a forensic authentication assessment on any media file central to the matter. A Tier 1 screen establishes metadata consistency, encoding profile consistency, and the absence of detected manipulation indicators. It provides the documented basis for the section 31.1 authentication argument and pre-empts a bare deepfake allegation before it is raised.

When a challenge arises: require the challenging party to provide the specific forensic basis for the challenge. A bare allegation that modern AI tools could have created the evidence does not meet the threshold established by R v Medow, Huang v Tesla, and United States v Reffitt. If a forensically grounded challenge is raised, commission a Tier 2 comprehensive analysis. Serve the resulting report in accordance with the applicable expert report rules and ensure compliance with Ontario Regulation 384/24 where applicable.

When you suspect opposing evidence: request the original file at the discovery stage. Commission a Tier 1 screen before raising a formal challenge. If the screen identifies indicators, commission Tier 2 analysis. Raise the challenge at the earliest available pre-trial proceeding with the forensic report as the evidentiary foundation.

Authenticating evidence in the age of AI

As generative AI improves, the risks associated with deepfake evidence increase. This article explores these risks and how they map onto existing and proposed evidentiary rules in Canada, ultimately concluding that – in a world where truth is increasingly less apparent – more may be needed to protect litigation’s truth-finding function.

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