AI Litigation Briefing — June 30, 2026
Updated: Sep 26
This is an honest AI disclosure. This briefing presents my analysis, as Pouya Shafabakhsh, from the perspective of AI governance, risk, compliance, and AI litigation. For the convenience of legal professionals and busy executives, this page also includes a podcast. The written briefing on this page is the controlling substantive analysis; a verbatim transcript is not currently available.
I. SCC Rules an Accused Can Access an Expunged Police Misconduct Record
In Edmonton (Police Service) v. McKee, 2026 SCC 24 (released June 26, 2026), the Supreme Court of Canada unanimously held (Martin J.) that a 2015 police misconduct finding, later removed from a detective's record under s. 22 of Alberta's Police Service Regulation (Alta. Reg. 356/1990), remained subject to first-party Crown disclosure because it was relevant to the accused's right to make full answer and defence. The Court dismissed the Edmonton Police Service's appeal, affirming that administrative expungement changes a record's status, not its character.
AI GRC Specialist Analysis
The governing framework is the Stinchcombe / McNeil disclosure line — R. v. Stinchcombe, [1991] 3 S.C.R. 326 (first-party disclosure of all relevant, non-privileged information) and R. v. McNeil, 2009 SCC 3 (serious police misconduct as disclosable). The constitutional anchor is s. 7 of the Charter and the right to full answer and defence. McKee's holding — that "relevance is the watchword," and that a record removed under a provincial regulation (PSR s. 22) cannot defeat a federal constitutional disclosure obligation — translates directly to AI governance. Organizations routinely assume that "deleted," "purged," or retention-expired data is gone; McKee is a clean reminder that legal relevance, not an internal deletion policy, controls producibility. For AI systems, the discoverable surface includes prompts, outputs, system and audit logs, vector stores, and model-interaction trails. Under Canadian civil procedure (e.g., Ontario Rules of Civil Procedure, r. 30 documentary discovery; r. 30.1 deemed undertaking) and U.S. e-discovery (FRCP 26(b), 34, 37(e) on failure to preserve ESI), that surface is producible if relevant. The defensible posture is a documented, independent map of what each AI system retains, for how long, and where — built before a dispute, not reconstructed under a disclosure motion. Data sovereignty is a litigation-readiness control, not merely a privacy one.
II. How Three Courts Are Approaching AI Adoption
Law360 reports that three court systems are approaching AI experimentation in markedly different ways — some offering proprietary tools and training, others operating under unwritten policies that effectively discourage AI use altogether. The piece captures a judiciary improvising governance in real time, without a shared standard.
AI GRC Specialist Analysis
The inconsistency is the operative risk, and it sits against a thickening backdrop of formal rules. In New York, 22 NYCRR Part 161 (effective June 1, 2026) permits AI but, read with 22 NYCRR Part 130 and Rule 3.3 of the Rules of Professional Conduct (candor toward the tribunal), makes the signer responsible for certifying no fabricated AI material — and individual judges retain discretion to add part-rule disclosure requirements. In Ontario, Rule 4.06.1(2.1) of the Rules of Civil Procedure (added by O. Reg. 384/24) requires a signed certification of the authenticity of every authority in a factum, and LSO Rule of Professional Conduct 3.1 (competence) applies regardless of tooling. When the bench's posture ranges from "use our tool" to an unwritten "don't," a practitioner cannot calibrate to a single expectation. The efficient response is to maintain a verification baseline that can be adjusted to the actual rule, court, certification requirement, and matter-specific duty that applies. That converts a venue-by-venue patchwork into a single, documented, reproducible process. The alternative — re-engineering verification for each court and each judge — is precisely the kind of unstructured adoption that produces the unverified filings now drawing costs orders and sanctions across both countries.
III. New Connecticut Law Puts AI-Assisted Hiring Under a Microscope
Law.com reports on Connecticut's new AI statute — Senate Bill 5, the Connecticut Artificial Intelligence Responsibility and Transparency Act (the "CART Act"), enacted as Public Act 26-15 (signed May 27, 2026) — which places automated employment-related decision technology (AEDT) under heightened scrutiny.
AI GRC Specialist Analysis
The CART Act regulates AEDT — technology that processes personal data to generate an output (prediction, recommendation, classification, ranking, or score) that is a "substantial factor" in an employment-related decision (hire, promote, discipline, discharge, tenure, training). Three provisions matter most for litigation exposure. First, the Act amends the Connecticut Fair Employment Practices Act so that, from October 1, 2026, an employer's use of AEDT is not a defense to a discrimination complaint — though a court or the Commission on Human Rights and Opportunities may weigh documented anti-bias testing. Second, from October 1, 2026, employers issuing federal WARN Act notices must disclose to the Connecticut Department of Labor whether layoffs relate to AI or technological change. Third, from October 1, 2027, deployers must give plain-language notice when AEDT directly interacts with applicants or employees. Enforcement runs through the Connecticut Unfair Trade Practices Act (CUTPA), exclusively by the Attorney General, with a notice-and-cure window (Oct 1, 2026 – Dec 31, 2027) and no private right of action. The pattern is the story: with Colorado's AI Act (SB 205, as amended by SB 26-189, effective Jan 1, 2027) and CCPA automated-decision rules also live, multi-state employers face overlapping, non-identical regimes. An AI Gap Analysis — mapping every AEDT touchpoint, the data it processes, and whether anti-bias testing is documented — is the defensible way to show a regulator you can explain and defend the system.
IV. Goodwin Procter Confirms a Third Data Breach in Five Years
Law360 reports that Goodwin Procter confirmed a data-security breach in spring 2026 — its third cybersecurity incident since 2021. The disclosure lands amid steady reporting on escalating law-firm breach trends.
AI GRC Specialist Analysis
Law firms are high-value targets because they aggregate privileged strategy, transaction data, and personal information across many clients. Repeat incidents implicate concrete obligations. Under LSO Rule of Professional Conduct 3.3 (confidentiality) and its commentary on technological competence, and the parallel duty in ABA Model Rule 1.6(c) (reasonable efforts to prevent unauthorized disclosure) informed by ABA Formal Opinion 483 (breach response), safeguarding client data is an ethical duty, not just an IT function. Statutory breach-notification regimes attach as well: PIPEDA's mandatory breach reporting (s. 10.1, "real risk of significant harm") in Canada, and the New York SHIELD Act (Gen. Bus. Law §§ 899-aa, 899-bb) for New York residents. The AI dimension widens the surface: every sensitive file routed through a cloud-hosted AI tool multiplies the points at which privileged data can be exposed, and standards such as NIST AI RMF (Map/Measure/Manage/Govern) and ISO/IEC 42001 expect data-flow boundaries to be defined and controlled. Data sovereignty — processing the most sensitive matter data in an isolated, offline environment rather than a multi-tenant cloud model, and excluding it from third-party training under contract — is the structural control that shrinks the breach surface. The cross-border U.S. CLOUD Act exposure of cloud-resident data only sharpens the case for keeping the crown-jewel files off the network entirely.
V. AAA and Integra Ledger Announce a Legal Protocol for AI-Agent Transactions
The American Arbitration Association and Integra Ledger, with a coalition including Google, IBM, and Circle, launched the Legal Context Protocol (LCP) on June 24, 2026 — an open (Apache-2.0) standard that makes legal terms, consent, governing law, and dispute-resolution paths discoverable and cryptographically verifiable when AI agents transact.
AI GRC Specialist Analysis
LCP answers a question the payment rails ignore: when an autonomous agent acts, what were the terms, who consented, which law governs, and how is a dispute resolved? It publishes terms at a standard URL and records a cryptographic fingerprint so a counterpart agent, court, regulator, or auditor can later verify which terms governed a transaction. For litigation and M&A practice, the forensic implications are direct. As agents execute procurement and settle payments, the evidentiary questions become authentication and reconstruction: under U.S. law, Federal Rule of Evidence 901 (authentication) and FRE 902(13)–(14) (self-authenticating machine-generated and digitally-verified records) will frame whether an agent's transaction record is admissible; in arbitration, the AAA's own commercial rules and the Federal Arbitration Act supply the dispute pathway. The deeper point for AI GRC: an infrastructure that lets humans verify what an agent did is, functionally, an audit standard for agentic behaviour. Where LCP is absent or partial, the gap must be filled by independent forensic reconstruction — proving, from logs and artifacts, what an autonomous system actually agreed to and executed. Organizations deploying transacting agents should map now where agent actions create legal exposure, whether consent and terms are verifiable after the fact, and who can reconstruct an agent's decision trail under challenge. Agentic commerce makes "show your work" a machine-level requirement, and verifiable legal context the new baseline of diligence.
VI. AI Is Making the Partner the New Bottleneck
Canadian Lawyer (opinion) argues that as AI absorbs the routine work once handed to junior lawyers, the senior partner becomes the new bottleneck — the last human checkpoint through which an ever-larger volume of AI-produced work must pass for review.
AI GRC Specialist Analysis
The piece names a structural failure mode the year's discipline record keeps illustrating. The professional duties do not bend to volume: LSO Rule of Professional Conduct 3.1 (competence), Rule 5.1 and the supervision duties over delegated work, and Rule 3.2 (quality of service) all locate responsibility at the supervising lawyer, regardless of how the first draft was produced. Ontario authenticity certifications and any New York court-specific Part 161 requirements should be checked against the exact filing, court, and rule in force rather than treated as identical statewide obligations. When AI multiplies output but verification still funnels through a few reviewers, the checkpoint degrades into one of two failure states: a rubber stamp (the duty is nominally met but not really discharged) or a chokepoint (throughput collapses). The first is what produces the fabricated-citation filings now drawing costs orders; the second wastes the efficiency AI promised. The resolution is not more partner hours but a verification layer that scales independently of any single reviewer's time — an independent, documented, reproducible check that catches fabricated or misquoted authority before signing, and leaves a contemporaneous record a tribunal will credit. Governance is what converts the partner from a bottleneck back into an accountable control, by giving the checkpoint a process rather than relying on a hurried human signature at the end of the line.
VII. Privacy as the Foundation for Governing AI
Canadian Lawyer profiles how Samsung's counsel treats privacy as the foundational discipline for governing AI — extending mature privacy infrastructure into AI oversight rather than building governance from scratch.
AI GRC Specialist Analysis
The approach is sound because privacy law already answers AI governance's hardest operational questions. Canada's PIPEDA (Principles 4.1 accountability, 4.5 limiting use/retention, 4.7 safeguards), Quebec's Law 25, and — looming — Bill C-36's proposed Protecting Privacy and Consumer Data Act all require an organization to know what personal data it holds, why, where it flows, and for how long. That is precisely the data map AI governance needs before a model ingests, retains, or reuses personal information. Internationally, ISO/IEC 27701 (privacy information management) dovetails with ISO/IEC 42001 (AI management systems) and the NIST AI RMF "Govern" function, so a firm with mature privacy controls inherits most of the scaffolding for AI oversight. For ON–NY cross-border practices, the convergence is strategic: the same data-flow inventory that satisfies a privacy regulator under PIPEDA or the NY SHIELD Act is the inventory an independent AI verification depends on to know which data touched which model. Building AI governance on a privacy foundation also clarifies cross-border exposure — where data resides, which providers can access it, and whether the U.S. CLOUD Act reaches it. The practical takeaway: do not stand up an "AI governance program" parallel to privacy; extend the privacy program's inventory, retention schedule, and accountability roles to cover AI systems, and verify the result independently. Privacy maturity is a head start on AI defensibility.
VIII. Reed Smith Partners With Cornell to Train Attorneys on AI
Law360 and Law.com report that Reed Smith launched an AI leadership program with Cornell University to equip its partners to advise clients navigating significant AI change — part of a broader 2026 wave of law-firm AI education investment.
AI GRC Specialist Analysis
The training wave is rational, but it should be read against the competence duties it implicates rather than as a substitute for them. LSO Rule of Professional Conduct 3.1-2 commentary now expressly contemplates technological competence; ABA Model Rule 1.1 Comment [8] requires lawyers to keep abreast of the benefits and risks of relevant technology; and ABA Formal Opinion 512 (2024) sets out the generative-AI duties of competence, confidentiality, communication, candor (Rule 3.3), and reasonable fees. Training advances the first half — fluency — but none of these rules is satisfied by fluency alone. The risk is a confidence-competence gap: a partner trained to deploy AI quickly, without an independent verification step behind the output, can produce errors faster and with more institutional authority, which is exactly the profile behind this year's sanctions. Education and verification are complementary controls. Training can improve AI competence, while independent verification can support counsel in meeting the professional, court-specific, and filing-specific duties that actually apply. Firms that treat training as the finish line risk institutionalizing faster unverified work; firms that pair training with a documented verification layer convert AI literacy into defensible product. The investment signal is also a market signal: as fluency becomes table stakes, the differentiator shifts to who can prove their AI-assisted work is sound.
IX. Drawing the Line Between AI and the Law
Law.com features an analysis on where the line falls between AI capability and accountable legal judgment — which tasks AI may perform, and which must remain with a lawyer's non-delegable judgment.
AI GRC Specialist Analysis
The "line" is not abstract; it is being drawn, repeatedly, at the point of signing and filing. Across jurisdictions the same principle now recurs: AI may assist, but the accountable judgment and the verification behind a certification cannot be delegated. In Ontario, Rule 4.06.1(2.1) of the Rules of Civil Procedure (O. Reg. 384/24) requires a signed certification of the authenticity of every authority in a factum, and Rule of Professional Conduct 3.1 fixes competence on the lawyer. In New York, Part 161 establishes a statewide AI policy framework, while any certification or verification duty must be assessed under the specific court rule, filing requirement, professional duty, and facts that actually apply. In Quebec, a 2026 Superior Court decision annulled an arbitral award built on hallucinated authorities — extending the line to adjudicators. In the U.S. federal courts, FRCP 11(b) (certification by signature) and the inherent sanctions power have produced a year of costs orders and suspensions for AI-fabricated citations. The throughline for practitioners is a documented division of labour: AI on the efficiency side (drafting, summarizing, first-pass research), and human judgment plus independent verification on the accountability side (authenticity of authorities, candor to the tribunal, the final signature). Putting that division in writing — a firm AI policy that names which tasks AI may touch and mandates an independent verification step before filing — is what lets a practice capture AI's speed on the first half without forfeiting the second.
X. Big Tech Moves Into Legal: Perplexity's "Computer for Counsel"
Law.com tracks Big Tech's accelerating entry into the legal market, including Perplexity's launch of "Computer for Counsel" and deepening enterprise AI adoption (e.g., Gunderson Dettmer expanding Perplexity use), as platform vendors build legal-specific products.
AI GRC Specialist Analysis
As general-purpose platforms ship legal-specific AI, capability will spread faster than governance — the pattern underlying every item in this briefing. The duties, however, are platform-agnostic. ABA Formal Opinion 512 and Model Rule 1.6(c) require reasonable safeguards over client confidentiality whenever a tool processes matter data; ABA Model Rule 5.3 extends supervisory duties to non-lawyer assistance, which now includes AI vendors; and the data-handling terms of any consumer or enterprise AI product must be assessed against PIPEDA, the NY SHIELD Act, and U.S. CLOUD Act exposure before privileged data touches it. Easy, powerful tools lower the barrier to AI-assisted work and, simultaneously, the barrier to unverified, privilege-leaking, or non-compliant use. The differentiator for a serious practice is therefore not access to the newest platform but the governance wrapped around it: an approved-tool list, defined data boundaries (what may and may not be entered), a vendor-terms review against confidentiality and cross-border rules, and an independent verification step before any AI-assisted product is filed or relied upon. NIST AI RMF and ISO/IEC 42001 both frame this as governance over the tool lifecycle. The platforms will keep improving; the obligation to prove the output is sound, and the privileged data protected, stays exactly where it has always been — with the lawyer who signs.
I would welcome to exchange insights with AI and Legal Professionals in comments, and if you find this article helpful, I appreciate sharing with your network.
Author: Pouya Shafabakhsh Co-Founder, CAIO & Principal Forensic AI Auditor, Radsam Academy of AI Sovereign Governance. Independent AI governance and forensic-audit analysis. Educational information only; not legal advice.
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Updated September 2, 2026: This dated briefing has been reviewed for current-status wording and claim calibration. A statement is a binding “must” only where the cited law, rule, court order, professional duty or contract actually imposes it; otherwise it should be read as a governance or risk-management consideration. NIST AI RMF is voluntary, and ISO/IEC standards do not by themselves create statutory duties. Radsam services are not generally required by law and do not guarantee privilege, confidentiality, security, compliance, admissibility, licensing, insurance coverage, reputation, or litigation results.
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