Radsam's Tuesday AI Litigation Briefing for Legal Professionals, Top-Tier Lawyers and Honorable Judges - August 18, 2026
- Pouya Shafabakhsh
- Aug 18
- 13 min read
I. Patent Eligibility Pressure Builds as AI Disputes Accelerate
Case or Development Summary and Status
Law.com highlighted an interview with departing U.S. District Judge Alan Albright focused on patents, litigation practice, and continuing uncertainty under 35 U.S.C. Section 101. The article headline framed his concern as a coming wave of AI cases unless Congress clarifies patent eligibility. That is commentary from an experienced patent judge, not a judicial holding and not a change in governing eligibility doctrine. For patent teams, however, it is a useful signal that AI-related inventions may intensify already difficult questions about abstract ideas, software functionality, technical improvement, and claim drafting.
Litigation AI GRC Analysis
For AI companies and litigators, eligibility risk can appear long before a courtroom challenge. Product descriptions, model architecture, training or inference methods, workflow automation, and claimed technical effects can all shape the factual record later used to characterize an invention. A governance failure occurs when marketing language, patent specifications, engineering documents, and litigation positions tell materially different stories about what the system actually does. In an Ontario-New York corridor matter, U.S. patent doctrine controls U.S. eligibility while Canadian patentability questions require separate Canadian analysis; one framework should not be imported into the other.
Strategic Risk Management Recommendation
Build an eligibility evidence file before dispute pressure rises. Preserve dated architecture diagrams, system versions, benchmark definitions, engineering change logs, inventorship records, claim-support materials, and the technical basis for asserted improvements. Separate legal conclusions from engineering facts and require patent counsel to validate any AI-assisted research or drafting. If generative tools touch claim analysis, prior-art review, or briefs, retain reproducible prompts or workflow records where permitted and independently verify authorities. The objective is not to predict Section 101 outcomes; it is to make the technical narrative traceable, internally consistent, and defensible under the law that actually governs the forum. Keep this record before the litigation theory hardens, not after. Preserve source records.
This is an honest AI disclosure. This briefing is my, Pouya Shafabakhsh’s analysis from the perspective of AI governance, risk, and compliance, and AI litigation. For the convenience of esteemed lawyers and busy C-suite executives, we have also created an AI-generated podcast, which provides a deep dive analysis for those who prefer listening over reading.
II. Schulte v. LinkedIn Puts GenAI Document Review Under Familiar Discovery Discipline
Case or Development Summary and Status
Legaltech News examined Schulte v. LinkedIn as an emerging reference point for generative-AI document review. The reported ruling treated AI-assisted review through familiar discovery concepts rather than assuming that a new technology automatically demands a new legal standard. The court's reasoning, as summarized by commentators, emphasized reasonableness, proportionality, and concrete production deficiencies when parties challenge review methodology. The development matters because litigants increasingly combine search terms, technology-assisted review, generative systems, and human quality control in the same discovery pipeline.
Litigation AI GRC Analysis
The governance lesson is that an impressive model label does not establish discovery defensibility. Counsel should know what corpus entered the review, what material was excluded before AI analysis, which model or service version operated, how relevance instructions were framed, what quality checks were performed, and how human reviewers handled exceptions. A later challenge is harder to answer when teams cannot reconstruct those choices. Discovery proportionality also cuts both ways: demanding every internal metric may be excessive, while refusing targeted validation after a demonstrated deficiency may be difficult to defend. Technology choice and evidentiary record should therefore be designed together.
Strategic Risk Management Recommendation
Create a matter-specific AI review protocol before substantive review begins. Record custodians, collections, pre-culling criteria, search terms, model versions, instructions, sampling design, reviewer roles, privilege handling, escalation rules, and material changes. Preserve enough information to repeat or explain consequential decisions without needlessly exposing privileged strategy. Run targeted ground-truth checks and document both favorable and adverse results. If opposing counsel raises a specific deficiency, answer the demonstrated problem rather than relying on generic vendor claims. The goal is a proportionate, reproducible process in which lawyers remain accountable for discovery decisions and can distinguish system assistance from professional judgment. Agree the review protocol before generated relevance judgments influence production decisions. Record system versions, source boundaries, reviewer actions, exceptions, and approvals. Document material exceptions.
III. Agentic AI Reaches the Federal Appellate Level in the Amazon-Perplexity Dispute
Case or Development Summary and Status
Legaltech News reported a federal appellate decision in the Amazon-Perplexity dispute concerning an AI agent that acts for users while accessing an online platform. The appellate development drew attention because it addresses how conduct should be attributed when software agents perform user-directed tasks. The procedural posture matters: a preliminary-injunction ruling evaluates likelihood and equitable factors rather than finally resolving every legal theory. Even so, the dispute moves agentic AI from policy discussion into concrete questions about access, authorization, user agency, platform restrictions, and technical behavior.
Litigation AI GRC Analysis
For litigation AI GRC, the critical question is not whether a system is called an agent. It is who initiated the task, what permissions existed, which credentials were used, what the agent actually transmitted or retrieved, how platform controls responded, and whether the agent deviated from user instructions. Those facts can affect contract, computer-access, consumer, privacy, and evidence theories. Multi-agent workflows make attribution harder because one system may plan while another browses, calls tools, stores context, or takes action. Without event-level logs, organizations may later have only screenshots or vendor summaries instead of reconstructable evidence.
Strategic Risk Management Recommendation
Treat material agent actions as auditable events. Capture user authorization, task scope, tool calls, credential boundaries, timestamps, destination systems, model and agent versions, exceptions, retries, and human approvals. Define prohibited autonomous actions and require escalation for legal, financial, filing, or external-communication consequences. Contract for access to incident and activity records where third-party agents are used. In disputes, preserve native logs before changing the system. Do not assume that a user instruction automatically resolves legal attribution, or that autonomy automatically transfers responsibility to the vendor. Test the actual chain of action against the governing forum's law. Reconstruct the path from user instruction to external action before attribution becomes disputed. Preserve native sources, material versions, reviewer decisions, exceptions, and approvals. Verify sources.
IV. AI-Related Securities Class Actions Are Becoming a Distinct Litigation Workstream
Case or Development Summary and Status
Law.com analysis this week focused on the growing number of securities class actions tied to artificial-intelligence statements and performance narratives. The useful signal is not a prediction that every AI disclosure will generate liability. It is that public claims about AI capability, deployment, revenue contribution, competitive advantage, safety, or readiness can become litigation evidence when later events appear inconsistent with those claims. Plaintiffs and defendants will therefore increasingly work from the same cross-functional record: filings, earnings calls, product materials, internal evaluations, incident records, and executive approvals.
Litigation AI GRC Analysis
AI governance becomes litigation governance when statements leave the organization. A company may describe a capability as deployed while engineering records show a pilot, or describe human oversight while operating logs show minimal intervention. The reverse can also occur. Those mismatches matter because securities cases turn on legal elements and context, not merely technical error. For counsel, the defensible task is to preserve what decision-makers knew, when they knew it, how metrics were defined, and what qualifications accompanied public statements. Model performance should not be compressed into a single promotional number without traceable assumptions and limitations.
Strategic Risk Management Recommendation
Create a disclosure-control lane for material AI claims. Route significant public statements through legal, finance, product, engineering, risk, and governance owners; record source evidence and unresolved limitations; and preserve the approved wording with its supporting data. Define terms such as deployed, autonomous, accurate, secure, or revenue-generating before using them externally. When incidents arise, freeze relevant model versions, dashboards, communications, evaluation results, and decision records. For litigation readiness, map each challenged statement to contemporaneous evidence rather than rebuilding the narrative after a complaint is filed. Governance should make truthful, bounded communication easier, not simply create more policy. Use the same evidence base for public statements and later litigation explanations. Preserve sources, model versions, reviewer actions, exceptions, and approvals.
V. New York City Bar Policy Work Reinforces Professional-Judgment Boundaries for Legal AI
Case or Development Summary and Status
Law360 reported that a New York City Bar Association committee called for a nationwide framework governing lawyers' use of artificial intelligence and emphasized that AI may assist legal work but cannot replace professional legal judgment. The proposal is policy advocacy rather than binding national regulation. It nevertheless fits a broader New York pattern in which competence, confidentiality, supervision, verification, and accountability are being translated into AI-specific practice guidance. New York's separate 22 NYCRR Part 161 also addresses AI use in court papers through a model-rule framework that courts may apply.
Litigation AI GRC Analysis
For managing partners, the practical risk is policy fragmentation. A firm can have one enterprise AI policy while individual courts, clients, practice groups, vendors, and professional-responsibility authorities impose different operational expectations. A lawyer using an approved tool can still create risk if confidential inputs are mishandled, authorities are not verified, or review is delegated beyond the lawyer's competence. Conversely, a blanket prohibition may drive use into unmanaged channels. The governance objective is therefore controlled permission: approved use cases, data classifications, human-review duties, supervision, incident escalation, matter-specific restrictions, and evidence that the controls actually operate.
Strategic Risk Management Recommendation
Translate high-level ethics language into a litigation AI control matrix. For each use case, identify permitted tools, prohibited data, required human checks, supervisor responsibility, recordkeeping, client or court disclosure triggers, and escalation. Map New York requirements separately from Ontario professional and procedural duties. Verify the current rule of the actual court rather than treating Appendix A to Part 161 as an automatic statewide filing rule in every matter. Train lawyers with realistic examples from briefs, discovery, expert work, client communications, and research. Audit implementation periodically, because a written policy without operating evidence is weak assurance. Test policy controls against real matter files, not only written procedures. Record system versions, source boundaries, reviewer actions, exceptions, and approvals.
VI. Machine-Readable Watermarks Add a Provenance Signal, Not a Truth Certificate
Case or Development Summary and Status
Artificial Lawyer examined the legal-sector impact of Anthropic's move toward machine-readable marking of Claude outputs. Provenance technology can help identify that content passed through a particular generation or distribution process, but it does not establish that the content is factually correct, lawfully obtained, authored by a particular human, or admissible. Marking can also be lost or transformed through copying, reformatting, screenshots, transcription, or downstream editing. For litigators, the development is therefore useful as one evidentiary signal within a larger authenticity and chain-of-custody analysis.
Litigation AI GRC Analysis
The forensic mistake would be to treat a watermark as conclusive. Authenticity questions usually require context: original files, metadata, source-system records, account attribution, timestamps, transformation history, witness evidence, and the reliability of the detection method. The absence of a detectable mark may also be ambiguous because legitimate transformations can remove or degrade signals. In adversarial settings, counsel should distinguish provider provenance from human authorship and from substantive truth. A generated document can carry valid provenance while containing false statements; an authentic human-created document can lack any machine-readable marker. Those are different propositions requiring different evidence.
Strategic Risk Management Recommendation
Preserve native files and provenance metadata before converting or annotating contested content. Record the detector, version, configuration, date, operator, output, limitations, and any reference material used to interpret a watermark. Test whether ordinary transformations alter detection and retain both original and transformed samples. Corroborate machine-readable marks with account, device, repository, communication, and witness evidence when relevant. Do not describe a watermark result as proof of authorship, authenticity, truth, or admissibility. For internal drafting, provenance features can support disclosure and traceability, but lawyer verification remains necessary for legal authorities, facts, quotations, and representations to a court. Treat provenance as corroborating evidence, not a shortcut around authentication analysis. Record system versions, source boundaries, reviewer actions, exceptions, and approvals. Preserve sources, versions, reviewers, and approvals.
VII. Relativity claiR Brings Conversational AI Deeper Into Discovery Data
Case or Development Summary and Status
Legaltech News reported Relativity's new conversational AI interface for attorneys, with early access involving major law firms. The development matters because conversational interfaces can move AI closer to the corpus lawyers already use for discovery and investigation. That can reduce friction for asking questions, summarizing material, or navigating large datasets, but it can also make model outputs feel more authoritative than the underlying evidence supports. A chat-style answer is still an analytical layer over source material, not the source record itself.
Litigation AI GRC Analysis
In litigation, convenience must not break traceability. Counsel should be able to identify which documents supported an answer, what data was available to the system, whether privileged or restricted collections were in scope, how citations were generated, and whether later model or index changes would alter the response. Conversational systems also create new records: prompts, retrieved passages, answers, user feedback, and saved work product. Their discoverability, privilege status, retention, and security depend on facts and governing law. Governance should therefore cover the interaction layer as carefully as collection and review.
Strategic Risk Management Recommendation
Before enabling conversational discovery on a live matter, define corpus boundaries, user permissions, privilege controls, retention settings, citation expectations, and validation procedures. Require users to open and verify supporting documents rather than rely on synthesized answers. Preserve consequential queries and outputs when they influence case strategy, expert instructions, production decisions, or factual representations, subject to counsel's privilege and preservation analysis. Test known-answer questions, failure modes, access controls, and citation accuracy. Maintain an escalation path for unsupported or conflicting results. The goal is faster navigation without losing the evidentiary distinction between source material, machine inference, and lawyer judgment. Show where each answer came from and why a lawyer accepted it. Keep model versions, source evidence, reviewer decisions, exceptions, and approvals. Preserve native sources, material versions, reviewer decisions, exceptions, and approvals.
VIII. Agent Handoffs Create a New Chain-of-Custody Problem Across Legal AI Platforms
Case or Development Summary and Status
Legaltech News reported DeepJudge's Agent Handoff Protocol, designed to let work move between AI platforms while carrying context, with Harvey and Thomson Reuters reported as planned adopters. Interoperability can reduce repeated prompting and manual transfer, but it also changes the evidence trail. A single legal task may pass through several providers, models, tools, repositories, and policy domains before a lawyer sees the final output. That makes context transfer, permission inheritance, data minimization, provenance, and responsibility central governance questions rather than purely technical integration details.
Litigation AI GRC Analysis
A handoff can preserve useful context while obscuring where a statement originated or which system transformed it. Privileged or confidential material may also cross a boundary that was acceptable in the first platform but not the next. Security, retention, model-training terms, geographic processing, and audit access can differ by provider. From a forensic perspective, the important artifact is the transfer record: source agent, destination agent, payload, redactions, user authorization, policy checks, timestamps, tool calls, and changes made after transfer. Without that record, reconstruction can become speculative.
Strategic Risk Management Recommendation
Design cross-platform agent workflows as controlled data transfers. Classify the context before handoff; strip information not required for the next task; verify destination permissions and contractual terms; and log what was transferred, why, by whom, and under which policy. Assign an accountable human owner for the overall task even when several agents participate. Test whether citations, attachments, instructions, and privilege labels survive the handoff accurately. Preserve material transfer logs when litigation is reasonably anticipated. Do not let interoperability imply equivalence: each provider and processing step requires its own security, confidentiality, retention, and reliability assessment. Keep the custody path visible when context moves across platforms. Keep model versions, source evidence, reviewer decisions, exceptions, and approvals. Preserve native sources, material versions, reviewer decisions, exceptions, and approvals. Verify scope, authority, material exceptions independently.
IX. Goodwin’s Proprietary AI Platform Shows Firm-Built Tools Becoming Operational Infrastructure
Case or Development Summary and Status
Legaltech News and Law360 Pulse reported that Goodwin developed a proprietary artificial-intelligence platform and an attorney-built venture-financing tool intended as the first in a broader suite. The important market signal is that legal AI is moving from isolated third-party subscriptions toward firm-built or firm-configured operational systems. Internal development can improve workflow fit and governance visibility, but ownership does not make a system inherently reliable, secure, or professionally compliant. A proprietary platform still needs defined purpose, testing, change control, user permissions, incident management, and accountable legal oversight.
Litigation AI GRC Analysis
For law firms, custom systems create a dual record: the client matter and the product lifecycle. If a tool influences substantive work, future questions may reach requirements, training or retrieval sources, evaluation results, model versions, prompt templates, human-review design, and known limitations. Internal developers and lawyers may also use different vocabulary for accuracy, release readiness, or material error. Governance should connect those languages before deployment. A matter team should know which version operated and what controls were active, rather than assuming that the current production system accurately represents historical use.
Strategic Risk Management Recommendation
Establish a legal-AI release gate for internally built tools. Document the intended use, prohibited use, data classes, evaluation set, acceptance thresholds, red-team findings, human-review duties, security review, vendor dependencies, rollback process, and responsible owner. Version prompts, retrieval sources, models, and material code or configuration changes. Require matter teams to record substantive reliance when the system influences legal analysis or client advice. Revalidate after significant changes and maintain a retirement plan. Proprietary development can be strategically valuable, but defensibility comes from documented controls and reproducibility, not from the fact that the firm owns the interface. Preserve the release history that existed when client work was performed. Preserve native sources, material versions, reviewer decisions, exceptions, and approvals. Preserve sources, model versions, reviewer actions, exceptions, and approvals.
X. AI May Increase Pro Se Filings Without Increasing Courtroom Success
Case or Development Summary and Status
Law360 reported scholarly findings suggesting that AI availability is associated with a substantial increase in federal pro se litigation while not necessarily improving self-represented litigants' courtroom success. The report should be treated as a research-driven observation, not as proof that AI caused every filing increase or that every AI-assisted litigant performs poorly. For courts and opposing counsel, however, larger volumes of AI-assisted filings can increase verification, case-management, authenticity, citation, and procedural burdens even when the substantive quality of claims does not improve.
Litigation AI GRC Analysis
The governance challenge is asymmetric. A self-represented party may use tools that create polished pleadings without understanding jurisdiction, standards of review, evidentiary foundations, or professional restrictions that apply to lawyers. Courts must protect access to justice while maintaining reliable records and fair procedure. Lawyers responding to such filings should avoid assuming that unusual language proves AI use or misconduct. The relevant issues remain the actual filing, governing rules, factual support, and any demonstrated fabrication or abuse. New York's Part 161 provides an AI framework for court papers, but applicability and local implementation still require forum-specific verification.
Strategic Risk Management Recommendation
Use a neutral verification protocol when a filing raises authenticity or AI concerns. Check cited authorities against official or reliable sources, preserve the filed version, identify specific defects, and separate formatting anomalies from substantive falsity. Do not accuse a party of AI misuse without evidence. Where the court has an applicable AI rule or standing order, follow its procedures and give precise notice of the issue. For organizations building public-facing legal tools, test whether interfaces encourage unsupported legal conclusions, fabricated citations, or unauthorized practice. Access should be improved without presenting automated output as a substitute for qualified legal judgment. Focus on demonstrable filing defects, not assumptions about AI authorship. Preserve native sources, material versions, reviewer decisions, exceptions, and approvals. Verify governing authority.
Educational judicial note: Judges, court personnel, and lawyers may use these ten developments as a neutral tabletop exercise on authority verification, provenance, AI-assisted review, and evidence reconstruction. This briefing is educational analysis, not legal advice, and no commercial service is offered to a court or judge.
Commercial pathway - authorized decision-makers only: Managing Partners, Principal Lawyers, General Counsel, and authorized enterprise leaders may request a scope-defined assessment of whether a generic Sovereign Sanctuary Vault technical-fit evaluation is appropriate for a defined workflow. Air-gapping can reduce selected network-exposure pathways, but it does not itself guarantee privilege, defeat lawful process, establish authenticity, or satisfy any legal duty. No plan tier, price, capacity, certification, admissibility, or outcome is represented here.
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Author: Pouya Shafabakhsh Co-Founder, CAIO & Principal Forensic AI Auditor, Radsam Academy of AI Sovereign Governance. The Architect of North America's: Judicial Forensic AI Audit Standards, AI Governance, Risks & Compliance Standards, Air-Gapped Sovereign Sanctuary AI Audit System.
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