📌 Too Busy to Read This Week’s Editorial: “Lawyers and AI Oversight: What the VA’s Patient Safety Warning Teaches About Ethical Law Firm Technology Use!” ⚖️🤖

/ The Tech-Savvy Lawyer.Page/

Join us for an AI-powered deep dive into the ethical challenges facing legal professionals in the age of generative AI. 🤖 In this episode, we discuss our February 16, 2026, editorial, “Lawyers and AI Oversight: What the VA’s Patient Safety Warning Teaches About Ethical Law Firm Technology Use! ⚖️🤖” and explore why treating AI-generated drafts as hypotheses—not answers—is quickly becoming a survival skill for law firms of every size. We connect a real-world AI failure risk at the Department of Veterans Affairs to the everyday ways lawyers are using tools like chatbots, and we translate ABA Model Rules into practical oversight steps any practitioner can implement without becoming a programmer.

In our conversation, we cover the following

  • 00:00:00 – Why conversations about the future of law default to Silicon Valley, and why that’s a problem ⚖️

  • 00:01:00 – How a crisis at the U.S. Department of Veterans Affairs became a “mirror” for the legal profession 🩺➡️⚖️

  • 00:03:00 – “Speed without governance”: what the VA Inspector General actually warned about, and why it matters to your practice

  • 00:04:00 – From patient safety risk to client safety and justice risk: the shared AI failure pattern in healthcare and law

  • 00:06:00 – Shadow AI in law firms: staff “just trying out” public chatbots on live matters and the unseen risk this creates

  • 00:07:00 – Why not tracking hallucinations, data leakage, or bias turns risk management into wishful thinking

  • 00:08:00 – Applying existing ABA Model Rules (1.1, 1.6, 5.1, 5.2, and 5.3) directly to AI use in legal practice

  • 00:09:00 – Competence in the age of AI: why “I’m not a tech person” is no longer a safe answer 🧠

  • 00:09:30 – Confidentiality and public chatbots: how you can silently lose privilege by pasting client data into a text box

  • 00:10:30 – Supervision duties: why partners cannot safely claim ignorance of how their teams use AI

  • 00:11:00 – Candor to tribunals: the real ethics problem behind AI-generated fake cases and citations

  • 00:12:00 – From slogan to system: why “meaningful human engagement” must be operationalized, not just admired 

  • 00:12:30 – The key mindset shift: treating AI-assisted drafts as hypotheses, not answers 🧪

  • 00:13:00 – What reasonable human oversight looks like in practice: citations, quotes, and legal conclusions under stress test

  • 00:14:00 – You don’t need to be a computer scientist: the essential due diligence questions every lawyer can ask about AI 

  • 00:15:00 – Risk mapping: distinguishing administrative AI use from “safety-critical” lawyering tasks

  • 00:16:00 – High-stakes matters (freedom, immigration, finances, benefits, licenses) and heightened AI safeguards

  • 00:16:45 – Practical guardrails: access controls, narrow scoping, and periodic quality audits for AI use

  • 00:17:00 – Why governance is not “just for BigLaw” and how solos can implement checklists and simple documentation 📋

  • 00:17:45 – Updating engagement letters and talking to clients about AI use in their matters

  • 00:18:00 – Redefining the “human touch” as the safety mechanism that makes AI ethically usable at all 🤝

  • 00:19:00 – AI as power tool: why lawyers must remain the “captain of the ship” even when AI drafts at lightning speed 🚢

  • 00:20:00 – Rethinking value: if AI creates the first draft, what exactly are clients paying lawyers for?

  • 00:20:30 – Are we ready to bill for judgment, oversight, and safety instead of pure production time?

  • 00:21:00 – Final takeaways: building a practice where human judgment still has the final word over AI

RESOURCES

Mentioned in the episode

Software & Cloud Services mentioned in the conversation

🎙️ Ep. #131, Supercharging Litigation With AI: How StrongSuit Helps Lawyers Transform Research, Doc Review, and Drafting 💼⚖️

/ The Tech-Savvy Lawyer.Page/

My next guest is Justin McCallan, founder of StrongSuit, an AI-powered litigation platform built to transform how litigators handle legal research, document review, and drafting while keeping lawyers firmly in control. In this episode, Justin and I dig into practical, real-world workflows that solos, small firms, and big-firm litigators can use today and over the next few years to change the economics, pace, and strategy of litigation—without sacrificing accuracy, ethics, or the quality of advocacy.

Join Justin and me as we discuss the following three questions and more!

  1. What are the top three ways litigators should be using AI tools like StrongSuit right now to change the economics and pace of litigation without sacrificing accuracy, ethics, or quality of advocacy?

  2. What are the top three mistakes lawyers make when adopting AI for litigation, and what practical workflows help lawyers stay in the loop and use AI as a force multiplier instead of a risk? 

  3. Looking ahead to 2026 and beyond, what are the top three AI-driven workflows every litigator should master to stay competitive, and how can platforms like StrongSuit help build those capabilities into day-to-day practice? 

In our conversation, we cover the following

  • 00:00 – Welcome and guest introduction

    • Justin joins the show and shares his current tech setup at his desk. 

  • 00:00–01:00 – Justin’s current tech stack

    • Lenovo laptop, ultra-wide monitor, and regular use of StrongSuit, ChatGPT, and Gemini for different AI tasks.

    • Everyday tools: Microsoft Word and Power BI for analytics and fast decision-making.

  • 01:00–02:00 – Android vs. iPhone for AI use

    • Why Justin has been on Android for 17 years and how UI/UX familiarity often drives device choice more than AI capability.

  • 02:00–05:30 – Q1: Top three ways litigators should be using AI right now

    • Using AI for end-to-end legal research across 11 million precedential U.S. cases to build litigation outlines and identify key authorities.

    • Scaling document review so AI surfaces relevant documents and synthesizes insights while lawyers focus on strategy and judgment.

    • Leveraging AI for drafting and editing—improving style, clarity, and consistency beyond traditional spelling and grammar checks.

  • 05:30–07:30 – StrongSuit vs. basic tools like Word grammar check

    • How StrongSuit aims to “up-level” a lawyer’s writing, not just catch typos.

    • Stylistic improvements, clarity enhancements, and catching subtle inconsistencies in legal documents.

  • 06:00–08:00 – AI context limits and scaling doc review

    • Constraints of large models’ context windows (around ~1M tokens ≈ ~750 pages).

    • How StrongSuit runs multiple AI agents in parallel, each handling small page sets with heuristics to maintain cohesion and share insights.

  • 08:00–09:00 – Handling tens of thousands of documents

    • How StrongSuit can handle between roughly 10,000–50,000 pages at a time, with the ability to scale further for enterprise matters.

  • 09:00–11:30 – Origin story of StrongSuit

    • Why Justin saw a once-in-a-generation opportunity when large language models emerged and how law, with its precedent and text-heavy nature, is especially suited to AI.

    • StrongSuit’s focus on litigators: supporting lawyers from intake through trial while keeping them in the loop at every step.

  • 11:30–13:30 – From intake to brief drafting in minutes

    • Generating full litigation outlines, research, and analysis in about ten minutes, then moving directly into drafting memos, briefs, complaints, and motions.

    • StrongSuit’s long-term goal: automating 50–99% of major litigation workflows by the end of 2026 while preserving lawyer control and judgment.

  • 12:00–14:30 – How StrongSuit tackles hallucinations

    • Building a full database of all precedential U.S. cases enriched with metadata: parties, summaries, holdings, and more.

    • Validating citations by checking whether the Bluebook citation actually exists in StrongSuit’s case database before surfacing it to the user.

    • Why lawyers should still review cases on-platform before filing, even when AI has filtered out hallucinations.

  • 14:30–16:30 – Coverage and jurisdictions

    • Coverage of all U.S. jurisdictions, federal and state, focused on precedential cases.

    • Handling most regulations from administrative agencies, and limits around local ordinances.

    • Uploading your own case files and using complaints and prior research as inputs into StrongSuit workflows.

  • 15:00–17:00 – Security and confidentiality for litigators

    • SOC 2 compliance and industry-standard encryption at rest and in transit.

    • No model training on user data.

    • Optional end-to-end encryption that can even prevent developers from accessing case content, using local encryption keys.

  • 16:30–20:30 – Q2: Top mistakes lawyers make when adopting AI for litigation

    • Mistake #1: Talking about AI instead of diving in with structured experiments and sanitized documents.

    • Using a framework to identify high-impact tasks: high volume, repetitive work, and heavy data/analysis (e.g., doc review, research, contract drafting).

    • How to shortlist tools: look for SOC 2, real product depth, awards, and a focus on your specific workflows.

    • Mistake #2: Expecting immediate mastery instead of moving through predictable adoption stages—from learning the tool, to daily use, to stringing workflows together.

  • 20:30–22:30 – Building firm-wide AI workflows over time

    • Moving from isolated experiments to integrated, low-friction workflows, such as automatic intake-to-research pipelines.

    • Using client intake audio or transcripts to automatically extract facts, issues, and research paths.

  • 22:30–24:30 – Time constraints and “no-time” lawyers

    • Why lawyers don’t need to be “technical” to use StrongSuit.

    • Reframing AI as text-based tools where lawyers’ writing skills and analytical thinking are assets, not obstacles. 

  • 24:00–26:00 – Practical workflows beyond intake

    • Using AI to prepare for expert depositions, including reviewing valuation analyses, flagging departures from market consensus, and generating targeted questions.

    • Reinforcing the value of AI-enhanced legal research and drafting as core litigation workflows.

  • 26:00–29:30 – Q3: 2026 and beyond – AI-driven workflows every litigator should master

    • Rapid improvement of baseline models (e.g., jumping from single-digit to high double-digit performance on difficult benchmarks year over year). 

    • The idea of “tipping points,” where small performance gains turn AI from marginally useful to essential in specific tasks.

    • Why legal research is a great training ground for understanding where AI excels, where it falls short, and how to divide labor between human and machine.

    • The value of learning basic prompting skills to get more from AI systems, even when platforms offer visual workflows.

  • 29:30–32:30 – Will workflows actually change—or just get better?

    • Why Justin expects familiar litigation workflows (doc review, research, drafting) to remain structurally similar, but become far faster and more sophisticated.

    • AI agents handling the grind work while lawyers focus on synthesis, judgment, and strategy.

    • A future where “AI + lawyer vs. AI + lawyer” resembles high-level chess: same rules, but much deeper thinking on both sides.

  • 32:30–End – Where to find Justin and StrongSuit

    • How to connect with Justin and learn more about StrongSuit’s litigation tools.

Resources

Connect with Justin

Hardware mentioned in the conversation

Software & Cloud Services mentioned in the conversation

MTC: Lawyers and AI Oversight: What the VA’s Patient Safety Warning Teaches About Ethical Law Firm Technology Use! ⚖️🤖

/ The Tech-Savvy Lawyer.Page/

Human-in-the-loop is the point: Effective oversight happens where AI meets care—aligning clinical judgment, privacy, and compliance with real-world workflows.

The Department of Veterans Affairs’ experience with generative AI is not a distant government problem; it is a mirror held up to every law firm experimenting with AI tools for drafting, research, and client communication. I recently listened to an interview by Terry Gerton of the Federal News Network of Charyl Mason, Inspector General of the Department of Veterans Affairs, “VA rolled out new AI tools quickly, but without a system to catch mistakes, patient safety is on the line” and gained some insights on how lawyers can learn from this perhaps hastilly impliment AI program. VA clinicians are using AI chatbots to document visits and support clinical decisions, yet a federal watchdog has warned that there is no formal mechanism to identify, track, or resolve AI‑related risks—a “potential patient safety risk” created by speed without governance. In law, that same pattern translates into “potential client safety and justice risk,” because the core failure is identical: deploying powerful systems without a structured way to catch and correct their mistakes.

The oversight gap at the VA is striking. There is no standardized process for reporting AI‑related concerns, no feedback loop to detect patterns, and no clearly assigned responsibility for coordinating safety responses across the organization. Clinicians may have helpful tools, but the institution lacks the governance architecture that turns “helpful” into “reliably safe.” When law firms license AI research platforms, enable generative tools in email and document systems, or encourage staff to “try out” chatbots on live matters without written policies, risk registers, or escalation paths, they recreate that same governance vacuum. If no one measures hallucinations, data leakage, or embedded bias in outputs, risk management has given way to wishful thinking.

Existing ethics rules already tell us why that is unacceptable. Under ABA Model Rule 1.1, competence now includes understanding the capabilities and limitations of AI tools used in practice, or associating with someone who does. Model Rule 1.6 requires lawyers to critically evaluate what client information is fed into self‑learning systems and whether informed consent is required, particularly when providers reuse inputs for training. Model Rules 5.1, 5.2, and 5.3 extend these obligations across partners, supervising lawyers, and non‑lawyer staff: if a supervised lawyer or paraprofessional relies on AI in a way that undermines client protection, firm leadership cannot plausibly claim ignorance. And rules on candor to tribunals make clear that “the AI drafted it” is never a defense to filing inaccurate or fictitious authority.

Explaining the algorithm to decision-makers: Oversight means making AI risks understandable to judges, boards, and the public—clearly and credibly.

What the VA story adds is a vivid reminder that effective AI oversight is a system, not a slogan. The inspector general emphasized that AI can be “a helpful tool” only if it is paired with meaningful human engagement: defined review processes, clear routes for reporting concerns, and institutional learning from near misses. For law practice, that points directly toward structured workflows. AI‑assisted drafts should be treated as hypotheses, not answers. Reasonable human oversight includes verifying citations, checking quotations against original sources, stress‑testing legal conclusions, and documenting that review—especially in high‑stakes matters involving liberty, benefits, regulatory exposure, or professional discipline.

For lawyers with limited to moderate tech skills, this should not be discouraging; done correctly, AI governance actually makes technology more approachable. You do not need to understand model weights or training architectures to ask practical questions: What data does this tool see? When has it been wrong in the past? Who is responsible for catching those errors before they reach a client, a court, or an opposing party? Thoughtful prompts, standardized checklists for reviewing AI output, and clear sign‑off requirements are all well within reach of every practitioner.

The VA’s experience also highlights the importance of mapping AI uses and classifying their risk. In health care, certain AI use cases are obviously safety‑critical; in law, the parallel category includes anything that could affect a person’s freedom, immigration status, financial security, public benefits, or professional license. Those use cases merit heightened safeguards: tighter access control, narrower scoping of AI tasks, periodic sampling of outputs for quality, and specific training for the lawyers who use them. Importantly, this is not a “big‑law only” discipline. Solo and small‑firm lawyers can implement proportionate governance with simple written policies, matter‑level notes showing how AI was used, and explicit conversations with clients where appropriate.

Critically, AI does not dilute core professional responsibility. If a generative system inserts fictitious cases into a brief or subtly mischaracterizes a statute, the duty of candor and competence still rests squarely on the attorney who signs the work product. The VA continues to hold clinicians responsible for patient care decisions, even when AI is used as a support tool; the law should be no different. That reality should inform how lawyers describe AI use in engagement letters, how they supervise junior lawyers and staff, and how they respond when AI‑related concerns arise. In some situations, meeting ethical duties may require forthright client communication, corrective filings, and revisions to internal policies.

AI oversight starts at the desk: Lawyers must be able to interrogate model outputs, data quality, and risk signals—before technology impacts patient care.

The practical lesson from the VA’s AI warning is straightforward. The “human touch” in legal technology is not a nostalgic ideal; it is the safety mechanism that makes AI ethically usable at all. Lawyers who embrace AI while investing in governance—policies, training, and oversight calibrated to risk—will be best positioned to align with the ABA’s evolving guidance, satisfy courts and regulators, and preserve hard‑earned client trust. Those who treat AI as a magic upgrade and skip the hard work of oversight are, knowingly or not, accepting that their clients may become the test cases that reveal where the system fails. In a profession grounded in judgment, the real innovation is not adopting AI; it is designing a practice where human judgment still has the final word.

MTC

TSL.P Labs 🧪: Everyday Tech, Extraordinary Sanctions: How Courts Are Cracking Down on Fake AI Evidence in 2026 ⚖️🤖

/ The Tech-Savvy Lawyer.Page/

Everyday devices can capture extraordinary evidence, but the same tools can also manufacture convincing fakes. 🎥⚖️ In this episode, we unpack our February 9, 2026, editorial on how courts are punishing fake digital and AI-generated evidence, then translate the risk into practical guidance for lawyers and legal teams.

You’ll hear why judges are treating authenticity as a frontline issue, what ethical duties get triggered when AI touches evidence or briefing, and how a simple “authenticity playbook” can help you avoid career-ending mistakes. ✅

In our conversation, we cover the following

  • 00:00:00 – Preview: From digital discovery to digital deception, and the question of what happens when your “star witness” is actually a hallucination or deepfake 🚨

  • 00:00:20 – Introducing the editorial “Everyday Tech, Extraordinary Evidence Again: How Courts Are Punishing Fake Digital and AI Data.” 📄

  • 00:00:40 – Welcome to the Tech-Savvy Lawyer.Page Labs Initiative and this AI Deep Dive Roundtable 🎙️

  • 00:01:00 – Framing the episode: flipping last month’s optimism about smartphones, dash cams, and wearables as case-winning “silent witnesses” to their dark mirror—AI-fabricated evidence 🌗

  • 00:01:30 – How everyday devices and AI tools can both supercharge litigation strategy and become ethical landmines under the ABA Model Rules ⚖️

  • 00:02:00 – Panel discussion opens: revisiting last month’s “Everyday Tech, Extraordinary Evidence” AI bonus and the optimism around smartphone, smartwatch, and dash cam data as unbiased proof 📱⌚🚗

  • 00:02:30 – Remembering cases like the Minnesota shooting and why these devices were framed as “ultimate witnesses” if the data is preserved quickly enough 🕒

  • 00:03:00 – The pivot: same tools, new threats—moving from digital discovery to digital deception as deepfakes and hallucinations enter the evidentiary record 🤖

  • 00:03:30 – Setting the “mission” for the episode: examining how courts are reacting to AI-generated “slop” and deepfakes, with an increasingly aggressive posture toward sanctions 💣

  • 00:04:00 – Why courts are on high alert: the “democratization of deception,” falling costs of convincing video fakes, and the collapse of the old presumption that “pictures don’t lie” 🎬

  • 00:04:30 – Everyday scrutiny: judges now start with “Where did this come from?” and demand details on who created the file, how it was handled, and what the metadata shows 🔍

  • 00:05:00 – Metadata explained as the “data about the data”—timestamps, software history, edit traces—and how it reveals possible AI manipulation 🧬

  • 00:06:00 – Entering the “sanction phase”: why we are beyond warnings and into real penalties for mishandling or fabricating digital and AI evidence 🚫

  • 00:06:30 – Horror Story #1 (Mendon v. Cushman & Wakefield, Cal. Super. Ct. 2025): plaintiffs submit videos, photos, and screenshots later determined to be deepfakes created or altered with generative AI 🧨

  • 00:07:00 – Judge Victoria Kakowski’s response: finding that the deepfakes undermined the integrity of judicial proceedings and imposing terminating sanctions—“death penalty” for the lawsuit ⚖️

  • 00:07:30 – How a single deepfake “poisons the well,” destroying the court’s trust in all of a party’s submissions and forfeiting their right to the court’s time 💥

  • 00:08:00 – Horror Story #2 (S.D.N.Y. 2023): the New York “hallucinating lawyer” case where six imaginary cases generated by ChatGPT were filed without verification 📚

  • 00:08:30 – Rule 11 sanctions and humiliation: Judge Castel’s order, monetary penalty, and the requirement to send apology letters to real judges whose names were misused ✉️

  • 00:09:00 – California follow-on: appellate lawyer Amir Mustaf files an appeal brief with 21 fake citations, triggering a 10,000-dollar sanction and a finding that he did not read or verify his own filing 💸

  • 00:09:30 – Courts’ reasoning: outsourcing your job to an AI tool is not just being wrong—it is wasting judicial resources and taxpayer money 🧾

  • 00:10:00 – Do we need new laws? Why Michael argues that existing ABA Model Rules already provide the safety rails; the task is to apply them to AI and digital evidence, not to reinvent them 🧩

  • 00:10:20 – Rule 1.1 (competence): why “I’m not a tech person” is no longer a viable excuse if you use AI to enhance video or draft briefs without understanding or verifying the output 🧠

  • 00:11:00 – Rule 1.6 (confidentiality): the ethical minefield of uploading client dash cam video or wearable medical data to consumer-grade AI tools and risking privilege leakage ☁️

  • 00:11:30 – Training risk: how client data can end up in model training sets and why “quick AI summaries” can inadvertently expose secrets 🔐

  • 00:12:00 – Rules 3.3 and 4.1 (candor and truthfulness): presenting AI-altered media as original or failing to verify AI output can now be treated as misrepresentation 🤥

  • 00:12:30 – Rules 5.1–5.3 (supervision): why partners and supervising lawyers remain on the hook for juniors, staff, and vendors who misuse AI—even if they didn’t personally type the prompts 🧑‍💼

  • 00:13:00 – Authenticity Playbook, Step 1: mindset shift—never treat AI as a “silent co-counsel”; instead, treat it like a very eager, very inexperienced, slightly drunk intern who always needs checking 🍷🤖

  • 00:13:30 – Authenticity Playbook, Step 2: preserve the original and disclose any AI enhancement; build a clean chain of custody while staying transparent about edits 🎞️

  • 00:14:00 – Authenticity Playbook, Step 3: using forensic vendors as authenticity firewalls—experts who can certify that metadata and visual cues show no AI manipulation 🛡️

  • 00:14:30 – Authenticity Playbook, Step 4: “train with fear” by showing your team real orders, sanctions, and public shaming rather than relying on abstract ethics lectures ⚠️

  • 00:15:00 – Authenticity Playbook, Step 5: documenting verification steps—logging files, tools, and checks so you can demonstrate good faith if a judge questions your evidence 📝

  • 00:16:00 – Bigger picture: the era of easy, unchallenged digital evidence is over; mishandled tech can now produce “extraordinary sanctions” instead of extraordinary evidence 🧭

  • 00:16:30 – Authenticity as “the moral center of digital advocacy”: if you cannot vouch for your digital evidence, you are failing in your role as an advocate 🏛️

  • 00:17:00 – Future risk: as deepfakes become perfect and nearly impossible to detect with the naked eye, forensic expertise may become a prerequisite for trusting any digital evidence 🔬

  • 00:17:30 – “Does truth get a price tag?”—whether justice becomes a luxury product if only wealthy parties can afford authenticity firewalls and expert validation 💼

  • 00:18:00 – Closing reflections: fake evidence, real consequences, and the call to verify sources and check metadata before you file ✅

  • 00:18:30 – Closing: invitation to visit Tech-Savvy Lawyer.Page for the full editorial, resources, and to like, subscribe, and share with colleagues who need to stay ahead of legal tech innovation 🌐

Resources

Cases

ABA Model Rules

  • Rule 1.1 (Competence): You must understand the benefits and risks of relevant technology, which now clearly includes generative AI and deepfake tools.⚖️ Using AI to draft or “enhance” without checking the output is not a harmless shortcut—it is a competence problem.

    • Comment 8’s duty of technological competence; the new sanctions landscape simply clarifies the stakes.📚

  • Rule 1.6 (Confidentiality): Uploading client videos, wearable logs, or sensitive communications to consumer‑grade AI sites can expose them to unknown retention and training practices, risking confidentiality violations.🔐

  • Rule 3.3 (Candor to the Tribunal) and Rule 4.1 (Truthfulness): Presenting AI‑altered video or fake citations as if they were genuine is the very definition of misrepresentation, as the New York and California sanction orders make clear.⚠️ Even negligent failure to verify can be treated harshly once the court’s patience for AI excuses runs out.

  • Rules 5.1–5.3 (Supervision): Supervising lawyers must ensure that associates, law clerks, and vendors understand that AI outputs are starting points, not trustworthy final products, and that fake or manipulated digital evidence will not be tolerated.👥

ANNOUNCEMENT: My Book, “The Lawyer’s Guide to Podcasting,” is Amazon #1 New Release (Law Office Technology)

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I’m excited to report that The Lawyer’s Guide to Podcasting ranked #1 as a New Release in Amazon’s Law Office Technology category for the week of February 07, 2026, and sales have already doubled since last month. 🎙️📈

For lawyers with limited-to-moderate tech skills, the book focuses on practical, repeatable workflows for launching and sustaining a compliant podcast presence. ⚖️💡

As you plan content, remember ABA Model Rule 1.1 (technology competence) and the related duties of confidentiality (Rule 1.6) and communications about services (Rule 7.1): use secure tools, avoid accidental client disclosures, and ensure marketing statements are accurate. 🔐✅

Get your copy today! 📘🚀

 
 
amazon link

TSL.P Labs 🧪: Legal Tech Wars, Client Data, and Your Law License: An AI-Powered Ethics Deep Dive ⚖️🤖

/ The Tech-Savvy Lawyer.Page/

📌 To Busy to Read This Week’s Editorial?

Join us for an AI-powered deep dive into the ethical challenges facing legal professionals in the age of generative AI. 🤖 In this Tech-Savvy Lawyer Page Labs Initiative episode, AI co-hosts walk through how high‑profile “legal tech wars” between practice‑management vendors and AI research startups can push your client data into the litigation spotlight and create real ethics exposure under ABA Model Rules 1.1, 1.6, and 5.3.

We’ll explore what happens when core platforms face federal lawsuits, why discovery and forensic audits can put confidential matters in front of third parties, and how API lockdowns, stalled product roadmaps, and forced sales can grind your practice operations to a halt. More importantly, you’ll get a clear five‑step action plan—inventorying your tech stack, confirming data‑export rights, mapping backup providers, documenting diligence, and communicating with clients—that works even if you consider yourself “moderately tech‑savvy” at best.

Whether you’re a solo, a small‑firm practitioner, in‑house, or simply AI‑curious, this conversation will help you evaluate whether you are the supervisor of your legal tech—or its hostage. 🔐

👉 Listen now and decide: are you supervising your legal tech—or are you its hostage?

In our conversation, we cover the following

  • 00:00:00 – Setting the stage: Legal tech wars, “Godzilla vs. Kong,” and why vendor lawsuits are not just Silicon Valley drama for spectators.

  • 00:01:00 – Introducing the Tech-Savvy Lawyer Page Labs Initiative and the use of AI-generated discussions to stress-test legal tech ethics in real-world scenarios.

  • 00:02:00 – Who’s fighting and why it matters: Clio as the “nervous system” of many firms versus Alexi as the “brainy intern” of AI legal research.

  • 00:03:00 – The client data crossfire: How disputes over data access and training AI tools turn your routine practice data into high-stakes litigation evidence.

  • 00:04:00 – Allegations in the Clio–Alexi dispute, from improper data access to claims of anti-competitive gatekeeping of legal industry data.

  • 00:05:00 – Visualizing risk: Client files as sandcastles on a shelled beach and why this reframes vendor fights as ethics issues, not IT gossip.

  • 00:06:00 – ABA Model Rule 1.1 (Competence): What “technology competence” really entails and why ignorance of vendor instability is no longer defensible.

  • 00:07:00 – Continuity planning as competence: Injunctions, frozen servers, vendor shutdowns, and how missed deadlines can become malpractice.

  • 00:08:00 – ABA Model Rule 1.6 (Confidentiality): The “danger zone” of treating the cloud like a bank vault and misunderstanding who really holds the key.

  • 00:09:00 – Discovery risk explained: Forensic audits, third‑party access, protective orders that fail, and the cascading impact on client secrets.

  • 00:10:00 – Data‑export rights as your “escape hatch”: Why “usable formats” (CSV, PDF) matter more than bare contractual promises.

  • 00:11:00 – Practical homework: Testing whether you can actually export your case list today, not during a crisis.

  • 00:12:00 – ABA Model Rule 5.3 (Supervision): Treating software vendors like non‑lawyer assistants you actively supervise rather than passive utilities.

  • 00:13:00 – Asking better questions: Uptime, security posture, and whether your vendor is using your data in its own defense.

  • 00:14:00 – Operational friction: Rising subscription costs, API lockdowns, broken integrations, and the return of manual copy‑pasting.

  • 00:15:00 – Vaporware and stalled product roadmaps: How litigation diverts engineering resources away from features you are counting on.

  • 00:16:00 – Forced sales and 30‑day shutdown notices: Data‑migration nightmares under pressure and why waiting is the riskiest strategy.

  • 00:17:00 – The five‑step moderate‑tech action plan: Inventory dependencies, review contracts, map contingencies, document diligence, and communicate with nuance.

  • 00:18:00 – Turning risk management into a client‑facing strength and part of your value story in pitches and ongoing relationships.

  • 00:19:00 – Reframing legal tech tools as members of your legal team rather than invisible utilities.

  • 00:20:00 – “Supervisor or hostage?”: The closing challenge to check your contracts, your data‑export rights, and your practical ability to “fire” a vendor.

Resources

Mentioned in the episode

Software & Cloud Services mentioned in the conversation

#LegalTech #AIinLaw #LegalEthics #Cybersecurity #LawPracticeManagement

Word of the Week: Vendor Risk Management for Law Firms in 026: Lessons from the Clio–Alexi CRM Fight ⚖️💻

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Clio vs. Alexi: CRM Litigation COULD THREATEN Law Firm Data

“Vendor risk management” is no longer an IT buzzword; it is now a core law‑practice skill for any attorney who relies on cloud‑based tools, CRMs, or AI‑driven research platforms.⚙️📊 The Tech‑Savvy Lawyer.Page’s February 2, 2026 editorial on the Clio–Alexi CRM litigation showed how a dispute between legal‑tech companies can reach straight into your client list, calendars, and workflows.⚖️🧾

In that piece, Clio and Alexi’s legal fight over data, AI training, and competition was framed not as “tech drama,” but as a live test of how well your firm understands its dependencies on vendors that control client‑related information.🧠📂 When the platform that hosts your CRM, matter data, or AI research tools becomes embroiled in high‑stakes litigation, your risk profile changes even if you never set foot in that courtroom.⚠️🏛️

Under ABA Model Rule 1.1, competence includes a practical understanding of the technology that underpins your practice, and that now clearly includes vendor risk.📚💡 You do not have to reverse‑engineer APIs, yet you should be able to answer basic questions: Which vendors are mission‑critical, what data do they hold, how would you respond if one faced an injunction, outage, or rushed acquisition.🧩🚨 That is vendor risk management at a level that is realistic for lawyers with limited to moderate tech skills.🙂🧑‍💼

LawyerS NEED TO Build Vendor Risk Plan for Ethical Compliance

Model Rule 1.6 on confidentiality sits at the center of this analysis, because litigation involving a vendor can expose or pressure the systems that hold client information.🔐📁 Our February 2 article emphasized the need to know where your data is hosted, what the contracts say about subpoenas and law‑enforcement requests, and how quickly you can export data if your ethics analysis changes.⏱️📄 Vendor risk management, therefore, includes reviewing terms of service, capturing “current” versions of online agreements, and documenting export rights and notice obligations.📝🧷

Model Rule 5.3 requires reasonable efforts to ensure that non‑lawyer assistance is compatible with your professional duties, and 2026 legal‑tech commentary increasingly treats vendors as supervised extensions of the law office.🧑‍⚖️🤝 CRMs, AI research tools, document‑automation platforms, and e‑billing systems all act as non‑lawyer assistants for ethics purposes, which means you must screen them before adoption, monitor them for material changes, and reassess when events like the Clio–Alexi dispute surface.📡📊

Recent legal‑tech reporting has described 2026 as a reckoning year for vendors, with AI‑driven tools under heavier regulatory and client scrutiny, which makes disciplined vendor risk management a competitive advantage rather than a burden.📈🤖 Practical steps include maintaining a simple vendor inventory, ranking systems by criticality, reviewing cyber and data‑security representations, and identifying a plausible backup provider for each crucial function.📋🛡️

LAWYERS NEED TO SHIELD THEIR CLIENT DATA FROM CRM LITIGATION AS MUCH AS THEY NEED TO PROTECT THEIR EthicS DUTIES!

Vendor risk management, properly understood, turns your technology stack into part of your professional judgment instead of a black box that “IT” owns alone.🧱🧠 For solo and small‑firm lawyers, that shift can feel incremental rather than overwhelming: start by reading the Clio–Alexi editorial, pull your top three vendor contracts, and ask whether they let you protect competence, confidentiality, and continuity if your vendors suddenly become the ones needing legal help.🧑‍⚖️🧰

MTC: Clio–Alexi Legal Tech Fight: What CRM Vendor Litigation Means for Your Law Firm, Client Data and ABA Model Rule Compliance ⚖️💻

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Competence, Confidentiality, Vendor Oversight!

When the companies behind your CRM and AI research tools start suing each other, the dispute is not just “tech industry drama” — it can reshape the practical and ethical foundations of your practice. At a basic to moderate level, the Clio–Alexi fight is about who controls valuable legal data, how that data can be used to power AI tools, and whether one side is using its market position unfairly. Clio (a major practice‑management and CRM platform) is tied to legal research tools and large legal databases. Alexi is a newer AI‑driven research company that depends on access to caselaw and related materials to train and deliver its products. In broad strokes, one side claims the other misused or improperly accessed data and technology; the other responds that the litigation is “sham” or anticompetitive, designed to limit a smaller rival and protect a dominant ecosystem. There are allegations around trade secrets, data licensing, and antitrust‑style behavior. None of that may sound like your problem — until you remember that your client data, workflows, and deadlines live inside tools these companies own, operate, or integrate with.

For lawyers with limited to moderate technology skills, you do not need to decode every technical claim in the complaints and counterclaims. You do, however, need to recognize that vendor instability, lawsuits, and potential regulatory scrutiny can directly touch: your access to client files and calendars, the confidentiality of matter information stored in the cloud, and the long‑term reliability of the systems you use to serve clients and get paid. Once you see the dispute in those terms, it becomes squarely an ethics, risk‑management, and governance issue — not just “IT.”

ABA Model Rule 1.1: Competence Now Includes Tech and Vendor Risk

Model Rule 1.1 requires “competent representation,” which includes the legal knowledge, skill, thoroughness, and preparation reasonably necessary for the representation. In the modern practice environment, that has been interpreted to include technology competence. That does not mean you must be a programmer. It does mean you must understand, in a practical way, the tools on which your work depends and the risks they bring.

If your primary CRM, practice‑management system, or AI research tool is operated by a company in serious litigation about data, licensing, or competition, that is a material fact about your environment. Competence today includes: knowing which mission‑critical workflows rely on that vendor (intake, docketing, conflicts, billing, research, etc.); having at least a baseline sense of how vendor instability could disrupt those workflows; and building and documenting a plan for continuity — how you would move or access data if the worst‑case scenario occurred (for example, a sudden outage, injunction, or acquisition). Failing to consider these issues can undercut the “thoroughness and preparation” the Rule expects. Even if your firm is small or mid‑sized, and even if you feel “non‑technical,” you are still expected to think through these risks at a reasonable level.

ABA Model Rule 1.6: Confidentiality in a Litigation Spotlight

Model Rule 1.6 is often front of mind when lawyers think about cloud tools, and the Clio–Alexi dispute reinforces why. When a technology company is sued, its systems may become part of discovery. That raises questions like: what types of client‑related information (names, contact details, matter descriptions, notes, uploaded files) reside on those systems; under what circumstances that information could be accessed, even in redacted or aggregate form, by litigants, experts, or regulators; and how quickly and completely you can remove or export client data if a risk materializes.

You remain the steward of client confidentiality, even when data is stored with a third‑party provider. A reasonable, non‑technical but diligent approach includes: understanding where your data is hosted (jurisdictions, major sub‑processors, data‑center regions); reviewing your contracts or terms of service for clauses about data access, subpoenas, law‑enforcement or regulatory requests, and notice to you; and ensuring you have clearly defined data‑export rights — not only if you voluntarily leave, but also if the vendor is sold, enjoined, or materially disrupted by litigation. You are not expected to eliminate all risk, but you are expected to show that you considered how vendor disputes intersect with your duty to protect confidential information.

ABA Model Rule 5.3: Treat Vendors as Supervised Non‑Lawyer Assistants

ABA Rules for Modern Legal Technology can be a factor when legal tech companies fight!

Model Rule 5.3 requires lawyers to make reasonable efforts to ensure that non‑lawyer assistants’ conduct is compatible with professional obligations. In 2026, core technology vendors — CRMs, AI research platforms, document‑automation tools — clearly fall into this category.

You are not supervising individual programmers, but you are responsible for: performing documented diligence before adopting a vendor (security posture, uptime, reputation, regulatory or litigation history); monitoring for material changes (lawsuits like the Clio–Alexi matter, mergers, new data‑sharing practices, or major product shifts); and reassessing risk when those changes occur and adjusting your tech stack or contracts accordingly. A litigation event is a signal that “facts have changed.” Reasonable supervision in that moment might mean: having someone (inside counsel, managing partner, or a trusted advisor) read high‑level summaries of the dispute; asking the vendor for an explanation of how the litigation affects uptime, data security, and long‑term support; and considering whether you need contractual amendments, additional audit rights, or a backup plan with another provider. Again, the standard is not perfection, but reasoned, documented effort.

How the Clio–Alexi Battle Can Create Problems for Users

A dispute at this scale can create practical, near‑term friction for everyday users, quite apart from any final judgment. Even if the platforms remain online, lawyers may see more frequent product changes, tightened integrations, shifting data‑sharing terms, or revised pricing structures as companies adjust to litigation costs and strategy. Any of these changes can disrupt familiar workflows, create confusion around where data actually lives, or complicate internal training and procedures.

There is also the possibility of more subtle instability. For example, if a product roadmap slows down or pivots under legal pressure, features that firms were counting on — for automation, AI‑assisted drafting, or analytics — may be delayed or re‑scoped. That can leave firms who invested heavily in a particular tool scrambling to fill functionality gaps with manual workarounds or additional software. None of this automatically violates any rule, but it can introduce operational risk that lawyers must understand and manage.

In edge cases, such as a court order that forces a vendor to disable key features on short notice or a rapid sale of part of the business, intense litigation can even raise questions about long‑term continuity. A company might divest a product line, change licensing models, or settle on terms that affect how data can be stored, accessed, or used for AI. Firms could then face tight timelines to accept new terms, migrate data, or re‑evaluate how integrated AI features operate on client materials. Without offering any legal advice about what an individual firm should do, it is fair to say that paying attention early — before options narrow — is usually more comfortable than reacting after a sudden announcement or deadline.

Practical Steps for Firms at a Basic–Moderate Tech Level

You do not need a CIO to respond intelligently. For most firms, a short, structured exercise will go a long way:

Practical Tech Steps for Today’s Law Firms

  1. Inventory your dependencies. List your core systems (CRM/practice management, document management, time and billing, conflicts, research/AI tools) and note which vendors are in high‑profile disputes or under regulatory or antitrust scrutiny.

  2. Review contracts for safety valves. Look for data‑export provisions, notice obligations if the vendor faces litigation affecting your data, incident‑response timelines, and business‑continuity commitments; capture current online terms.

  3. Map a contingency plan. Decide how you would export and migrate data if compelled by ethics, client demand, or operational need, and identify at least one alternative provider in each critical category.

  4. Document your diligence. Prepare a brief internal memo or checklist summarizing what you reviewed, what you concluded, and what you will monitor, so you can later show your decisions were thoughtful.

  5. Communicate without alarming. Most clients care about continuity and confidentiality, not vendor‑litigation details; you can honestly say you monitor providers, have export and backup options, and have assessed the impact of current disputes.

From “IT Problem” to Core Professional Skill

The Clio–Alexi litigation is a prominent reminder that law practice now runs on contested digital infrastructure. The real message for working lawyers is not to flee from technology but to fold vendor risk into ordinary professional judgment. If you understand, at a basic to moderate level, what the dispute is about — data, AI training, licensing, and competition — and you take concrete steps to evaluate contracts, plan for continuity, and protect confidentiality, you are already practicing technology competence in a way the ABA Model Rules contemplate. You do not have to be an engineer to be a careful, ethics‑focused consumer of legal tech. By treating CRM and AI providers as supervised non‑lawyer assistants, rather than invisible utilities, you position your firm to navigate future lawsuits, acquisitions, and regulatory storms with far less disruption. That is good risk management, sound ethics, and, increasingly, a core element of competent lawyering in the digital era. 💼⚖️

📽️ BONUS Labs 🧪 Initiative: Tech-Savvy Lawyer on Law Practice Today Podcast — Essential Trust Account Tips for Solo & Small Law Firms w/ Terrell Turner (Copy)

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For those who prefer video over plain audio, enjoy this take on my guest appearance on Law Practice Today Podcast!

🙏 Special Thanks to Terrell Turner and the ABA for having me on the Law Practice Today Podcast, produced by the Law Practice Division of the American Bar Association. We have an important discussion on trust account management. We cover essential insights on managing trust accounts using online services. This episode has been edited for time, but no information was altered. We are grateful to the ABA and the Law Practice Today Podcast for allowing us to share this valuable conversation with our audience.

🎯 Join Terrell and me as we discuss the following three questions and more!

  1. What precautions should lawyers using online services to manage trust accounts be aware of?

  2. How can solo and small firm attorneys find competent bookkeepers who understand legal trust accounting?

  3. What security measures should attorneys implement when using online payment processors for client funds?

⏱️ In our conversation, we cover the following:

00:00 – Introduction & Preview: Trust Accounts in the Digital Age

01:00 – Welcome to the Law Practice Today Podcast

01:30 – Today's Topic: Online Services for Payments

02:00 – Guest Introduction: Michael D.J. Eisenberg's Background

03:00 – Michael's Experience with Trust Accounts

04:00 – Challenges for Solo and Small Practitioners

05:00 – Ensuring Security in Online Services

06:00 – Questions to Ask Online Payment Providers

07:00 – Password Security & Two-Factor Authentication

08:00 – Finding a Competent Legal Bookkeeper

09:00 – Why 8AM Law Pay Works for Attorneys

10:00 – Daily Monitoring of Trust Accounts

11:00 – FDIC Insurance & Silicon Valley Bank Lessons

13:00 – Researching Trust Account Best Practices

15:00 – Closing Remarks & Podcast Information

📚 Resources

🔗 Connect with Terrell

💼 LinkedIn: https://www.linkedin.com/in/terrellturner/

🌐 Website: https://www.tlturnergroup.com/

🎙️ Law Practice Today Podcast – https://lawpracticetoday.buzzsprout.com

📰 Mentioned in the Episode

💻 Software & Cloud Services Mentioned in the Conversation

  • 8AM Law Pay – Legal payment processing designed for trust account compliance – https://www.8am.com/lawpay/

  • 1Password – Password manager for generating and syncing complex passwords – https://1password.com/

  • LastPass – Mentioned as a password manager with noted security concerns – https://www.lastpass.com/

ANNOUNCEMENT: The Lawyer’s Guide to Podcasting Is Here: A Practical, Ethical Launch Plan for Busy Lawyers 🎙️⚖️

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I’m excited to share! The wait is over! The Lawyer’s Guide to Podcasting is officially released. 🎉🎙️ This book is built for lawyers, paralegals, and legal professionals who want a clear, practical path to launching a podcast—without needing to be “techy” to get it right.

Podcasting has become one of the most effective ways to build trust at scale. People want more than ads. They want a real voice. They want context. They want clarity. A podcast lets you educate, connect, and show your professional judgment in a way a website cannot. It also gives prospective clients a low-pressure way to get to know you before they ever call. 📈🤝

This guide covers the full podcast lifecycle in plain language. You will learn how to pick a topic that fits your goals and schedule. You will learn the most useful show formats for legal audiences, including solo episodes, interviews, storytelling, and educational series. You will also learn what to buy (and what to skip) when building your gear setup. That includes microphones, headphones, webcams, lighting, and basic acoustic improvements that matter in real offices. 🎧🎥💡

QR Code for 📚 purchase on amazon

Software matters too. In this book, I explain beginner and pro options for recording and editing. It also covers remote recording tools and simple video workflows for YouTube and modern platforms. You will get a clear explanation of podcast hosting and distribution, including how RSS feeds deliver your episodes to directories like Apple Podcasts and Spotify. 📲🌍

A major focus of this book is professional responsibility. Lawyers must avoid accidental legal advice. Lawyers must avoid creating unintended attorney-client relationships. Lawyers must also watch multi-jurisdictional issues and advertising rules. This guide addresses those risks directly and gives practical guardrails you can use in real episodes. 🛡️📜

You will also learn how to use AI efficiently and ethically. AI can save time on transcripts, show notes, clips, and repurposed content. It can also create risk if you feed it sensitive data or publish unverified output. The book offers a workflow-first approach that protects confidentiality and supports accuracy. ✅🤖

The Lawyer’s Guide to Podcasting is part of the Lawyers Tech Guide (LTG) series from Michael D.J. Eisenberg, creator of The Tech-Savvy Lawyer.Page. The mission is simple: use technology to communicate clearly, serve people better, and reclaim time. ⏳✨

Ready to launch?
You are just one click away!

🔗 Purchase here on Amazon 🔗