⭐ First Five-Star Amazon Review for “The Lawyer’s Guide to Podcasting” – Why Tech-Savvy Lawyers Should Care About ABA Ethics, Client Trust, and Smart Marketing 🎙️⚖️

“The Lawyer’s Guide to Podcasting” by your favorite blogger/podcaster just earned its first five-star Amazon review, and it’s a milestone worth your attention. 🎉📘 The reviewer highlights what many of us in legal tech have been saying: podcasting is no longer a fringe hobby; it is a strategic, ethics-aware marketing channel for modern law practice. 🎙️

For lawyers with limited to moderate tech skills, this book demystifies microphones, workflows, and publishing tools without assuming you want to become an engineer. Instead, it walks you through practical steps to share your expertise in a format today’s clients already trust—long-form, authentic audio. 🔊

From a professional responsibility perspective, the guidance aligns with ABA Model Rule 1.1 on technology competence and Model Rule 1.6 on confidentiality by emphasizing the use of secure platforms, thoughtful content planning, and careful handling of client-identifying details. The book reinforces that podcasting can showcase your substantive knowledge while staying within the guardrails of Model Rule 7.1, avoiding misleading claims about your services. ⚖️

QR Code for Amazon book link

The first five-star review underlines two themes: listeners want real conversations, and they quickly recognize when a lawyer respects both the audience’s time and the profession’s ethical duties. That is exactly the posture this book encourages—credible, compliant, and client-centered. 🌟

If you are ready to build authority, differentiate your practice, and satisfy your tech-competence obligations without drowning in jargon, now is the perfect time to get your copy of “The Lawyer’s Guide to Podcasting” on Amazon and start planning your first ethically sound episode. 🚀

MTC: AI may not be your co‑counsel—and a recent SDNY decision just made that painfully clear. ⚖️🤖

SDNY Heppner Ruling: Public AI Use Breaks Attorney-Client PrivilegE!

In United States v. Heppner, Judge Jed Rakoff of the Southern District of New York ruled that documents a criminal defendant generated with a publicly accessible AI tool and later sent to his lawyers were not protected by either attorney‑client privilege or the work‑product doctrine. That decision should be a wake‑up call for every lawyer who has ever dropped client facts into a public chatbot.

The court’s analysis followed traditional privilege principles rather than futuristic AI theory. Privilege requires confidential communication between a client and a lawyer made for the purpose of obtaining legal advice. In Heppner, the AI tool was “obviously not an attorney,” and there was no “trusting human relationship” with a licensed professional who owed duties of loyalty and confidentiality. Moreover, the platform’s privacy policy disclosed that user inputs and outputs could be collected and shared with third parties, undermining any reasonable expectation of confidentiality. In short, the defendant’s AI‑generated drafts looked less like protected client notes and more like research entrusted to a third‑party service.

For sometime now, I’ve warned on The Tech‑Savvy Lawyer.Page has warned practitioners not to paste client PII or case‑specific facts into generative AI tools, particularly public models whose terms of use and training practices erode confidentiality. We have consistently framed AI as an extension of a lawyer’s existing ethical duties, not a shortcut around them. I have encouraged readers to treat these systems like any other non‑lawyer vendor that must be vetted, contractually constrained, and configured before use. That perspective aligns squarely with Heppner’s outcome: once you treat a public AI as a casual brainstorming partner, you risk treating your client’s confidences as discoverable data.

A Tech-Savvy Lawyer Avoids AI Privilege Waiver With Confidentiality Safeguards!

For lawyers, this has immediate implications under the ABA Model Rules. Model Rule 1.1 on competence now explicitly includes understanding the “benefits and risks associated” with relevant technology, and recent ABA guidance on generative AI emphasizes that uncritical reliance on these tools can breach the duty of competence. A lawyer who casually uses public AI tools with client facts—without reading the terms of use, configuring privacy, or warning the client—may fail the competence test in both technology and privilege preservation. The Tech‑Savvy Lawyer.Page repeatedly underscores this point, translating dense ethics opinions into practical checklists and workflows so that even lawyers with only moderate tech literacy can implement safer practices.

Model Rule 1.6 on confidentiality is equally implicated. If a lawyer discloses client confidential information to a public AI platform that uses data for training or reserves broad rights to disclose to third parties, that disclosure can be treated like sharing with any non‑necessary third party, risking waiver of privilege. Ethical guidance stresses that lawyers must understand whether an AI provider logs, trains on, or shares client data and must adopt reasonable safeguards before using such tools. That means reading privacy policies, toggling enterprise settings, and, in many cases, avoiding consumer tools altogether for client‑specific prompts.

Does a private, paid AI make a difference? Possibly, but only if it is structured like other trusted legal technology. Enterprise or legal‑industry tools that contractually commit not to train on user data and to maintain strict confidentiality can better support privilege claims, because confidentiality and reasonable expectations are preserved. Tools like Lexis‑style or Westlaw‑style AI offerings, deployed under robust business associate and security agreements, look more like traditional research platforms or litigation support vendors within Model Rules 5.1 and 5.3, which govern supervisory duties over non‑lawyer assistants. The Tech‑Savvy Lawyer.Page has emphasized this distinction, encouraging lawyers to favor vetted, enterprise‑grade solutions over consumer chatbots when client information is involved.

Enterprise AI Vetting Checklist for Lawyers: Contracts, NDA, No Training

The tech‑savvy lawyer in 2026 is not the one who uses the most AI; it is the one who knows when not to use it. Before entering client facts into any generative AI, lawyers should ask: Is this tool configured to protect client confidentiality? Have I satisfied my duties of competence and communication by explaining the risks to my client (Model Rules 1.1 and 1.4)? And if a court reads this platform’s privacy policy the way Judge Rakoff did, will I be able to defend my privilege claims with a straight face to a court or to a disciplinary bar?

AI may be a powerful drafting partner, but it is not your co‑counsel and not your client’s confidant. The tech‑savvy lawyer—of the sort championed by The Tech‑Savvy Lawyer.Page—treats it as a tool: carefully vetted, contractually constrained, and ethically supervised, or not used at all. 🔒🤖

📌 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!” ⚖️🤖

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

🎙️ My Law School Library Adds The Lawyer’s Guide to Podcasting to Empower Ethical, Tech-Savvy Attorneys ⚖️

https://law-capital.libguides.com/SpecialCollections/NewBooks

I’m thrilled to share that my alma mater, Capital University Law School, has added my book, The Lawyer’s Guide to Podcasting, to its Law Library Special Collections. 🎉📚 Seeing this guide on the same shelves where I learned to think like a lawyer underscores how central ethical technology use has become to modern advocacy. 🎙️ Written for attorneys with limited to moderate tech skills, it walks readers through planning, recording, and promoting a law‑firm podcast while honoring ABA Model Rules on technology competence, confidentiality, and attorney advertising, helping you communicate confidently, credibly, and compliantly. ⚖️🚀

You can pick up your copy on Amazon Today!

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

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

Word of the Week: Deepfakes: How Lawyers Can Spot Fake Digital Evidence and Avoid ABA Model Rule Violations ⚖️

A Tech-Savvy Lawyer needs to be able to spot Deepfakes Before Courtroom Ethics Violations!

“Deepfakes” are AI‑generated or heavily manipulated audio, video, or images that convincingly depict people saying or doing things that never happened.🧠 They are moving from internet novelty to everyday litigation risk, especially as parties try to slip fabricated “evidence” into the record.📹

Recent cases and commentary show courts will not treat deepfakes as harmless tech problems. Judges have dismissed actions outright and imposed severe sanctions when parties submit AI‑generated or altered media, because such evidence attacks the integrity of the judicial process itself.⚖️ At the same time, courts are wary of lawyers who cry “deepfake” without real support, since baseless challenges can look like gamesmanship rather than genuine concern about authenticity.

For practicing lawyers, deepfakes are first and foremost a professional responsibility issue. ABA Model Rule 1.1 (Competence) now clearly includes a duty to understand the benefits and risks of relevant technology, which includes generative AI tools that create or detect deepfakes. You do not need to be an engineer, but you should recognize common red flags, know when to request native files or metadata, and understand when to bring in a qualified forensic expert.

Deepfakes in Litigation: Detect Fake Evidence, Protect Your License!

Deepfakes also implicate Model Rule 3.3 (Candor to the tribunal) and Model Rule 3.4 (Fairness to opposing party and counsel). If you knowingly offer manipulated media, or ignore obvious signs of fabrication in your client’s “evidence,” you risk presenting false material to the court and obstructing access to truthful proof. Courts have made clear that submitting fake digital evidence can justify terminating sanctions, fee shifting, and referrals for disciplinary action.

Model Rule 8.4(c), which prohibits conduct involving dishonesty, fraud, deceit, or misrepresentation, sits in the background of every deepfake decision. A lawyer who helps create, weaponize, or strategically “look away” from deepfake evidence is not just making a discovery mistake; they may be engaging in professional misconduct. Likewise, a lawyer who recklessly accuses an opponent of using deepfakes without factual grounding risks violating duties of candor and professionalism.

Practically, you can start protecting your clients with a few repeatable steps. Ask early in the case what digital media exists, how it was created, and who controlled the devices or accounts.🔍 Build authentication into your discovery plan, including requests for original files, device logs, and platform records that can help confirm provenance. When the stakes justify it, consult a forensic expert rather than relying on “gut feel” about whether a recording “looks real.”

lawyers need to know Deepfakes, Metadata, and ABA Ethics Rules!

Finally, talk to clients about deepfakes before they become a problem. Explain that altering media or using AI to “clean up” evidence is dangerous, even if they believe they are only fixing quality.📲 Remind them that courts are increasingly sophisticated about AI and that discovery misconduct in this area can destroy otherwise strong cases. Treat deepfakes as another routine topic in your litigation checklist, alongside spoliation and privilege, and you will be better prepared for the next “too good to be true” video that lands in your inbox.

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

📌 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

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

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. 💼⚖️

HOW TO: How Lawyers Can Protect Themselves on LinkedIn from New Phishing 🎣 Scams!

Fake LinkedIn warnings target lawyers!

LinkedIn has become an essential networking tool for lawyers, making it a high‑value target for sophisticated phishing campaigns.⚖️ Recent scams use fake “policy violation” comments that mimic LinkedIn’s branding and even leverage the official lnkd.in URL shortener to trick users into clicking on malicious links. For legal professionals handling confidential client information, falling victim to one of these attacks can create both security and ethical problems.

First, understand how this specific scam works.💻 Attackers create LinkedIn‑themed profiles and company pages (for example, “Linked Very”) that use the LinkedIn logo and post “reply” comments on your content, claiming your account is “temporarily restricted” for non‑compliance with platform rules. The comment urges you to click a link to “verify your identity,” which leads to a phishing site that harvests your LinkedIn credentials. Some links use non‑LinkedIn domains, such as .app, or redirect through lnkd.in, making visual inspection harder.

To protect yourself, treat all public “policy violation” comments as inherently suspect.🔍 LinkedIn has confirmed it does not communicate policy violations through public comments, so any such message should be considered a red flag. Instead of clicking, navigate directly to LinkedIn in your browser or app, check your notifications and security settings, and only interact with alerts that appear within your authenticated session. If the comment uses a shortened link, hover over it (on desktop) to preview the destination, or simply refuse to click and report it.

From an ethics standpoint, these scams directly implicate your duties under ABA Model Rules 1.1 and 1.6.⚖️ Comment 8 to Rule 1.1 stresses that competent representation includes understanding the benefits and risks associated with relevant technology. Failing to use basic safeguards on a platform where you communicate with clients and colleagues can fall short of that standard. Likewise, Rule 1.6 requires reasonable efforts to prevent unauthorized access to client information, which includes preventing account takeover that could expose your messages, contacts, or confidential discussions.

Public “policy violations” are a red flag!

Practically, you should enable multi‑factor authentication (MFA) on LinkedIn, use a unique, strong password stored in a reputable password manager, and review active sessions regularly for unfamiliar devices or locations.🔐 If you suspect you clicked a malicious link, immediately change your LinkedIn password, revoke active sessions, enable or confirm MFA, and run updated anti‑malware on your device. Then notify your firm’s IT or security contact and consider whether any client‑related disclosures are required under your jurisdiction’s ethics rules and breach‑notification laws.

Finally, build a culture of security awareness in your practice.👥 Brief colleagues and staff about this specific comment‑reply scam, show screenshots, and explain that LinkedIn does not resolve “policy violations” via comment threads. Encourage a “pause before you click” mindset and make reporting easy—internally to your IT team and externally to LinkedIn’s abuse channels. Taking these steps not only protects your professional identity but also demonstrates the technological competence and confidentiality safeguards the ABA Model Rules expect from modern legal practitioners.

From an ethics standpoint, these scams directly implicate your duties under ABA Model Rules 1.1 and 1.6.⚖️ Comment 8 to Rule 1.1 stresses that competent representation includes understanding the benefits and risks associated with relevant technology. Failing to use basic safeguards on a platform where you communicate with clients and colleagues can fall short of that standard. Likewise, Rule 1.6 requires reasonable efforts to prevent unauthorized access to client information, which includes preventing account takeover that could expose your messages, contacts, or confidential discussions.

Train your team to pause and report!

Practically, you should enable multi‑factor authentication (MFA) on LinkedIn, use a unique, strong password stored in a reputable password manager, and review active sessions regularly for unfamiliar devices or locations.🔐 If you suspect you clicked a malicious link, immediately change your LinkedIn password, revoke active sessions, enable or confirm MFA, and run updated anti‑malware on your device. Then notify your firm’s IT or security contact and consider whether any client‑related disclosures are required under your jurisdiction’s ethics rules and breach‑notification laws.

Finally, build a culture of security awareness in your practice.👥 Brief colleagues and staff about this specific comment‑reply scam, show screenshots, and explain that LinkedIn does not resolve “policy violations” via comment threads. Encourage a “pause before you click” mindset and make reporting easy—internally to your IT team and externally to LinkedIn’s abuse channels. Taking these steps not only protects your professional identity but also demonstrates the technological competence and confidentiality safeguards the ABA Model Rules expect from modern legal practitioners.