🎙️ Ep. 138: How US Legal Support Integrates AI, Security, and Remote Depositions into Your Litigation Tech Stack ⚖️💻

use AI transcript review to streamline complex deposition analysis!

My next guest is Jimmie Bridwell of US Legal Support, a Houston‑based litigation support company that manages depositions, record retrieval, trial technology, and graphics generation for law firms nationwide. In this post, we dive into how US Legal Support blends security, integration, and AI‑driven tools to help solos, small firms, and AM Law practices modernize their tech stack, streamline discovery, and run more effective remote depositions.⚖️💡

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

• What are the top three ways lawyers should expect companies like US Legal Support’s technology platforms — whether remote deposition solutions, transcription services, or document management — to integrate seamlessly into a law firm’s existing tech stack to eliminate duplicative data entry and streamline trial preparation?

• What are the top three technology investments or skillsets that lawyers consistently overlook, but would dramatically improve their practice efficiency and client services in 2026?

• Based on US Legal Support’s experience facilitating over 245,000 remote events annually, what are the top three technology mistakes you see lawyers making during remote depositions or virtual proceedings, and how can they course correct to deliver more efficient client representation?

In our conversation, we cover the following

• [00:00:00] Jimmie’s personal tech stack: Surface Pro laptop, 47‑inch curved Samsung monitor, HyperCast microphone, and Logitech Brio camera in a Microsoft‑based environment with Microsoft Cloud.

• [00:00:45] Managing dual smartphones (Apple for work, Android for personal) and why Apple’s security posture matters in litigation and device holds.

• [00:01:20] How US Legal Support uses Apple computers in graphics studios while the broader organization runs on Microsoft infrastructure.

• [00:02:00] Defining the first big question: how vendors like US Legal Support should integrate with law firm tech stacks to prevent duplicate data entry.

• [00:02:15] Why integration, security, and data management form the core triad for any law‑firm‑to‑vendor data exchange.

• [00:03:00] The importance of a single, trusted first input for case data so humans aren’t re‑keying information across multiple systems.

• [00:03:40] Inside US Legal Support’s security model: SOC 2 Type 2, HIPAA, NIST protocols, Microsoft and Amazon cloud, and internal security validation.

• [00:04:40] Concrete questions lawyers should ask prospective vendors about encryption, security reviews, and penetration tests.

• [00:06:00] Data breach reporting expectations and the need for a clearly published, timely incident response framework.

• [00:07:30] Data management and access: preventing unauthorized secondary use of client data and guaranteeing 24/7 access to discovery and litigation documents.

proper ai use can help prevent chaotic Zoom deposition versus calm litigation!

• [00:08:30] Integration patterns: standardized vs customized APIs, multipoint data flows, and the importance of vendor integration experience with law firm case management tools.

• [00:11:00] Reframing overlooked tech investments and skills, with an emphasis on AI‑powered transcript review in 2026.

• [00:11:15] How AI‑assisted transcript review condenses multi‑day depositions into summarized, keyworded, and key‑point‑driven outputs for faster strategy decisions.

• [00:12:20] Why AI hallucination risk is lower when models work directly from the underlying deposition record.

• [00:14:10] AI‑assisted deposition tools: secure portals that ingest exhibits and records, identify pre‑existing conditions, output outlines, and suggest deposition questions.

• [00:15:40] Comparing legacy OCR workflows to today’s generative AI tools and how template‑free extraction speeds up discovery.

• [00:17:00] Evaluating partners with holistic litigation solutions versus piecemeal, point‑solution vendors.

• [00:18:00] The maturation of the litigation support market from small shops with thin tech budgets to larger organizations with in‑house dev teams.

• [00:19:10] Why US Legal Support favors transactional BPaaS pricing over long subscription contracts in a fast‑moving tech landscape.

• [00:22:00] Common mistakes in remote depositions, including heavy reliance on general meeting tools like Zoom for litigation‑specific workflows.

• [00:22:10] Litigation‑ready platforms vs. meeting tools: exhibit management, date stamping, annotations, real‑time feeds, and AI enhancements.

• [00:24:00] The evolution of case management platforms from generic workflow systems to highly tuned legal solutions.

• [00:26:00] Emerging horizon tools, including facial recognition and facial sentiment analytics for remote proceedings.

• [00:26:40] The long‑tail impact of COVID: remote depositions moving from nearly 0% to roughly 60–70% of proceedings, and how that forced adoption changed lawyer attitudes.

• [00:27:30] Internal adoption challenges in medium and large firms: inconsistent processes, under‑used tech, and the operational cost of “every lawyer does it differently.”

• [00:29:00] Applying manufacturing‑style process discipline to law firm workflows while respecting attorney autonomy.

• [00:30:00] Where to find US Legal Support online and how they serve clients across the United States.

RESOURCES

Connect with Jimmie Bridwell

lawyers make sure your cloud platforms are SOC 2, HIPAA, NIST‑compliant!

• US Legal Support: https://www.uslegalsupport.com

Hardware mentioned in the conversation

• Logitech Brio camera - https://www.logitech.com/en-us/products/webcams/brio-4k-hdr-webcam.html

• Surface Pro laptop - https://www.microsoft.com/en-us/surface/devices/surface-pro

• 47‑inch curved Samsung monitor - https://www.samsung.com/us/monitors/all-monitors/

Software & Cloud Services mentioned in the conversation

• Android operating systems and devices - https://www.android.com/

• Apple operating systems and devices - https://www.apple.com/

• Microsoft Cloud and Microsoft Office - https://www.microsoft.com/en-us/microsoft-365/onedrive/online-cloud-storage

• Zoom - http://www.zoom.com/

MTC: AI Won’t Replace Solo and Small-Firm Lawyers — It Will Supercharge Them ⚖️🤖

Solo lawyers can use artificial intelligence as a virtual associate to handle legal research, drafting, intake, and billing in a modern small law firm ⚖️🤖

If you run a solo or small-to-medium firm, you’ve probably heard the predictions: AI will automate legal tasks in “12 to 18 months” or replace traditional lawyers entirely by 2035. Those headlines make great clickbait, but they miss what is actually happening on the ground in smaller practices. AI is not wiping out solo and small-firm lawyers; it is changing the mix of tasks we do — and creating more opportunities for us if we adopt it intentionally and ethically. 

In a recent Washington Post opinion, Damien Charlotin argues that AI won’t replace lawyers. It will create more of them. His logic is especially important for solos and small firms. He describes legal jobs as “bundles of tasks,” many of which are tightly linked and not easily peeled apart for automation. If you’ve ever juggled intake, research, drafting, negotiation, and billing in a single day, you know exactly what that tight bundle feels like. AI is about to start pulling on pieces of that bundle — and your job is to decide how to rebundle your work in a way that serves clients, protects ethics, and keeps your business healthy. ⚖️🤖

Why Solo and Small Firms Should Ignore the Doom Headlines 😅

Charlotin points out that lawyers have never been more numerous in the United States, with law school applications rising and record-high employment in bar-required jobs. That’s happening at the same time as AI hype, which should tell you something: the profession is not collapsing.

For solos and small firms, the bigger risk is not AI replaces me, but AI-literate competitors out-serve my clients. Larger firms may have innovation teams and internal IT, but you have agility and direct control over your workflows. If you can use AI to shave hours off routine tasks — and reinvest that time into client counseling, business development, or flat-fee offerings — you can turn AI from a threat into a differentiator. As I often say on The Tech-Savvy Lawyer.Page podcast, AI is the junior associate you don’t have to hire, but still have to supervise.

Your Practice as a “Tight Bundle” of Tasks 🧩

Charlotin’s “bundles of tasks” concept is tailor-made for solo and small-firm reality. In big firms, tasks can be split across teams; in smaller shops, you wear most of the hats. Research, drafting, strategy, client communication, and billing are often intertwined in a single matter.

For experienced lawyers, Charlotin notes, “doing legal research and evaluating an argument are … often the same mental activity” — we check the argument by writing it. If you offload only the writing to AI, verification becomes a separate, deliberate act that takes time, and if you skip it, you risk sanctions for hallucinated filings. This is why I push solo and small-firm lawyers to treat AI as an assistant that drafts and summarizes, while you retain control over the analysis and final product.

Lessons from E-Discovery for Small Practices 📂➡️📈

Charlotin likens the current AI hype to the e-discovery wave more than a decade ago. Back then, headlines like those from The New York Times predicted “Armies of Expensive Lawyers, Replaced by Cheaper Software.” What actually happened? The volume of discoverable material exploded; the tools became part of practice; and lawyers moved into new roles managing, interpreting, and litigating around that information.

That same Jevons paradox — cheaper processes leading to more usage — is already playing out in tools marketed to solo and small firms. AI-assisted drafting and research platforms now make it viable for smaller shops to handle matters that previously required big-firm staffing, and to offer more predictable pricing without cutting quality. Cheaper legal work often means more legal work — especially for clients who previously couldn’t afford you.

ABA Model Rule 1.1: Competence for Lean Teams 📚

Small law firm team using legal AI tools to improve collaboration, client service, and ABA-compliant workflows across a lean practice 👩‍⚖️👨‍⚖️💻.

For solos and small- to medium-sized firms, ABA Model Rule 1.1 on competence is both a challenge and an opportunity. It requires you to understand “the benefits and risks associated with relevant technology,” including AI. But unlike big firms, you can’t delegate that understanding to an IT department or an internal AI committee; you are the committee.

Practically, that means you need at least a working grasp of what your chosen AI tools do, how they handle data, and where they fit in your workflows. You don’t need to run every experiment at once. Start with one or two high-impact areas — say, summarizing long PDFs, generating first drafts of routine emails, or creating checklists from statutes or rules — and build from there. Competence for solo and small-firm lawyers is not about chasing every new feature; it’s about picking the right tools for your practice and using them deliberately.

Rules 5.1 and 5.3: Supervision When “You Are the Management” 👥🤖

You might think Rules 5.1 and 5.3 (supervision of lawyers and nonlawyers) are big-firm problems. They’re not. If you have even one staff member, contract attorney, or virtual assistant, you are responsible for how they use AI. And even if you’re truly solo, you’re still responsible for supervising the AI tools you deploy as if they were a nonlawyer assistant.

For small practices, the most practical move is a simple written AI policy, even if it’s a one-page document:

• Which tasks can use AI (e.g., research assistance, first-draft documents);

• Which tasks require heightened review (e.g., anything filed with a court);

• Which tasks are off-limits (e.g., unsupervised client advice, sensitive fact patterns pasted into consumer chatbots).

As discussed both in Charlotin’s piece and in bar guidance for smaller firms, formal policies help you avoid ad hoc, inconsistent AI use that could jeopardize client confidentiality or court obligations.

Rule 1.6 Confidentiality: Cloud Tools on a Budget 🔐

Model Rule 1.6 on confidentiality doesn’t change just because you’re a small shop — but your margin for error is thinner. Many solos and small firms rely on cloud-based tools because they can’t host their own infrastructure. That’s fine, as long as you are careful.

Before pasting client facts into an AI tool, you must know whether it stores or reuses data, whether it trains on your inputs, and whether there’s an option for a “no training” or “enterprise” mode. When in doubt, prefer AI features built into reputable legal platforms (research tools, practice management systems, document automation suites) with clear confidentiality commitments, rather than generic consumer apps. On The Tech-Savvy Lawyer.Page, I hammer this point because solos cannot absorb the cost of a major data mishap the way some larger organizations can.

Legislative Inflation and Niche Opportunities for Smaller Firms 📜📈

Charlotin notes that every jurisdiction is “afflicted by legislative inflation” — more rules, more norms, more regulations. That means more interpretation, more disputes, more filings, and more need for lawyers. For solos and small-to-medium firms, this is an opportunity to carve out narrow niches and use AI to keep up with complex, evolving regimes that might otherwise be out of reach.

An AI-enabled solo can monitor regulatory changes, generate quick client alerts, and update templates far faster than before. Combined with targeted content marketing and SEO, this makes it possible to dominate specific micro-niches without a big marketing budget — something I frequently discuss on The Tech-Savvy Lawyer.Page when we talk about modern business development.

Entry-Level Work and the Solo/Small Pyramid 🧑‍🎓➡️⚖️

a Small-firm lawyer can use AI-powered legal technology to serve niche clients, track changing regulations, and deliver efficient legal services across a local market 🎯⚖️

Charlotin flags a serious concern: AI may change entry-level work. For big firms, that means rethinking associate leverage. In smaller firms, it means you may hire differently — or delay that first hire because AI picks up some of the routine drafting and research.

But Charlotin also notes that young lawyers are hired for reasons beyond their marginal drafting value — future partnership, signals to clients, bench strength for unpredictable surges. The same is true for small and mid-size firms. AI can handle some grunt work, but it can’t attend a community event, build a local reputation, or bring in referrals. If you use AI to free juniors from the most repetitive tasks, you can push them earlier into client-facing and business-building roles, which is exactly where smaller firms thrive.

Reorganization, Not Replacement — Especially for You 🔄

Charlotin closes by emphasizing that while the profession will look different in 2035, the lawyer is here to stay, and there will likely be more lawyers, not fewer. They will use AI — “they would be fools not to” — and they will charge for that value.

For solo and small-to-medium firms, the reorganization is already underway:

• Routine drafting and research shift toward AI-assisted workflows.

• Verification, judgment, and client counseling become even more central.

• Niche expertise, responsiveness, and pricing flexibility become your competitive edge.

If you treat AI as a core part of your toolkit — governed by the ABA Model Rules and aligned with your business goals — you must position your firm not just to survive the AI wave, but to ride it. ⚖️🤖

Its been said many times by myself and others, lawyers must embrace AI into their practice of law or be left behind by those who do!

🎙️ TSL.P Ep. #135: Ethical AI, Paperless Practice, and Smart Hardware Choices with ABA LTRC Chair Alan Klevan ⚖️🤖

My next guest is Alan Klevan, a veteran personal injury lawyer and Chair of the ABA Law Practice Division’s Legal Technology Resource Center (LTRC), known for running one of the first paperless practices in New England and for his clear-eyed approach to AI in law. In this live episode recorded at the ABA Spring Conference in San Diego, Alan and I dig into how solos and small firms can use AI, case management platforms, hardware, and workflows to practice more efficiently while honoring their ethical duties and protecting client confidentiality.

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

• What are the top three ways Alan uses AI and other tech tools to control discovery and document management at scale, protect client confidentiality, and communicate complex case progress to clients who only care that it is accurate and on time?

• As Chair of the ABA Law Practice Division’s Legal Technology Resource Center, what top three technology practices does Alan wish every small or solo lawyer would adopt in the next 12 months?

• What were the three most important technology decisions Alan made early in his career around paperless workflows, practice management, automation, and AI‑powered research—and how can today’s practitioners follow that lead?

In our conversation, we covered the following:

• [00:00:00] Live from the ABA Spring Conference in San Diego, introducing Alan Klevan and the setting of the conversation 🌴

• [00:00:30] Alan’s mirrored bi‑state setup: two Lenovo i7 laptops in Massachusetts and Florida, dual 24" HP HD monitors, two ScanSnap iX1600 scanners, laser printers, and Microsoft OneDrive syncing between offices 💻📠

• [00:01:10] Traveling with a third “road warrior” Lenovo laptop, iPhone as primary smart device, and using the reMarkable 2 tablet for handwritten notes that sync into client and ABA files ✍️

• [00:01:45] Early impressions of the Plaud (AI wearable) device, background-noise muting, and why Alan limits it to non‑critical meetings due to privilege concerns 🎧

• [00:02:20] Judicial skepticism about AI recording tools in court; motion practice, privilege issues, and a New York judge flatly banning AI recorders in the courtroom 🚫

• [00:03:10] AI hallucinations in legal practice, roughly 1,300 known hallucination incidents, and why the real problem is lawyers not checking citations—highlighted by a recent Oregon sanctions case 💸

• [00:04:00] The Oregon lawyer who tried to “fix” hallucinated citations with a motion to refile instead of candor to the court and opposing counsel, and how that became a fraud‑on‑the‑court issue under the Oregon Rules of Professional Responsibility

• [00:04:45] Using Google Scholar as an AI‑prompting “hack” to verify every citation and case suggested by AI tools 🔍

• [00:05:20] Question 1 restated: top three ways Alan uses AI and tech to (1) control discovery, (2) protect confidentiality and ethical duties, and (3) communicate complex case progress to clients

• [00:05:45] Drafting AI and social media policies directly into contingency‑fee agreements so clients do not post about their case or use open‑source AI on case‑related issues 📜

• [00:06:30] Hepner and Warner: open‑source vs enterprise AI, attorney–client privilege, work product concerns, and emerging discoverability questions for public‑facing AI platforms

• [00:07:20] Trap for the unwary: why Alan insists clients notify him before using AI on their case and why he prefers enterprise versions of AI for better protection and governance 🧠

• [00:08:10] The Nippon Life Insurance case: client uploads attorney communications into ChatGPT, asks if her lawyer is gaslighting her, then files 44 AI‑drafted motions—raising product liability and disclaimer questions for AI vendors 🏛️

• [00:09:30] Court pushback on AI disclaimer language, defective product theories, and the infancy of AI‑related legal liability

• [00:10:10] Alan’s big personal‑injury “Aaron Brockovich‑type” case with a deep‑pocket defendant and using AI to level the playing field on litigation management and motion practice ⚖️

• [00:11:00] Feeding facts, parties, defense counsel names, and pleadings into a case management system with a built‑in, highly accurate legal AI component (VL) and generating 50‑state case research for negligent infliction of emotional distress claims 📂

• [00:12:00] Running the same matter through two AI platforms (case management AI and Claude) to compare outputs, reduce hallucination risk, and mold responses to Alan’s writing style and Massachusetts practice

• [00:13:00] Using Claude (enterprise tier) to draft an opposition to a motion to dismiss seven emotional‑distress claims, followed by manual review and cross‑checking in the case management AI—leading to the defendant’s motion being denied ✅

• [00:14:15] Alan’s process for verifying AI outputs: second set of “AI eyes,” Google Scholar citation checks, and lawyer‑level review of every filing

• [00:15:00] Advice for new attorneys: try AI platforms before buying, choose a tool that fits your workflow, avoid shiny‑object syndrome, and do not over‑commit to annual plans while the market is moving fast 🧩

• [00:16:00] Michael’s caution about yearly plans, vendor lock‑in, and ensuring your data is nimble enough to move between AI platforms without costly migrations

• [00:16:45] Alan’s rule: do not chase every AI; become a master of one platform, learn it deeply, and resist the temptation to constantly switch 🧠

• [00:17:10] Both hosts stress “review, review, review”—AI as a law librarian or 3L intern, not as your practicing lawyer, and the concept that AI does not have a JD 🎓

• [00:18:00] Anecdote from 1990: Alan is sent to court unprepared, gets sent out of the courtroom to learn his file, and how that story frames his modern view of AI oversight and responsibility

• [00:19:10] Question 2: as LTRC Chair, Alan’s top three technology practices every small or solo lawyer should adopt in the next 12 months

• [00:19:30] Tech Practice #1: invest in a fast machine (Windows or Mac) with as much RAM and storage as you can reasonably afford, and strip the “crapware” off box‑store Windows machines 🖥️

• [00:20:10] Discussion of Apple vs Windows pricing, the need for more than 16 GB of RAM, multi‑core processors, and why Alan buys Lenovo laptops with 32 GB RAM and expects 3–4 year laptop lifespans 💾

• [00:21:30] Backups and storage: redundant cloud backups, redundant hard drives, using external 5 TB drives from Staples, and keeping active machines “clean” for better AI performance

• [00:22:30] Tech Practice #2: immerse yourself in what is happening with AI and law practice, become a master of one AI platform, and continuously read ethics and disciplinary decisions about AI use 📚

• [00:23:15] Tech Practice #3: your head is your most important piece of technology—using judgment, stepping back to assess risks, and making sure anything submitted to court or client is accurate

• [00:24:00] Economic access, hardware costs, and why Alan still believes lower‑resource attorneys can get workable hardware by being strategic about purchases, specs, and lifecycles

• [00:25:10] Michael’s storage philosophy: lots of local SSD, multiple backups, and revisiting older briefs and arguments (e.g., mailbox‑rule analysis) to build new work more efficiently

• [00:26:10] Disk space versus backup strategy, internal vs external drives, cloud vs local files, and disaster recovery considerations

• [00:27:20] Question 3: top three early technology decisions Alan made around paperless practice, automation, and AI‑powered research

• [00:27:40] Answer #1: going fully paperless in 2005—the first paperless practice in New England—and eliminating almost all postage costs by sending encrypted electronic communications and demand packages ✉️

• [00:28:15] Answer #2: becoming a power‑user of Adobe Acrobat and PDF workflows so he can respond to massive production requests (e.g., 10,000 pages) in seconds instead of hours 📑

• [00:29:00] Answer #3: adopting case management platforms with AI‑driven workflows that automatically assemble record requests, HIPAA authorizations, and certifications for medical providers

• [00:29:45] Dusty hardware: why Alan’s printer and ScanSnap are seeing less use, yet scanners remain necessary for partners who still prefer paper and non‑electronic delivery 🖨️

• [00:30:20] Michael’s own shrinking paper consumption, stamps.com, and transitioning to PDF‑based workflows with secure electronic delivery

• [00:31:00] Adobe Acrobat as “gold standard” for lawyers, why every attorney must understand PDFs deeply, and Alan’s “learn it, love it, live it” mantra 📄

• [00:31:40] Bonus segment: what the ABA Legal Technology Resource Center (LTRC) is, its role as a “delivery board,” and how it serves both the Law Practice Division and the broader ABA membership 🏛️

• [00:32:20] LTRC’s four pillars of law practice management—marketing, technology, practice, and finance—and how it delivers content via Law Technology Today, webinars, podcasts, and roundtables

• [00:33:10] 2024–25 LTRC theme: AI‑centric content from intake through trial, and why Alan believes LTRC may become the ABA’s most important board for practitioners navigating AI

• [00:34:00] Using AI for law‑firm marketing, content creation, case‑law recaps, and SEO—along with warnings about legal advice, PII, and AI‑generated “SEO articles” that sound inauthentic

• [00:35:00] Call to action: join the ABA Law Practice Division and LTRC, become one of roughly 30 tech‑focused thought leaders, and help shape AI guidance for the profession 🙌

• [00:36:00] Where to find Alan: why he is minimizing social presence during a major move and high‑stakes case, and the best way to reach him on LinkedIn

Hardware mentioned in the conversation

• Fujitsu ScanSnap iX1600 scanners – https://www.pfu-us.ricoh.com/scanners/scansnap/ix1600

• HP HD monitors – https://www.hp.com/us-en/shop/slp/hp-monitors/hp-series-3-pro?jumpid=ma_accy_button_na_2_260304

• HP LaserJet printers – https://www.hp.com/us-en/shop/vwa/printers/prnttyp=Laser?jumpid=ma_pr_featured_na_1_250511

• iPhone – https://www.apple.com/iphone

• Lenovo laptops – https://www.lenovo.com/us/en/laptops/

• Mac mini – https://www.apple.com/mac-mini

• reMarkable 2 tablet – https://remarkable.com/store/remarkable-2

Software & cloud services mentioned

• Adobe Acrobat – https://www.adobe.com/acrobat

• ChatGPT – OpenAI ChatGPT – https://chatgpt.com

• Claude – Anthropic Claude – https://www.anthropic.com/claude

• Dropbox – https://www.dropbox.com

• Microsoft OneDrive – https://www.microsoft.com/en-us/microsoft-365/onedrive/online-cloud-storage

• Stamps.com – https://www.stamps.com

• Zoom – https://zoom.us

📖 Word of the Week: “Cross‑Tenant” Learning in Legal Practice

Cross-tenant learning helps law firms improve AI tools without exposing data

If your firm uses cloud‑based tools, you are already living in a multi‑tenant world. In that world, cross‑tenant learning is quickly becoming a key concept that every lawyer and legal operations professional should understand. 🧠⚖️

In simple terms, a “tenant” is your firm’s logically separate space inside a cloud platform: your own users, matters, documents, and settings, isolated from everyone else’s. Cross‑tenant learning refers to techniques in which a vendor’s system learns from patterns across multiple tenants (for example, many law firms) to improve its features—such as search, drafting suggestions, or document classification—without exposing any other firm’s confidential data to you or yours to them.

Why cross‑tenant learning matters for law firms

Cross‑tenant learning is especially relevant as generative AI and machine‑learning tools become embedded in e‑discovery platforms, contract review tools, legal research systems, and practice‑management software. Vendors may use aggregated and anonymized usage data to:

• Improve relevance of search results and recommendations.

• Enhance clause and issue spotting in contracts and briefs.

• Reduce false positives in e‑discovery or compliance alerts.

• Optimize workflows based on how similar firms use the product.

For lawyers, the value proposition is straightforward: your tools can become “smarter” faster, based on lessons learned across many organizations, not just your own firm’s experience. Done properly, cross‑tenant learning can raise the baseline quality and efficiency of technology available to your practice. ⚙️📈

ABA Model Rules: Confidentiality and Competence

Any discussion of cross‑tenant learning for law firms must start with confidentiality and competence.

• Model Rule 1.6 (Confidentiality of Information) requires lawyers to safeguard information relating to the representation of a client. That obligation extends to how your vendors collect, store, and use your data. You must understand whether and how client data may be used for cross‑tenant learning and ensure that any such use preserves confidentiality through anonymization, aggregation, and strong technical and contractual controls. 🔐

• Model Rule 1.1 (Competence), including Comment 8, emphasizes that lawyers should keep abreast of the benefits and risks associated with relevant technology. Understanding cross‑tenant learning is now part of that duty. You do not need to become a data scientist, but you should be comfortable asking vendors precise questions and recognizing red flags.

• Model Rule 5.3 (Responsibilities Regarding Nonlawyer Assistance) applies when you rely on vendors as nonlawyer assistants. You must make reasonable efforts to ensure that their conduct is compatible with your professional obligations, including how they use your data for cross‑tenant learning. 🧾

Key questions to ask your vendors

ABA Model Rules guide ethical use of cross-tenant learning technologies

When evaluating a product that relies on cross‑tenant learning, consider asking:

1. What data is used?

• Is it only metadata or usage logs, or are actual document contents included?

• Is the data aggregated and anonymized before it is used to train shared models?

1. How is confidentiality protected?

• Can other tenants ever see prompts, documents, or client‑identifying information from our firm?

• What technical measures (encryption, access controls, tenant isolation) are in place?

1. Can cross‑tenant learning be limited or disabled?

• Do we have opt‑out or configuration controls?

• Is there a dedicated model or environment for our firm if needed?

1. What do the contract and policies say?

• Does the MSA or DPA clearly limit use of client data to defined purposes?

• How long is data retained, and how is it deleted if we leave?

These questions are not merely IT concerns; they go directly to your obligations under the ABA Model Rules and your firm’s risk profile.

Practical examples in law practice

Consider a cloud‑based contract‑analysis platform used by hundreds of firms. Over time, the provider can see which clauses lawyers routinely flag as risky, which edits are typically made, and what becomes the “preferred” language for certain issues. Through cross‑tenant learning, the system can use that aggregated knowledge to highlight problematic clauses and suggest alternatives more accurately for everyone.

Another example is an e‑discovery platform that uses cross‑tenant learning to distinguish between truly relevant documents and common “noise” such as automatically generated emails. The more matters the system processes across different tenants, the better it gets at ranking documents and reducing review burdens. This can be a material efficiency gain for litigation teams. ⚖️💼

In both scenarios, your ethical comfort depends on whether underlying data is appropriately anonymized, compartmentalized, and contractually protected.

Governance steps for your firm

To align cross‑tenant learning with professional obligations, firms can:

• Update vendor‑due‑diligence checklists to include explicit questions about cross‑tenant learning, training data use, and model isolation.

• Involve a cross‑functional team—lawyers, IT, information security, and risk management—in vendor selection and review.

• Document your analysis of vendor practices and how they satisfy confidentiality, competence, and supervision obligations under the ABA Model Rules.

• Educate lawyers and staff about how AI‑enabled tools work, what kinds of data they send into the system, and how to avoid unnecessary exposure of client‑identifying details.

Takeaway for busy practitioners

Smart vendor questions reduce risk in cross-tenant legal technology adoption

You do not need to reject cross‑tenant learning to protect your clients. Instead, you should approach it as a powerful capability that demands informed oversight. When well‑implemented, cross‑tenant learning can help your firm deliver faster, more consistent, and more cost‑effective legal services, while still honoring confidentiality and ethical duties. When poorly explained or loosely governed, it becomes an unnecessary and avoidable risk.

Understanding how your tools learn—and from whom—is now part of competent, modern legal practice. ⚖️💡

MTC: Even Though AI Hallucinations Are Down: Lawyers STILL MUST Verify AI, Guard PII, and Follow ABA Ethics Rules ⚖️🤖

A Tech-Savvy Lawyer MUST REVIEW AI-Generated Legal Documents

AI hallucinations are reportedly down across many domains. Still, previous podcast guest Dorna Moini is right to warn that legal remains the unnerving exception—and that is where our professional duties truly begin, not end. Her article, “AI hallucinations are down 96%. Legal is the exception,” helpfully shifts the conversation from “AI is bad at law” to “lawyers must change how they use AI,” yet from the perspective of ethics and risk management, we need to push her three recommendations much further. This is not only a product‑design problem; it is a competence, confidentiality, and candor problem under the ABA Model Rules. ⚖️🤖

Her first point—“give AI your actual documents”—is directionally sound. When we anchor AI in contracts, playbooks, and internal standards, we move from free‑floating prediction to something closer to reading comprehension, and hallucinations usually fall. That is a genuine improvement, and Moini is right to emphasize it. But as soon as we start uploading real matter files, we are squarely inside Model Rule 1.6 territory: confidential information, privileged communications, trade secrets, and dense pockets of personally identifiable information. The article treats document‑grounding primarily as an accuracy-and-reliability upgrade, but lawyers and the legal profession must insist that it is first and foremost a data‑governance decision.

Before a single contract is uploaded, a lawyer must know where that data is stored, who can access it, how long it is retained, whether it is used to train shared models, and whether any cross‑border transfers could complicate privilege or regulatory compliance. That analysis should involve not just IT, but also risk management and, in many cases, outside vendors. “Give AI your actual documents” is safe only if your chosen platform offers strict access controls, clear no‑training guarantees, encryption in transit and at rest, and, ideally, firm‑controlled or on‑premise storage. Otherwise, you may be trading a marginal reduction in hallucinations for a major confidentiality incident or regulatory investigation. In other words, feeding AI your documents can be a smart move, but only after you read the terms, negotiate the data protection, and strip or tokenize unnecessary PII. 🔐

LawyerS NEED TO MONITOR AI Data Security and PII Compliance POLICIES OF THE AI PLATFORMS THEY USE IN THEIR LEGAL WORK.

Moini’s second point—“know which tasks your tool handles reliably”—is also excellent as far as it goes. Document‑grounded summarization, clause extraction, and playbook‑based redlines are indeed safer than open‑ended legal research, and she correctly notes that open‑ended research still demands heavy human verification. Reliability, however, cannot be left to vendor assurances, product marketing, or a single eye‑opening demo. For purposes of Model Rule 1.1 (competence) and 1.3 (diligence), the relevant question is not “Does this tool look impressive?” but “Have we independently tested it, in our own environment, on tasks that reflect our real matters?”

A counterpoint is that reliability has to be measured, not assumed. Firms should sandbox these tools on closed matters, compare AI outputs with known correct answers, and have experienced lawyers systematically review where the system fails. Certain categories of work—final cites in court filings, complex choice‑of‑law questions, nuanced procedural traps—should remain categorically off‑limits to unsupervised AI, because a hallucinated case there is not just an internal mistake; it can rise to misrepresentation to the court under Model Rule 3.3. Knowing what your tool does well is only half of the equation; you must also draw bright, documented lines around what it may never do without human review. 🧪

Her third point—“build verification into the workflow”—is where the article most clearly aligns with emerging ethics guidance from courts and bars, and it deserves strong validation. Judges are already sanctioning lawyers who submit AI‑fabricated authorities, and bar regulators are openly signaling that “the AI did it” will not excuse a lack of diligence. Verification, though, cannot remain an informal suggestion reserved for conscientious partners. It has to become a systematic, auditable process that satisfies the supervisory expectations in Model Rules 5.1 and 5.3.

That means written policies, checklists, training sessions, and oversight. Associates and staff should receive simple, non‑negotiable rules:

✅ Every citation generated with AI must be independently confirmed in a trusted legal research system;

✅ Every quoted passage must be checked against the original source; 

✅ Every factual assertion must be tied back to the record.

Supervising attorneys must periodically spot‑check AI‑assisted work for compliance with those rules. Moini is right that verification matters; the editorial extension is that verification must be embedded into the culture and procedures of the firm. It should be as routine as a conflict check.

Stepping back from her three‑point framework, the broader thesis—that legal hallucinations can be tamed by better tooling and smarter usage—is persuasive, but incomplete. Even as hallucination rates fall, our exposure is rising because more lawyers are quietly experimenting with AI on live matters. Model Rule 1.4 on communication reminds us that, in some contexts, clients may be entitled to know when significant aspects of their work product are generated or heavily assisted by AI, especially when it impacts cost, speed, or risk. Model Rule 1.2 on scope of representation looms in the background as we redesign workflows: shifting routine drafting to machines does not narrow the lawyer’s ultimate responsibility for the outcome.

Attorney must verify ai-generated Case Law

For practitioners with limited to moderate technology skills, the practical takeaway should be both empowering and sobering. Moini’s article offers a pragmatic starting structure—ground AI in your documents, match tasks to tools, and verify diligently. But you must layer ABA‑informed safeguards on top: treat every AI term of service as a potential ethics document; never drop client names, medical histories, addresses, Social Security numbers, or other PII into systems whose data‑handling you do not fully understand; and assume that regulators may someday scrutinize how your firm uses AI. Every AI‑assisted output must be reviewed line by line.

Legal AI is no longer optional, yet ethics and PII protection are not. The right stance is both appreciative and skeptical: appreciative of Moini’s clear, practitioner‑friendly guidance, and skeptical enough to insist that we overlay her three points with robust, documented safeguards rooted in the ABA Model Rules. Use AI, ground it in your documents, and choose tasks wisely—but do so as a lawyer first and a technologist second. Above all, review your work, stay relentlessly wary of the terms that govern your tools, and treat PII and client confidences as if a bar investigator were reading over your shoulder. In this era, one might be. ⚖️🤖🔐

MTC

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

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 ⚖️💻

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.

Word of the week: “Legal AI institutional memory” engages core ethics duties under the ABA Model Rules, so it is not optional “nice to know” tech.⚖️🤖

Institutional Memory Meets the ABA Model Rules

“Legal AI institutional Memory” is AI that remembers how your firm actually practices law, not just what generic precedent says. It captures negotiation history, clause choices, outcomes, and client preferences across matters so each new assignment starts from experience instead of a blank page.

From an ethics perspective, this capability sits directly in the path of ABA Model Rule 1.1 on competence, Rule 1.6 on confidentiality, and Rule 5.3 on responsibilities regarding nonlawyer assistance (which now includes AI systems). Comment 8 to Rule 1.1 stresses that competent representation requires understanding the “benefits and risks associated with relevant technology,” which squarely includes institutional‑memory AI in 2026. Using or rejecting this technology blindly can itself create risk if your peers are using it to deliver more thorough, consistent, and efficient work.🧩

Rule 1.6 requires “reasonable efforts” to prevent unauthorized disclosure or access to information relating to representation. Because institutional memory centralizes past matters and sensitive patterns, it raises the stakes on vendor security, configuration, and firm governance. Rule 5.3 extends supervision duties to “nonlawyer assistance,” which ethics commentators and bar materials now interpret to include AI tools used in client work. In short, if your AI is doing work that would otherwise be done by a human assistant, you must supervise it as such.🛡️

Why Institutional Memory Matters (Competence and Client Service)

Tools like Luminance and Harvey now market institutional‑memory features that retain negotiation patterns, drafting preferences, and matter‑level context across time. They promise faster contract cycles, fewer errors, and better use of a firm’s accumulated know‑how. Used wisely, that aligns with Rule 1.1’s requirement that you bring “thoroughness and preparation” reasonably necessary for the representation, and Comment 8’s directive to keep abreast of relevant technology.

At the same time, ethical competence does not mean turning judgment over to the model. It means understanding how the system makes recommendations, what data it relies on, and how to validate outputs against your playbooks and client instructions. Ethics guidance on generative AI emphasizes that lawyers must review AI‑generated work product, verify sources, and ensure that technology does not substitute for legal judgment. Legal AI institutional memory can enhance competence only if you treat it as an assistant you supervise, not an oracle you obey.⚙️

Legal AI That Remembers Your Practice—Ethics Required, Not Optional

How Legal AI Institutional Memory Works (and Where the Rules Bite)

Institutional‑memory platforms typically:

• Ingest a corpus of contracts or matters.

• Track negotiation moves, accepted fall‑backs, and outcomes over time.

• Expose that knowledge through natural‑language queries and drafting suggestions.

That design engages several ethics touchpoints🫆:

• Rule 1.1 (Competence): You must understand at a basic level how the AI uses and stores client information, what its limitations are, and when it is appropriate to rely on its suggestions. This may require CLE, vendor training, or collaboration with more technical colleagues until you reach a reasonable level of comfort.

• Rule 1.6 (Confidentiality): You must ensure that the vendor contract, configuration, and access controls provide “reasonable efforts” to protect confidentiality, including encryption, role‑based access, and breach‑notification obligations. Ethics guidance on cloud and AI use stresses the need to investigate provider security, retention practices, and rights to use or mine your data.

• Rule 5.3 (Nonlawyer Assistance): Because AI tools are “non‑human assistance,” you must supervise their work as you would a contract review outsourcer, document vendor, or litigation support team. That includes selecting competent providers, giving appropriate instructions, and monitoring outputs for compliance with your ethical obligations.🤖

Governance Checklist: Turning Ethics into Action

For lawyers with limited to moderate tech skills, it helps to translate the ABA Model Rules into a short adoption checklist.✅

When evaluating or deploying legal AI institutional memory, consider:

1. Define Scope (Rules 1.1 and 1.6): Start with a narrow use case such as NDAs or standard vendor contracts, and specify which documents the system may use to build its memory.

2. Vet the Vendor (Rules 1.6 and 5.3): Ask about data segregation, encryption, access logs, regional hosting, subcontractors, and incident‑response processes; confirm clear contractual obligations to preserve confidentiality and notify you of incidents.

3. Configure Access (Rules 1.6 and 5.3): Use role‑based permissions, client or matter scoping, and retention settings that match your existing information‑governance and legal‑hold policies.

4. Supervise Outputs (Rules 1.1 and 5.3): Require that lawyers review AI suggestions, verify sources, and override recommendations where they conflict with client instructions or risk tolerance.

5. Educate Your Team (Rule 1.1): Provide short trainings on how the system works, what it remembers, and how the Model Rules apply; document this as part of your technology‑competence efforts.

Educating Your Team Is Core to AI Competence

This approach respects the increasing bar on technological competence while protecting client information and maintaining human oversight.⚖️

This approach respects the increasing bar on technological competence while protecting client information and maintaining human oversight.⚖️

Word of the Week: "Constitutional AI" for Lawyers - What It Is, Why It Matters for ABA Rules, and How Solo & Small Firms Should Use It!

Constitutional AI’s ‘helpful, harmless, honest’ standard is a solid starting point for lawyers evaluating AI platforms.

The term “Constitutional AI” appeared this week in a Tech Savvy Lawyer post about the MTC/PornHub breach as a cybersecurity wake‑up call for lawyers 🚨. That article used it to highlight how AI systems (like those law firms now rely on) must be built and governed by clear, ethical rules — much like a constitution — to protect client data and uphold professional duties. This week’s Word of the Week unpacks what Constitutional AI really means and explains why it matters deeply for solo, small, and mid‑size law firms.

🔍 What is Constitutional AI?

Constitutional AI is a method for training large language models so they follow a written set of high‑level principles, called a “constitution” 📜. Those principles are designed to make the AI helpful, honest, and harmless in its responses.

As Claude AI from Anthropic explains:
“Constitutional AI refers to a set of techniques developed by researchers at Anthropic to align AI systems like myself with human values and make us helpful, harmless, and honest. The key ideas behind Constitutional AI are aligning an AI’s behavior with a ‘constitution’ defined by human principles, using techniques like self‑supervision and adversarial training, developing constrained optimization techniques, and designing training data and model architecture to encode beneficial behaviors.” — Claude AI, Anthropic (July 7th, 2023).

In practice, Constitutional AI uses the model itself to critique and revise its own outputs against that constitution. For example, the model might be told: “Do not generate illegal, dangerous, or unethical content,” “Be honest about what you don’t know,” and “Protect user privacy.” It then evaluates its own answers against those rules before giving a final response.

Think of it like a junior associate who’s been given a firm’s internal ethics manual and told: “Before you send that memo, check it against these rules.” Constitutional AI does that same kind of self‑checking, but at machine speed.

🤝 How Constitutional AI Relates to Lawyers

For lawyers, Constitutional AI is important because it directly shapes how AI tools behave when handling legal work 📚. Many legal AI tools are built on models that use Constitutional AI techniques, so understanding this concept helps lawyers:

• Judge whether an AI assistant is likely to hallucinate, leak sensitive info, or give ethically problematic advice.

• Choose tools whose underlying AI is designed to be more transparent, less biased, and more aligned with professional norms.

• Better supervise AI use in the firm, which is a core ethical duty under the ABA Model Rules.

Solo and small firms, in particular, often rely on off‑the‑shelf AI tools (like chatbots or document assistants). Knowing that a tool is built on Constitutional AI principles can give more confidence that it’s designed to avoid harmful outputs and respect confidentiality.

⚖️ Why It Matters for ABA Model Rules

For solo and small firms, asking whether an AI platform aligns with Constitutional AI’s standards is a practical first step in choosing a trustworthy tool.

The ABA’s Formal Opinion 512 on generative AI makes clear that lawyers remain responsible for all work done with AI, even if an AI tool helped draft it 📝. Constitutional AI is relevant here because it’s one way that AI developers try to build in ethical guardrails that align with lawyers' obligations.

Key connections to the Model Rules:

• Rule 1.1 (Competence): Lawyers must understand the benefits and risks of the technology they use. Knowing that a tool uses Constitutional AI helps assess whether it’s reasonably reliable for tasks like research, drafting, or summarizing.

• Rule 1.6 (Confidentiality): Constitutional AI models are designed to refuse to disclose sensitive information and to avoid memorizing or leaking private data. This supports the lawyer’s duty to make “reasonable efforts” to protect client confidences.

• Rule 5.1 / 5.3 (Supervision): Managing partners and supervising attorneys must ensure that AI tools used by staff are consistent with ethical rules. A tool built on Constitutional AI principles is more likely to support, rather than undermine, those supervisory duties.

• Rule 3.3 (Candor to the Tribunal): Constitutional AI models are trained to admit uncertainty and avoid fabricating facts or cases, which helps reduce the risk of submitting false or misleading information to a court.

In short, Constitutional AI doesn’t relieve lawyers of their ethical duties, but it can make AI tools safer and more trustworthy when used under proper supervision.

🛡️ The “Helpful, Harmless, and Honest” Principle

The three pillars of Constitutional AI — helpful, harmless, and honest — are especially relevant for lawyers:

• Helpful: The AI should provide useful, relevant information that advances the client’s matter, without unnecessary or irrelevant content.

• Harmless: The AI should avoid generating illegal, dangerous, or unethical content, and should respect privacy and confidentiality.

• Honest: The AI should admit when it doesn’t know something, avoid fabricating facts or cases, and not misrepresent its capabilities.

For law firms, this “helpful, harmless, and honest” standard is a useful mental checklist when using AI:

• Is this AI output actually helpful to the client’s case?

• Could this output harm the client (e.g., by leaking confidential info or suggesting an unethical strategy)?

• Is the AI being honest (e.g., not hallucinating case law or pretending to know facts it can’t know)?

If the answer to any of those questions is “no,” the AI output should not be used without significant human review and correction.

🛠️ Practical Takeaways for Law Firms

For solo, small, and mid‑size firms, here’s how to put this into practice:

Lawyers need to screen AI tools and ensure they are aligned with ABA Model Rules.

1. Know your tools. When evaluating a legal AI product, ask whether it’s built on a Constitutional AI–style model (e.g., Claude). That tells you it’s designed with explicit ethical constraints.

2. Treat AI as a supervised assistant. Never let AI make final decisions or file work without a lawyer’s review. Constitutional AI reduces risk, but it doesn’t eliminate the need for human judgment.

3. Train your team. Make sure everyone in the firm understands that AI outputs must be checked for accuracy, confidentiality, and ethical compliance — especially when using third‑party tools.

4. Update your engagement letters and policies. Disclose to clients when AI is used in their matters, and explain how the firm supervises it. This supports transparency under Rule 1.4 and Rule 1.6.

5. Focus on “helpful, honest, harmless.” Use Constitutional AI as a mental checklist: Is this AI being helpful to the client? Is it honest about its limits? Is it harmless (no bias, no privacy leaks)? If not, don’t rely on it.