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

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

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

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

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

In our conversation, we cover the following

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Resources

Cases

• In Mendones v. Cushman & Wakefield, Inc. (Cal. Super. Ct. Alameda County, 2025), plaintiffs submitted multiple videos, photos, and screenshots that the court determined were deepfakes or altered with generative AI.📹 Judge Victoria Kolakowski found intentional submission of false testimony and imposed terminating sanctions, dismissing the case outright and emphasizing that deepfake evidence “fundamentally undermines the integrity of judicial proceedings.”⚖️

• In New York, two lawyers became infamous in 2023 after filing a brief containing six imaginary cases generated by ChatGPT; Judge P. Kevin Castel sanctioned them under Rule 11 for abandoning their responsibilities and failing to verify the authorities they cited.📑 They were ordered to pay a monetary penalty and to notify the real judges whose names had been falsely invoked, a reputational hit that far exceeded the dollar amount.💸

• A California appellate lawyer, Amir Mostafavi, was later fined $10,000 for filing an appeal with twenty‑one fake case citations generated by ChatGPT.💻 The court stressed that he had not read or verified the AI‑generated text, and treated that omission as a violation of court rules and a waste of judicial resources and taxpayer money.⚠️ 

ABA Model Rules

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

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

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

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

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

📖 WORD OF THE WEEK YEAR🥳: Verification: The 2025 Word of the Year for Legal Technology ⚖️💻

all lawyers need to remember to check ai-generated legal citations

After reviewing a year's worth of content from The Tech-Savvy Lawyer.Page blog and podcast, one word emerged to me as the defining concept for 2025: Verification. This term captures the essential duty that separates competent legal practice from dangerous shortcuts in the age of artificial intelligence.

Throughout 2025, The Tech-Savvy Lawyer consistently emphasized verification across multiple contexts. The blog covered proper redaction techniques following the Jeffrey Epstein files disaster. The podcast explored hidden AI in everyday legal tools. Every discussion returned to one central theme: lawyers must verify everything. 🔍

Verification means more than just checking your work. The concept encompasses multiple layers of professional responsibility. Attorneys must verify AI-generated legal research to prevent hallucinations. Courts have sanctioned lawyers who submitted fictitious case citations created by generative AI tools. One study found error rates of 33% in Westlaw AI and 17% in Lexis+ AI. Note the study's foundation is from May 2024, but a 2025 update confirms these findings remain current—the risk of not checking has not gone away. "Verification" cannot be ignored.

The duty extends beyond research. Lawyers must verify that redactions actually remove confidential information rather than simply hiding it under black boxes. The DOJ's failed redaction of the Epstein files demonstrated what happens when attorneys skip proper verification steps. Tech-savvy readers simply copied text from beneath the visual overlays. ⚠️

use of ai-generated legal work requires “verification”, “Verification”, “Verification”!

ABA Model Rule 1.1 requires technological competence. Comment 8 specifically mandates that lawyers understand "the benefits and risks associated with relevant technology." Verification sits at the heart of this competence requirement. Attorneys cannot claim ignorance about AI features embedded in Microsoft 365, Zoom, Adobe, or legal research platforms. Each tool processes client data differently. Each requires verification of settings, outputs, and data handling practices. 🛡️

The verification duty also applies to cybersecurity. Zero Trust Architecture operates on the principle "never trust, always verify." This security model requires continuous verification of user identity, device health, and access context. Law firms can no longer trust that users inside their network perimeter are authorized. Remote work and cloud-based systems demand constant verification.

Hidden AI poses another verification challenge. Software updates automatically activate AI features in familiar tools. These invisible assistants process confidential client data by default. Lawyers must verify which AI systems operate in their technology stack. They must verify data retention policies. They must verify that AI processing does not waive attorney-client privilege. 🤖

ABA Formal Opinion 512 eliminates the "I didn't know" defense. Lawyers bear responsibility for understanding how their tools use AI. Rule 5.3 requires attorneys to supervise software with the same care they supervise human staff members. Verification transforms from a good practice into an ethical mandate.

verify your ai-generated work like your bar license depends on it!

The year 2025 taught legal professionals that technology competence means verification competence. Attorneys must verify redactions work properly. They must verify AI outputs for accuracy. They must verify security settings protect confidential information. They must verify that hidden AI complies with ethical obligations. ✅

Verification protects clients, preserves attorney licenses, and maintains the integrity of legal practice. As The Tech-Savvy Lawyer demonstrated throughout 2025, every technological advancement creates new verification responsibilities. Attorneys who master verification will thrive in the AI era. Those who skip verification steps risk sanctions, malpractice claims, and disciplinary action.

The legal profession's 2025 Word of the Year is verification. Master it or risk everything. 💼⚖️

MTC: Is Puerto Rico’s Professional Responsibility Rule 1.19 Really Necessary? A Technology Competence Perspective.

Is PR’s Rule 1.19 necessary?

The legal profession stands at a crossroads regarding technological competence requirements. With forty states already adopting Comment 8 to Model Rule 1.1, which mandates lawyers "keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology," the question emerges: do we need additional rules like PR Rule 1.19?

Comment 8 to Rule 1.1 establishes clear parameters for technological competence. This amendment, adopted by the ABA in 2012, expanded the traditional duty of competence beyond legal knowledge to encompass technological proficiency. The Rule requires lawyers to understand the "benefits and risks associated with relevant technology" in their practice areas.

The existing framework appears comprehensive. Comment 8 already addresses core technological competencies, including e-discovery, cybersecurity, and client communication systems. Under Rule 1.1 (Comment 5), legal professionals must evaluate whether their technological skills meet "the standards of competent practitioners" without requiring additional regulatory layers.

However, implementation challenges persist. Many attorneys struggle with the vague standard of "relevant technology". The rule's elasticity means that competence requirements continuously evolve in response to technological advancements. Some jurisdictions, like Puerto Rico (see PR’s Supreme Court’s Order ER-2025-02 approving adoption of its full set of Rules of Professional Conduct, have created dedicated technology competence rules (Rule 1.19) to provide clearer guidance.

The verdict: redundancy without added value. Rather than creating overlapping rules, the legal profession should focus on robust implementation of existing Comment 8 requirements. Enhanced continuing legal education mandates, clearer interpretive guidance, and practical competency frameworks would better serve practitioners than additional regulatory complexity.

Technology competence is essential, but regulatory efficiency should guide our approach. 🚀