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Build An AI-Powered Practice — Without Risking Compliance or your Ethics

Build An AI-Powered Practice — Without Risking Compliance or your Ethics

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Featured article

Morgan v. V2X Decided a Discovery Dispute. The Commentary Turned It Into Something Bigger.

Amy Swaner

May 2026

Calculating...

On March 30, 2026, a federal magistrate judge in Colorado issued an order in an employment discrimination case most lawyers will never read. They should — but not for the reasons most of the commentary has been suggesting. Morgan v. V2X, Inc., No. 25-cv-01991-SKC-MDB (D. Colo. Mar. 30, 2026), is one of the most thoughtful federal AI decisions issued so far this year. It is also narrower than it is being read to be.

In more than one venue, commentators have turned Magistrate Judge Maritza Dominguez Braswell’s order into a universal AI governance doctrine. It isn’t. What she decided was a discovery dispute about protective-order language in a case where a pro se plaintiff wanted to use public AI tools on documents the corporate defendant had produced under a confidentiality order. Judge Braswell’s reasoning and framework are intelligent and elegant. The reasoning deserves careful attention. But let’s be clear on what Braswell actually held and what it actually means for your use of AI.

Morgan as the Case Study

Background

The underlying case is an employment discrimination lawsuit in the District of Colorado. Archie Morgan is a pro se plaintiff suing his employer V2X. Both sides used AI in their litigation work. V2X has enterprise AI tools. Morgan, representing himself, uses public ones. The AI dispute surfaced when V2X moved to restrict Morgan’s use of public AI platforms — a restriction Morgan argued would create an unfair “technological gap” between a self-represented litigant and a well-funded corporate defendant with proprietary AI and cloud-based systems of its own. V2X also moved to compel Morgan to disclose which tools he was using.

Judge Braswell — who co-chairs her district’s AI Committee, co-founded the Judicial AI Consortium, is on Sedona Conference’s working group (WG13) regarding AI, and is genuinely knowledgeable in this material — did not default to either side’s proposed language. She wrote her own. Before uploading confidential information covered by the protective order to any AI platform, she held, the provider must be contractually prohibited from:

  • Storing or using inputs to train or improve the model;

  • Disclosing inputs to third parties (except where essential to service delivery, and then only on terms no less protective than the protective order itself); and

  • Retaining inputs beyond what is necessary.

She also required that the vendor contractually afford the party the ability to delete all confidential information upon request, and that the party retain written documentation of the contractual protections. And she ordered Morgan to disclose the name of the AI tool he was using, finding that tool selection alone does not reveal mental impressions or legal strategy absent a specific factual record.

That is the entire order. A set of contract requirements and a disclosure obligation, tied to information produced under a protective order. On its face, thoughtful but narrow

What the Judge Decided

Commentary on Morgan has slotted it alongside United States v. Heppner, No. 25-cr-00503 (S.D.N.Y. Feb. 17, 2026), and Warner v. Gilbarco, Inc., No. 2:24-cv-12333 (E.D. Mich. Feb. 10, 2026). That framing puts the three cases into a single bucket labeled “AI and privilege” and treats Braswell as though she were answering the same question as Judge Rakoff and Judge Patti. She wasn’t.

An Everlaw article discussing Morgan noted the procedural posture as material under a protective order being submitted into an AI tool, but then said “the court . . . established a precise set of contractual must-haves for any legal professional looking to integrate AI into their workflow.” Clio went further, headlining its post “Courts Are Starting to Pick AI Tool Winners” and characterizing the order as “a new standard for AI use in litigation”. Respectfully, both are overstatements about the holding and its reach. Heppner and Gilbarco both asked whether a litigant's use of AI waived existing legal protections — traditional doctrinal questions applied to a new technology. The question Braswell answered in Morgan was different and more narrow: in a discovery dispute, what should a protective order say about a pro se plaintiff's use of public AI tools on materials the corporate defendant produced under a confidentiality designation? That is a data governance question, not a privilege question.

As a data governance nerd (or maybe Diva?), this is the same type of question every data protection officer, a cloud governance lead, or a vendor risk analyst asks about any third-party processor. What happens to the data once it leaves my environment? Who can see it? How long does it persist? What rights does the vendor have to use it for their own purposes? Privacy professionals and information governance lawyers have been asking these questions for decades. However, they are more important now, thanks to widespread AI use.

The Applicability of the Order

Braswell decided two things, one of them quite narrow:

1. Work product privilege may apply to an advocate’s AI tool use;

2. A litigant bound by a protective order may not use AI tools on materials covered by that order unless the AI vendor meets her contract-based standard.

That’s it.

She did not hold that all lawyers everywhere must use only AI tools meeting that standard. She did not hold that a solo practitioner using ChatGPT or Claude on legal work is violating Rule 1.6. She did not hold that consumer AI is per se incompatible with confidentiality obligations. She resolved a discovery dispute about protective order language in a specific case with a specific fact pattern.

Granted, Judge Braswell’s reasoning is attractive. The standard is clean, sensible, and maps to real data governance principles. It should inform how lawyers think about AI tool selection generally. But “should inform” and “is binding authority” are different things.

What Judge Braswell no doubt understood was that different data should be treated differently.

Four Data Categories, Four Enforceability Tiers

Data governance is everything with AI implementation. One of the first questions with AI Governance must be “Whose data is at risk, what is the risk, and what can I enforce?” Four categories of data behave differently, and the required precaution change accordingly.

Your own non-regulated information. A pro se litigant using ChatGPT to analyze her own employment history and draft her own complaint. The information is hers; the harm from any leakage runs to her and only her. She is entitled to make her own decision. Braswell’s rule would be overbroad in this category — and she didn’t purport to apply it here.

Attorney-client privileged communications. The Heppner fact pattern. Here the harm is potentially catastrophic and irreversible. Judge Rakoff’s reasoning raises the question — left open in Heppner itself — of whether feeding lawyer-provided information into a consumer AI tool risks waiving privilege over the original attorney-client communications. If that reasoning is extended in future cases, the three-part standard may actually be insufficient in this category.

Regulated data categories. PHI under HIPAA, nonpublic personal information under GLBA, personal data under state privacy laws. Rule-governed territory. The harm is statutory. The precaution is mandatory, independent of Morgan.

Third-party confidential information under a protective order or similar confidentiality obligation. This is the Morgan fact pattern. The information was V2X’s, produced to Morgan under a confidentiality agreement. Morgan uploading it into a training-enabled AI tool would unravel the bargain without V2X’s consent. The harm is concrete, the non-consenting party bears it, and Braswell’s rule is proportionate.

The Enforceability Spectrum

Once you know which category your data falls in, you need to know which tier of contractual protection the tool actually offers. Tool choice is about whether the protection is enforceable, provable, and durable. That’s not necessarily determined by brand or cost.


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A few concrete examples, as of this writing (vendor terms change; verify before relying):

  • OpenAI Enterprise and OpenAI API commercial tier, Harvey, Anthropic commercial API and enterprise agreements: default no-training on customer data, contractually committed. Strong tier.

  • ChatGPT with the “improve the model for everyone” toggled off: enforceable representation, but dependent on consumer terms of service. Medium tier for most use; handle privileged information with care.

  • A free AI tool, or a browser extension AI tool with no “privacy mode” setting and no visible contract: weak. Do not use with client data.

You should always know which tier you are in, and keep in mind what your firm’s AI policy permits.

Best Practices

Keeping in mind, we only have district court decisions regarding AI at this point. Reading those decisions, six best practices emerge.

  1. Match the tier to the data category. Strong-tier tools are required for third-party confidential information under a protective order, attorney-client privileged communications, and regulated data (PHI, NPI, CUI). Medium-tier tools are acceptable for general research, brainstorming, and non-sensitive drafting. Weak-tier tools belong nowhere near client data.

  2. Audit every AI tool currently in use. Document where the no-training, no-sharing, and no-retention protections are — enterprise terms, MSA, addendum, DPA, or toggle plus vendor representation. Maintain records of what tier each tool occupies and the evidence supporting that placement.

  3. Review your AI policy to map tier to data category. Your policy should expressly address the fact that different categories of data, with different confidentiality needs exist.

  4. Build the spectrum into procurement. Evaluation starts with data processing terms, not features. The right question is not “does the vendor offer a privacy toggle” but “is the toggle anchored in enforceable language, can we prove its state at the time of use, and is the remedy adequate for the data category”.

  5. Counsel should direct AI use whenever the materials may be sought in discovery. The gap between Heppner and Warner turned in part on whether counsel directed the use

  6. Never use a weak-tier tool on client data, regardless of convenience. A browser extension with a “privacy mode” setting and no visible contract is not a defensible choice, and no AI policy should permit it.

The 60-Second Checklist

Before pasting client data into any AI tool, confirm:

  • Whose data is it? (Yours, the client’s, a third party’s under a protective order, or regulated?)

  • What tier is the tool? (Strong, medium-strong, medium, or weak?)

  • Is the tier proportionate to the data sensitivity?

  • Can you prove the protection state today?

  • Did counsel direct the use?

If you cannot answer all five, stop. Either move the work to a different tool or build the record before you proceed.

The Bottom Line

Braswell decided a narrow discovery dispute carefully, and intelligently. Braswell’s order illuminated a principle that was already sound. AI tools that ingest, share, or retain user data are not safe for information held under a confidentiality duty owed to others. She set out a framework for when users can put third-party confidential information into an AI tool. It has limited applicability to a lawyer’s own work outside of that factual posture.

© 2026 Amy Swaner. All Rights Reserved. May use with attribution and link to article.

In Morgan v. V2X, Judge Braswell offers a thoughtful, practical take on AI use in litigation—reminding lawyers (and even pro se litigants like Morgan) that when it comes to confidential data, it’s less about the tool itself and more about how responsibly you handle what you put into it.

Featured article

What the Musk?

Amy Swaner

May 2026

Calculating...

The Trial Started April 27, 2026

Elon Musk sued Sam Altman and OpenAI for actions that go back to the humble, non-profit beginnings of OpenAI. The filings contain some interesting, flashy gossip on the surface. But beneath the flashy gossip is a serious lesson.

The trial that started this week in Oakland is a vivid illustration of what happens when sophisticated organizations keep everything. Exhibits range from merely embarrassing to damaging — Greg Brockman’s 2017 personal journal, Elon Musk’s 2016 email calling Jeff Bezos “a bit of a tool,” internal communications about how to “control the narrative” around an investigation. These are all in the case for one reason-- nobody disposed of them.

Let's be honest. Law firms keep more, for longer, with less discipline than the Musk v. Altman defendants. Call it the “Just In Case” Rule of document retention and data governance. If your firm subscribes to that, this article shows you why you should rethink that.

What The Discovery Has Actually Produced

Hundreds of unsealed filings in Musk v. Altman have pried open the internal information practices of OpenAI, Microsoft, Meta, and Tesla. The court’s January 15, 2026 summary judgment order (docket; SJ order, 1/15/26) quotes directly from a September 2017 Brockman journal entry:

“This is the only chance we have to get out from Elon. … Financially, what will take me to $1B?”

An unsealed February 3, 2025 text exchange shows Mark Zuckerberg telling Musk that Meta’s teams were,

“on alert to take down content doxxing or threatening the people on your team” working on DOGE."

Internal OpenAI communications from March 2024 show then-communications chief of OpenAI, Hannah Wong, describing efforts to “control the narrative” around the WilmerHale investigation summary.

None of these were created as corporate records. Yet they are now central exhibits — preserved, accessible, and produced because they were relevant under Federal Rule of Civil Procedure 26 and within the corporation’s possession, custody, or control under Rule 34, That is the lesson. Discovery turns on relevance and on whether the producing party had the legal right or practical ability to obtain the material. Courts apply that test functionally. In re NTL, Inc. Securities Litigation, 244 F.R.D. 179 (S.D.N.Y. 2007). Where the corporation does not have control, Rule 45 third-party subpoenas reach the rest.

No governance lever makes existing relevant material vanish once litigation is reasonably anticipated. There is one lever that genuinely reduces what exists to be discovered, and it operates entirely before any preservation duty attaches--defensible disposition under a written retention schedule. Yep, part of one of my favorite topics: Data Governance.

Why This Is A Law Firm Problem

The instinct in most firms is to keep everything. Closed matter files, drafts, internal memos, deposition prep, conflict-check workpapers, engagement letters, billing narratives, KM databases, marketing CRMs — and now AI-tool outputs. Copilot drafts, transcription archives, redline histories, intake-bot logs. The rationales for keeping all of it are familiar: malpractice tail risk, future citation, fee disputes, the possibility a former client will ask for something. None justifies indefinite retention. Most justify defined retention with documented disposition at the end of it.

Three reasons the law firm exposure is worse than the Musk v. Altman defendants’:

Volume and surface area. Firms hold work product, strategic thinking, candid attorney communications, and client confidences for hundreds or thousands of matters at once. Each matter is its own discovery and breach surface.

Confidentiality runs indefinitely. ABA Model Rule 1.6 applies to client information after the representation ends, with no expiration. Indefinite retention extends the duration of that confidentiality risk. Every additional year a closed file sits on a server is another year it can be breached or subpoenaed.

Breach risk is asymmetric. When a tech company is breached, its own information is exposed. When a law firm is breached, the privileged information of dozens or hundreds of unrelated clients is exposed simultaneously. The ABA’s 2024 Cybersecurity TechReport and the steady cadence of firm ransomware incidents (Mossack Fonseca, the 2023 Orrick breach affecting hundreds of thousands of clients, the 2024 Houser LLP incident) drive the point home.

What the Rules Actually Permit

Ostensibly law firms cling to the "Just in Case" Rule mainly in fear of spoliation. We can all agree, however, that routine, good-faith disposition under a written schedule, applied consistently before any duty to preserve attaches, is not spoliation. The 2015 amendments to Federal Rule of Civil Procedure 37(e) tightened the spoliation framework. Sanctions for lost ESI now require that the party failed to take reasonable steps to preserve, that the information cannot be restored or replaced, and — for the most severe sanctions — that the party “acted with the intent to deprive another party of the information’s use in the litigation.” Negligence and even gross negligence will not support an adverse inference. Applebaum v. Target Corp., 831 F.3d 740, 745 (6th Cir. 2016). Conformance in good faith to a valid retention policy, before any duty to preserve arises, does not itself demonstrate the requisite intent to obstruct or deprive. Arthur Andersen LLP v. United States, 544 U.S. 696 (2005).

The Sedona Conference Commentary on Legal Holds and Commentary on Information Governance treat consistent disposition under a written schedule as the foundation of defensible practice. That’s beautiful framing – the foundation of a defensible practice. The duty to preserve attaches when litigation is reasonably anticipated, and the schedule must be suspended at that point — but disposition that occurred before that point is protected. The professional rules are aligned, not in tension. I highly recommend reading the Sedona Commentary if you're hanging on to files "just in case."

ABA Formal Op. 471 (2015) addresses what lawyers owe clients on termination of representation, surveys the split between the entire-file and end-product approaches, and confirms that lawyers are not required to retain every file indefinitely. The Federal Civil Rules and the Professional Rules of Conduct all permit defensible disposition. The “just in case” instinct is a malpractice-aversion habit that can actually be turned against you if you let it supercede good data governance.

What a Defensible Schedule Looks Like — Pick Your Model

Most firms cannot realistically tag every document inside a matter file by sub-category and apply category-specific disposition. A schedule that requires that level of operational sophistication is one most firms will not actually follow. Two simpler models are defensible and implementable; pick the one that fits your needs and infrastructure.

Model 1 — Matter-level Disposition

The entire matter file is treated as a single bucket and disposed of on one schedule triggered by matter close, with a small number of carve-outs handled separately. This is the simpler model and works for solo, small-firm, and many mid-sized practices that do not have document-level tagging in their DMS.

Matter file (all working materials, drafts, correspondence, end-product, internal memos): Retain for the period set by the longest-required category that applies to the matter — typically seven to ten years from matter close for general civil and transactional work, longer for trusts and estates, longer where statute-of-repose or active malpractice exposure requires.

Trust account records (separate): Retain for the period your jurisdiction requires — five years under ABA Model Rule 1.15(a). Check your jurisdiction specifically.

Original client documents (separate): Wills, deeds, executed instruments, and other originals returned to the client at matter close. Get into the habit of returning originals promptly whenever possible. Otherwise, you must babysit them, which comes with a resource drag.

Email (separate, system-level): Three-year default retention on routine business email, with matter-relevant emails captured into the matter file at matter close.

Marketing and CRM (separate): Three years from last contact.

Model 2 — Two-tier Disposition

The matter file is split into two tiers, each with its own disposition schedule. This is the realistic outer limit of category-specific disposition for firms without sophisticated data governance infrastructure.

  • End-product file (final pleadings, executed contracts, recorded instruments, opinion letters, closing binders): Retain for 7 - 10 years from matter close, or longer where the practice area requires; returned to the client at close where the entire-file approach applies.

  • Working file (drafts, internal memos, attorney work product, correspondence, research, deposition prep, billing detail): Retain for 3 - 5 years from matter close, with carve-outs for matters under active or threatened malpractice claim, ongoing investigation, or open ethics issue.

  • Trust account records (separate): As above — 5 years under Model Rule 1.15(a).

  • Email (separate): Three-year default; matter-relevant communications migrated into the working file or end-product file based on substance. •

  • Marketing, CRM, and AI-tool outputs (separate): As below.

Both models — the AI category AI-generated artifacts (copilot drafts, transcription archives, redline histories, intake-bot logs) are records subject to the same retention discipline as their human-authored equivalents, but live mostly outside the matter management system.

Maintain an inventory of AI tools in use, push vendor retention defaults into counsel’s hands (“indefinite” and “use for training” are the common defaults and both are unacceptable), and build AI accounts into legal-hold tooling.

Whichever model you pick, the schedule must be written, applied consistently, suspended on hold triggers, and audited. Selective enforcement — applied to associates but waived for the managing partner — is the signature failure mode.

When the Clock Starts

If agentic AI has taught me anything it’s that a schedule is only as good as its trigger. “Seven years from matter close” means nothing if “matter close” is undefined and as nebulous as morning fog. What that comes down to is pinning down in writing. Perhaps that sounds like an oversimplification, but here's why it's not. It shows a stable, consistent, measurable rule. You need that if you are going to defend a disposition decision years later.

  1. Define "Matter Close" as a Specific Event. "Matter close" cannot be an instinct if it is to stand up to scrutiny. A workable definition combines final invoice issued and paid (or written off), closing letter sent to the client, file marked closed in the practice management system, and any retained originals returned. Always document the event with a date.

  2. Trust Account Records run from termination of the representation, not matter close. ABA Model Rule 1.15(a) (many states have a similar rule) runs from termination. Usually the same date as matter close, but not always. Calendar these separately if needed.

  3. Statutes of Limitation and Repose Run on Separate Clocks. A schedule that disposes of a matter file before the malpractice statute of repose has run is a problem. Build a practice-area lookup into the schedule and trigger disposition at the later of the schedule or the applicable repose period, whichever is longer. Once you close the matter, run both clocks in parallel: the schedule cadence (eg, seven years from matter close) and the repose period, which usually predates close (eg, five years from the act). You dispose of the file when the later of the two has run.

Best Practices

I love data governance, and if you have a hammer, well, everything is a nail. But objectively, a lot of this is just good ‘ole data governance and data hygiene.

  1. Inventory what you retain and where it lives — including personal devices, AI tools, and vendor systems. You cannot govern what you cannot see.

  2. Apply the schedule consistently across the firm, including to founders and named partners. Selective enforcement defeats the doctrine and raises your liability.

  3. Suspend the schedule on legal hold and confirm holds reach AI accounts and personal devices. A schedule that doesn't pause for litigation is not a defensible schedule.

  4. Implement a written communications policy that addresses the use of personal devices and ephemeral messaging, including for senior lawyers.

  5. Review vendor retention and AI-training defaults before procurement. Indefinite retention and "use for training" defaults are common, but unacceptable. These are legal issues that shouldn't be left to the IT department.

  6. Document disposition, holds, and policy updates. Undocumented practice is indefensible practice.

  7. Calendar an annual review to address new tools, new practice areas, and new authorities. Boring advice, but it makes a big difference.

Bottom Line

Exponentially reduce the risk of that candid attorney communication by exercising good data governance. The work product, the strategic thinking, the candid attorney communications, and the client confidences sitting on your firm’s servers could turn out to be someone else’s exhibits in a future trial, or someone else’s data breach, someone else’s malpractice case, and someone else’s bar complaint. You’re keeping that file “just in case.” But really, it is unmanaged risk.

Records disposition is not the only factor at work in this case — channel discipline, privilege design, and platform governance also do real work — but it is the most concrete, the most actionable, and the most under-used.

Editorial note: this article is hardly dispositive or in-depth. Consider doing additional research.

Recommended Reading: Sedona Conference Commentary on Legal Holds, and Commentary on Information Governance

© 2026 Amy Swaner. All Rights Reserved.  May use with attribution and link to article.

Musk v. Altman Can Teach Lawyers A Lot About the “Just In Case” Rule of Document Retention

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Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.

Ai training, consulting and tools for law firms. Built by lawyers, engineered for legal ethics.

Lexara Consulting, LLC · Iowa · © 2026

Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.

Ai training, consulting and tools for law firms. Built by lawyers, engineered for legal ethics.

Lexara Consulting, LLC · Iowa · © 2026

Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.

Ai training, consulting and tools for law firms. Built by lawyers, engineered for legal ethics.

Lexara Consulting, LLC · Iowa · © 2026

Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.