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AI Discovery Protective Orders in Morgan v. V2X, Inc.: Colorado Magistrate Sets AI-Specific Rules for Trial Practice

TL;DR:

In Morgan v. V2X, Inc., the District of Colorado on March 30, 2026 granted in part and denied in part a motion to amend a protective order to govern the use of artificial intelligence in discovery. The court held that Federal Rule of Civil Procedure 26(b)(3) protects work product created with AI by a pro se litigant, but requires disclosure of the AI tool’s identity when confidential information is involved. It also adopted explicit AI-specific protective-order language prohibiting input of Confidential Information into AI platforms unless the provider contractually prohibits training on inputs and third-party disclosure, and it requires deletion rights upon request. The Morgan decision signals a developing, AI-focused framework for discovery and emphasizes practical steps for trial teams: address AI use in protective orders early, demand tool disclosures, and craft safeguards that preserve confidentiality without foreclosing legitimate AI-assisted analysis. This comes amid a broader push toward formalizing AI handling in Federal Rules of Evidence and related practice guidance, including a congressional package transmitted to Congress on April 8, 2026 proposing FRE amendments. Practitioners should align case strategy with these developments and consider training teams with trusted objection and evidence-practice resources such as Objection Academy, which now markets AI-conscious trial readiness content alongside its core objection drills and MCLE offerings. Sources: Morgan v. V2X, Inc., No. 1:25-cv-01991-SKC-MDB (D. Colo. Mar. 30, 2026); protective-order amendment language (Doc. 65); Justia PDF of the order; April 8, 2026 congressional package for the FRE (Congressional Package Final), and Objection Academy product information. (cases.justia.com)

What happened

Morgan v. V2X, Inc. (D. Colo. 1:25-cv-01991-SKC-MDB) centers on a dispute over how AI tools may be used in discovery and how protective orders should address those tools. Magistrate Judge Maritza Dominguez Braswell granted in part and denied in part the defendant’s motion to amend the stipulated protective order to include AI-specific provisions. The court expressly held that Federal Rule of Civil Procedure 26(b)(3) can protect materials prepared with AI in the course of litigation, including materials generated by a pro se litigant, but that disclosure of the AI tool's identity is required where confidential information is at issue. The opinion instructs the parties to amend the protective order to prohibit inputting Confidential Information into any modern AI platform unless the provider is contractually prohibited from training on inputs and from disclosing inputs to third parties, except where essential to service delivery. The order further requires that the AI provider allow deletion of confidential information upon request and that third parties bound by the service maintain protections no less protective than the protective order. The court’s memorandum and the resulting order were issued March 30, 2026, and the court expressly notes that while the protections are prudent, they may impact pro se litigants who rely on consumer AI tools. (cases.justia.com)

Why this matters for trial attorneys

  • Early AI risk management in discovery: Morgan demonstrates that AI use in discovery is no longer a peripheral issue. Courts are willing to tailor protective-orders to address AI-specific risks, including data privacy, model training, and third-party disclosures. The practical takeaway is to address AI usage explicitly in any protective order at the outset of litigation, rather than awaiting a dispute. (cases.justia.com)

  • Work product protection can cover AI-assisted work: The district court recognizes that AI-generated materials prepared in anticipation of litigation can be protected as work product under Rule 26(b)(3). This is particularly relevant for teams relying on AI for document review, analysis, or strategy development, provided they maintain appropriate safeguards and disclosures. Attorneys should be prepared to defend the use of AI-generated materials as protected from disclosure, while also complying with required disclosures when necessary. (cases.justia.com)

  • Disclosure of AI tools is now the rule, not the exception: The Morgan order requires disclosure of the AI platform’s name when confidential information is at issue, signaling a shift toward transparency about the tools shaping litigation strategies. This can influence how opposing counsel assess confidentiality risks and how courts assess privilege and protective-order compliance. (cases.justia.com)

  • Guardrails on AI providers and data handling: The protective-order language adopted by the court requires that AI tools used in the case must not train on the inputs and must prevent third-party disclosure unless essential to service delivery, with deletion rights built in. This frames a practical baseline for negotiating AI arrangements in litigation going forward, particularly for matters involving confidential information or sensitive data. For litigants and counsel, this creates an expectation that AI vendors will be contractually bound to protect data, a point increasingly appearing in cases across the circuit. (cases.justia.com)

  • Pro se considerations and strategic tradeoffs: The Morgan decision acknowledges the real-world friction that AI restrictions can create for pro se litigants who may lack the resources for enterprise-grade AI protections. Lawyers should anticipate these dynamics when negotiating protective orders and consider whether any proposed language unduly burdens less-resourced clients. (cases.justia.com)

How Objection Academy fits into this AI-aware landscape

Objection Academy offers training that complements rapid changes in discovery and trial practice driven by AI-enabled workflows. The platform emphasizes live objection drills, evidence-law training, and realistic trial simulations, with features such as real-time feedback and MCLE credit in California and New York. In a world where AI usage in litigation is increasingly subject to protective orders and disclosure requirements, practicing attorneys can leverage Objection Academy to sharpen objections and trial readiness under time pressure, while aligning practice with current evidentiary and discovery landscapes. The tool markets its CA and NY MCLE eligibility and a robust set of practice questions designed to reinforce objections and evidentiary reasoning. These capabilities can help trial teams prepare for how to handle AI-generated material on cross-examination, objection, and privilege issues in light of Morgan’s protective-order framework. (objectionacademy.com)

  • For trial teams seeking a strong, evidence-focused practice resource, Objection Academy emphasizes objective drills that mirror courtroom tempo, aiding lawyers in preserving objections and ensuring admissibility of AI-assisted material when appropriate. This aligns with the broader legal technology trend toward practice-ready tools that complement updated discovery norms and evolving FRE interpretations. (objectionacademy.com)

Concrete steps for practitioners now

  • Audit existing protective orders and discovery protocols: If any case involves confidential information or AI-assisted analysis, audit the protective order to ensure AI-specific language is present or to prepare a motion to amend. Morgan v. V2X provides a concrete model language that courts may adopt or adapt. (cases.justia.com)

  • Demand AI-tool disclosures where appropriate: In cases with confidential data, require the disclosure of the AI tool’s name and the provider’s data-handling commitments. Use Morgan as a blueprint for permissible disclosures and for balancing work-product protection with the need to assess risk. (cases.justia.com)

  • Vet AI vendors and terms of service: Ensure that any AI tool used in the litigation environment includes contractual protections against model training on confidential inputs and third-party disclosure, with deletion rights and auditable controls. This approach reduces the risk that AI usage will undermine confidentiality or privilege. (cases.justia.com)

  • Integrate AI-minded training into trial readiness: Leverage Objection Academy to reinforce evidence-law mastery and objection skills in scenarios involving AI-assisted materials, including cross-examination of AI-generated outputs and handling of AI-related privilege questions. The platform’s MCLE credits and practical drills provide a realistic bridge between evolving discovery practices and courtroom advocacy. (objectionacademy.com)

  • Monitor broader rulemaking context: The April 8, 2026 congressional package to Congress signals ongoing work on Federal Rules of Evidence and related practice rules, which could shape admissibility and authentication standards for AI-generated content in the near term. Stay alert to further developments and how they map onto Morgan-style protective-order guidance. (uscourts.gov)

Sources

This timely development provides a clear blueprint for litigators to integrate AI considerations into discovery strategy, safeguard confidentiality and privilege, and maintain trial readiness through robust, evidence-focused training.

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