Courts Are Restricting AI in Discovery: What Morgan v. V2X and Jeffries v. Harcros Mean for Your Practice

If you use AI tools in litigation discovery, two recent federal decisions have fundamentally changed the rules. Morgan v. V2X, Inc., No. 25-1991 (D. Colo. Mar. 30, 2026) and Jeffries v. Harcros Chemicals, Inc., No. 25-2569-KHV-ADM (D. Kan. 2026) have established a framework of protective order requirements that prohibit consumer AI platforms from processing discovery materials. For solo practitioners and small firms, this creates an acute problem: enterprise eDiscovery platforms cost $75,000 to $300,000 per year, but the tools you can actually afford are now prohibited by court order.

This article breaks down both decisions, explains the compliance requirements they impose, and describes the self-hosted AI architecture that JD Woods Law PLC has built to solve this problem.

Morgan v. V2X: The Three-Prong Test

In Morgan v. V2X, the District of Colorado addressed a dispute over AI tools being used to process confidential discovery materials. The court held that any AI tool processing documents designated CONFIDENTIAL or higher must satisfy three contractual safeguards:

1. No training on inputs. The AI system must not use any submitted documents to train, fine-tune, or otherwise modify its model. Documents are inference inputs only.
2. No third-party disclosure. No submitted data may be disclosed to any third party beyond what is essential for service delivery. This effectively prohibits cloud-based AI platforms where data passes through vendor servers.
3. Right to delete. The producing party must retain the ability to require complete deletion of all confidential information, with written documentation of the protections in place.

The court required that these protections be documented in writing and retained for the duration of the litigation. In practice, this means the party using AI tools must be able to produce a sworn declaration or affidavit describing their system's architecture and data handling procedures.

Jeffries v. Harcros: The Expansion to All Discovery Materials

Jeffries v. Harcros went further than Morgan. The District of Kansas restricted “open” AI tools for ALL discovery materials, not just those designated confidential. The court's reasoning rested on two findings:

1. Data incorporated into model training is irrecoverable. The court found that once data enters a consumer AI model's training pipeline, it is “impossible as a practical matter” to retrieve or delete. This makes meaningful clawback impossible.
2. Broad restrictions prevent perverse incentives. If only confidential materials were restricted, parties might strategically under-designate documents to enable AI processing, or over-redact to reduce the volume requiring compliant tools. Restricting all materials eliminates this gamesmanship.

Under Jeffries, no publicly accessible, cloud-hosted, or consumer-grade AI tool may be used on any discovery material in the case, regardless of confidentiality designation.

The Compliance Gap: Who This Hurts Most

Large firms with enterprise eDiscovery licenses (Relativity, Everlaw, DISCO) will adapt their existing contracts and infrastructure. The firms most affected are:

• Solo practitioners handling contingency cases with significant document volumes
• Small litigation firms (5 to 50 attorneys) that rely on consumer AI for document review efficiency
• Pro se litigants whose AI use has already been challenged in court (see United States v. Heppner, S.D.N.Y. 2026)
• Plaintiff's firms on tight budgets facing well-funded defendants who will weaponize protective orders against AI use

The irony is that AI-assisted document review is often the only way these practitioners can afford to handle large discovery volumes at all. The court decisions do not prohibit AI in discovery. They require specific architectural safeguards. The problem is access to systems that meet those requirements.

What a Compliant Architecture Looks Like

To satisfy both Morgan and Jeffries, an AI document processing system must meet every one of the following requirements:

• Self-hosted hardware. The AI model runs on physical hardware under the direct control of the processing party or its designated vendor. No cloud servers. No SaaS platform.
• Air-gapped inference. The inference engine operates without outbound internet connectivity during document processing. Documents cannot be transmitted externally by design, not just by policy.
• Open-source models. The model weights are locally stored and inspectable. No proprietary “black box” AI where data handling is governed by opaque terms of service.
• No model training. Documents are processed as inference inputs only. The model's weights remain identical before and after every engagement. No fine-tuning, no adaptation, no learning from client data.
• Verified secure destruction. Upon engagement completion, all documents and derivative data are permanently destroyed using a verifiable protocol (such as DoD 5220.22-M three-pass overwrite), with a destruction certificate issued per engagement.
• Sworn documentation. The operator can produce affidavits and declarations describing the technical architecture, data handling procedures, and compliance with specific protective order language.

A Note on This Article

When this article was first published, JD Woods Law PLC offered a self-hosted AI document processing service for litigators. That service has since been retired. The analysis of Morgan and Jeffries above is unchanged, and the architectural characteristics listed above remain a useful yardstick for evaluating any vendor. For a structured set of questions to put to a vendor, see the firm's 5 Questions to Ask Your eDiscovery Vendor and the companion vendor vetting checklist.

Why This Matters Beyond Discovery

Morgan and Jeffries are discovery-specific rulings, but the underlying principle has broader implications. Courts are recognizing that consumer AI platforms are fundamentally incompatible with the confidentiality obligations of legal practice. United States v. Heppner (S.D.N.Y. 2026) extended this reasoning to attorney-client privilege: using a public AI platform for legal strategy waives privilege because the platform's terms of service permit data collection, training, and third-party disclosure.

Warner v. Gilbarco (E.D. Mich. 2026) provides a related but distinct data point: even a pro se litigant with no attorney at all was granted work-product protection over AI-generated materials, because the court treated the AI tool as exactly that — a tool, not a person whose involvement determines the analysis. The issue in all of these cases is not whether a lawyer personally directs the AI. It is whether the architecture preserves the confidentiality and control that the law requires.

For practitioners navigating this evolving landscape, the path forward is clear: the AI must be under your control, on your hardware (or your vendor's hardware under contractual safeguards), with no data leaving the system. Anything less is a risk that courts are now explicitly prohibiting.

What To Do Now

If you are a litigator using AI tools in discovery, or anticipate facing a protective order that restricts AI use, consider these steps:

1. Audit your current tools. Does your AI platform process data on third-party servers? Does it train on your inputs? Can you produce documentation of its data handling? If you cannot answer these questions with certainty, your tools likely fail the Morgan test.
2. Review pending protective orders. If you have active cases with confidentiality orders, check whether they address AI use. If not, anticipate that opposing counsel may seek amendments. Be prepared to demonstrate compliance proactively.
3. Identify compliant alternatives. Self-hosted AI solutions exist and are becoming more accessible. Put the same questions to every vendor you consider: where documents are processed, whether inputs are used for training, what the contract says about deletion, and whether the vendor will sign an affidavit describing its architecture.

Jonathan D. Woods, Esq. is the principal of JD Woods Law PLC, a Florida law firm offering AI-enhanced legal services. He can be reached at jdwoods@jdwoodslaw.com.