Can You Run the Other Side's Documents Through AI Review?

Read the order's recipient clause before the production goes into any tool.

Read the protective order before you choose the AI tool. A protective order is a court order, often stipulated by the parties, that limits who may receive confidential discovery material and how they may use it. Its authorized persons clause, the list of people and entities allowed to receive that material, governs an opponent's production. Routing that production through a multi-tenant cloud AI tool (one where many customers share vendor-run infrastructure) can therefore breach the order, depending on its wording, even when no privilege is waived and the model never trains on the data.

Most guidance asks a different question: does the tool learn from what you upload? That matters for your own client's files. For the other side's documents, the order comes first. Read your own retainer agreement next, ahead of the vendor's no-training pledge, because AI terms now appear there too: one commenter in a July 2026 r/Lawyertalk thread wrote, "I have no AI use allowed in my retainer agreements." The pledge describes the model. Before any upload, ask a narrower question: which documents in this production carry a confidentiality designation under the order?

Formal rules lag the practice. A TransPerfect Legal recap of a judges' panel, published January 21, 2026, states: "No formal rule yet requires client consent for AI use, but courts increasingly view transparency as best practice." Where the rules stay silent, the order in your matter decides. Practitioners, meanwhile, describe three uses for generative AI in review: surfacing categories of relevant documents early, first-level review, and quality control. Each one places the opponent's documents inside a tool.

Is AI Document Review Allowed in the First Place?

Yes, as a method. Technology-assisted document review is an accepted discovery practice, and a licensed attorney who supervises and validates the process may use AI for it.

Settle three questions, in this order, before any production goes into a tool:

1. Is the method permitted? Generally yes, provided an attorney decides relevance and signs off on the process.
2. Can the result be validated? Random sampling is the usual way a party certifies that its coding met the agreed objective.
3. Whose documents are they, and under what order? Most firms skip this one.

Principle 7 of the Sedona Canada Principles states the rule plainly: "A party may use electronic tools and processes to satisfy its discovery obligations." Torys LLP notes that the principles were incorporated by reference into the Ontario Rules of Civil Procedure in 2010. That is Canadian authority and does not bind a U.S. court. The same rules tie the choice of method to proportionality, the requirement that discovery effort match the stakes, by asking whether the time required would be unreasonable, the expense unjustified, or the volume of documents excessive.

Technology-assisted review (TAR) is supervised machine learning: attorneys code a small set of documents, and a statistical model called a classifier predicts the coding of the rest. U.S. practitioners give generative AI the same analysis they have given TAR for a decade. One practitioner estimate puts human reviewers on only about 20% of files in most reviews, with TAR handling the other 80%.

The performance evidence points the same way. In a 2024 experiment, a generative AI system given the same protocol as contract attorneys found 96% of the relevant documents, against 54% for the humans. In that 2024 experiment, its precision (the share of documents coded relevant that truly were) was lower, 60% against 91%, and the prompt had no matter-specific tuning.

The common assumption is that AI is the legally novel step. It is not. An analysis of 5 sources on review technology shows one pattern: the method is accepted, and the supervision is what gets tested. For a small firm, permission is rarely the obstacle.

Acceptance is not uniform, however. In a November 2025 r/ediscovery thread, one commenter wrote that CAL/TAR "eventually became pretty much standard practice," while cautioning that Relativity's aiR "isn't capable of handling all review scenarios and/or document types." CAL, or continuous active learning, is the TAR variant that re-ranks documents as reviewers code them. Some clients still ask for linear review, meaning document-by-document human review.

Where the tool runs matters as much as what it does. Relevant e-Discovery runs processing single-tenant, or inside your own AWS account under your own keys, with no vendor retention and no model training. Vendor contracts deserve the same close reading, including whether your AI review vendor can swap models mid-matter.

That leaves the third question on the list, the one a no-training clause never answers: who, under the order in your matter, is allowed to receive the other side's documents?

Does a Protective Order Restrict Loading the Other Side's Documents Into an AI Tool?

Yes, it can. A protective order limits who may receive confidential material, so a shared cloud AI tool may be an unlisted recipient even when it never trains on the documents.

A protective order is the court order governing how confidential discovery material may be used and disclosed. Its authorized-persons clause lists who may see designated material.

Start with where the duty comes from. No Model Rule of Professional Conduct explicitly requires opposing counsel to protect the producing party's client data, absent a confidentiality agreement, protective order, or other applicable order. Model Rule 1.6(c) covers "information relating to the representation of a client," meaning the lawyer's own client. The other side's documents are protected by what the parties signed.

A firm can ask three different questions about an AI tool. Only one of them belongs to the order.

Most guidance on generative review stops at the first two rows, where the worry is a party's own production: inaccuracy, and the inadvertent release of privileged or confidential information. Forum advice to small firms sits there too: in a January 2026 r/legaltech thread, a commenter urged a boutique firm to test whether AI tools could take the "grind-y parts" of review "while keeping humans on privilege/nuance," which is a second-row answer.

The third row turns on access, not learning. Even a private, in-house generative AI tool could run afoul of a protective order if firm users outside the litigation can reach the confidential material. Technical staff can hold that reach: in a 2024 r/sysadmin thread, an employee of a managed service provider (an outside IT firm) said its staff load mailbox exports for clients under a "hard rule" that they will not dig through the emails. If colleagues down the hall can be the problem, a vendor's shared environment deserves the same question.

Judges frame the duty the same way. A January 2026 recap of a TransPerfect Legal panel reports judges saying that lawyers "remain ultimately responsible for protecting client data and ensuring compliance with confidentiality agreements" when third-party AI vendors assist. One judge was blunter: "You can't ensure confidentiality if you don't know how the tool works." The recap ties competence under Rule 1.1 to technological literacy, one more way e-discovery can make you a better lawyer.

• Before: The vendor does not train on our data, so we are covered.
• After: The order lists who may hold this production. Is the vendor on that list, and can anyone outside the matter reach it?

Deployment changes the answer. Relevant e-Discovery describes in-account deployment under the firm's own keys as a trust posture no consumer AI tool and almost no small-firm tool can match. Here the effect is specific: the production stays in an environment the firm controls. Orders differ in wording, though, so the text in your matter governs.

The next step, then, is not a vendor questionnaire. It is the order itself, read with the tool's data path beside it.

How Should a Small Firm Vet and Run AI Review on a Produced Collection?

Read the order's disclosure and use terms, confirm where the tool runs and what it keeps, then validate the review and preserve an audit trail.

I would run four steps, in this order, before a single produced document is loaded:

1. Read the disclosure and use clause. Note who may receive designated material, for what purpose, and whether outside service providers are covered.
2. Confirm the deployment model and retention terms. Ask where processing runs, who holds the encryption keys, who inside the firm can reach the matter, and what the vendor keeps afterward.
3. Validate before you rely. Test the instructions on a small labelled set, then on a larger sample.
4. Keep the record. Log who touched the production and tie every AI output to its source document.

Steps one and two answer the order. Steps three and four answer the challenge that follows. A validated review in the wrong environment still breaches the order. A clean order reading with no validation record still loses the challenge.

Validation is the documented check that an AI tool's coding matches what a knowledgeable attorney would decide. On Reed Smith's Tech Law Talks podcast, Marcian Krieger described a starting set of 100 documents: "about 30 documents that you absolutely know are relevant," about 30 known not relevant, and 40 randomly selected. The test then widens to a thousand documents, and every prompt revision reruns the whole thousand. Krieger adds that reviewers must read the tool's stated rationale, because a correct score with an off-base rationale "means you just got lucky."

That work is front-loaded. The same discussion put prompt drafting at "40, 50, 60 hours of senior attorney time." Budget for it.

The record is where tooling earns its place. Relevant e-Discovery keeps immutable originals with content hashing (a digital fingerprint that exposes any later change), append-only audit trails, and a documented chain of custody, so the process holds up if opposing counsel challenges it. Its outputs link back to the exact exhibit they came from, which lets an attorney verify with one click before filing. I treat that feature as the practical floor, since source-linked review is the new defensibility bar.

Can a small firm afford this? On fees, the company's answer is direct: AI-assisted review runs cents per document, against dollars per document and roughly $19K/GB for manual review, a difference you can see on your own matter. Ask for that comparison on your own collection before you commit.

Start with step one this week: pull the signed order and mark every person and entity it allows to hold the other side's documents.

Can You Run Opposing Counsel's Documents Through AI Review?

Yes, when the protective order permits it and the tool keeps the production inside an environment your firm controls. Check the order first, then the deployment, then the price.

Price is becoming the easy part. ComplexDiscovery's Winter 2026 eDiscovery Pricing Survey reports that hybrid and per-document billing "each claim roughly 28% of reported primary models" for GenAI-assisted review, with $0.11 to $0.50 per document "emerging as a competitive zone." These are self-reported perceptions, not verified transaction records. Still, a band is forming. As price settles, permission becomes the variable that decides whether review can start.

I expect the advantage to go to the firm that can answer one recipient question plainly: who, beyond the people the order lists, can reach this production? When the answer is no one, the conversation with opposing counsel is short. Pull the order in your current matter today. Find the clause that names who may receive confidential material, then ask your vendor where the documents will sit.