How Can M&A Attorneys Use AI to Classify Data Room Documents Without Making Disclosure Decisions?

Assign owners and approve the intake environment first

Name a supervising M&A attorney as the taxonomy owner and escalation reviewer. Assign a document coordinator to reconcile incoming files and route exceptions. Have a designated technology administrator verify the processing environment and permission boundaries. Where a reviewer lacks authority or expertise to resolve an issue, the supervising attorney should assign the appropriate specialist rather than allowing the model to settle it. This allocation follows the NIST AI Risk Management Framework's emphasis on documented responsibilities and differentiated human-AI oversight. [2]

Approval to organize a data room is not automatically approval to send its contents to any AI service. ABA Formal Opinion 512, issued in July 2024, addresses generative AI under the ABA Model Rules. It calls for evaluating confidentiality risks before inputting client information and understanding relevant terms and privacy policies. Local professional rules, client instructions, and matter-specific restrictions need separate consideration by qualified counsel. Do not infer that every AI use requires identical consent. [1]

Before intake, establish who can access prompts, extracted text, outputs, and retained copies. Review provider terms on retention, training use, and deletion, and confirm relevant settings with the administrator. If these questions remain unresolved, use synthetic documents or keep classification manual. A local deployment or a contractual restriction is a choice to assess, not proof of confidentiality or privilege preservation.

Define categories that describe documents, not release rights

Build a taxonomy, meaning a controlled set of categories with written definitions. For each category, specify what belongs, what does not, and when to escalate. Illustrative categories might include organizational records, commercial agreements, employment documents, intellectual property records, and dispute materials. Have counsel approve the definitions before testing. Avoid categories such as safe to disclose, buyer approved, or nonprivileged, because those ask the classifier to make decisions outside its assigned task.

Use narrow distinctions only when they serve an actual review need. For example, an illustrative supply agreement may fit commercial agreements even if it includes intellectual property provisions. Define whether the queue permits secondary labels or requires one primary category with a mixed-content flag. A lawyer-approved rule should resolve that structural choice, not a new model interpretation on each run.

Keep taxonomy versions and preserve a route for unknown document types. When reviewers repeatedly disagree, revisit the definition before blaming the model. An unclear category boundary makes the reference labels unreliable and leaves the team unable to distinguish a classification error from an inconsistent instruction.

Make every suggestion traceable to the original file

Maintain the original file and an intake inventory as the source of truth. Give each document a stable identifier and record its version, source location, and whether extraction succeeded. Extracted text is a processing aid, not a substitute for checking the original. The coordinator should reconcile the inventory against processing results so a failed upload or unreadable scan does not silently disappear from the queue.

A useful queue entry records the document identifier, proposed category, taxonomy version, supporting page or passage, uncertainty reason, assigned reviewer, and review status. Also record the tool configuration used for the trial. NIST's framework recommends documenting test sets, metrics, and evaluation tools, and assessing performance under conditions similar to deployment. These records make that approach concrete for classification. [2]

Require the reviewer to open the cited source and confirm that the passage exists in the identified version. Check whether an attachment, signature page, or later amendment changes the document's identity. Keep explanatory excerpts as short as useful and restrict them like the underlying file. A category or queue summary can itself reveal sensitive information.

Allow uncertainty instead of forcing a category

The classifier should return needs review when evidence is missing, contradictory, or outside the taxonomy. Separate reasons such as unreadable text, missing attachment, mixed document bundle, ambiguous category boundary, and unsupported document type. Those reasons tell the coordinator whether to repair extraction, obtain a missing file, or send the entry to counsel.

For an illustrative bundle containing an employment agreement and a legal memorandum, do not force the whole file into employment documents and treat the remainder as irrelevant. Flag mixed content for attorney review. Whether to separate the bundle or preserve it with multiple descriptors should follow the matter's document-handling instructions. Any privilege or disclosure assessment belongs to counsel and the relevant approval process, not the category suggestion.

Do not interpret a model-generated confidence score as proof of correctness. If a score is available, test how it behaves against reviewed examples before using it to prioritize work. Even a strongly expressed suggestion should retain its source evidence and human review requirement. NIST emphasizes evaluating validity and reliability in context and documenting limits on generalization. [2]

Test the taxonomy on a representative, permissioned pilot

Begin with synthetic examples to test the queue structure and permission separation. If the intake environment is approved, select a restricted pilot set that reflects the expected file types. Include ordinary agreements as well as amendments, similar-looking documents, scans, missing pages, and mixed bundles. Easy examples alone cannot establish whether the workflow handles the documents that create review problems.

Have qualified reviewers label the pilot independently of the AI output, then resolve disagreements under the approved taxonomy. Keep some reviewed documents aside to test the revised workflow rather than repeatedly adjusting it against the same examples. NIST recommends documenting evaluation methods and testing under deployment-like conditions; the specific pilot design here is a workflow recommendation, not a prescribed legal standard. [2]

Measure which categories are confused with one another, which relevant categories are missed, and how often uncertain results arise. Record unsupported source references and extraction failures separately from category errors. For each category, examine both wrongly included documents and documents that should have been included but were missed. Overall agreement can conceal an important weak category.

Set acceptance criteria with the supervising attorney before the trial. Do not borrow a universal accuracy threshold or use a vendor's headline result as the matter's acceptance test. Decide whether each observed error is tolerable in a fully reviewed queue, needs a narrower taxonomy, or requires manual handling. Passing this pilot does not approve disclosure.

Preserve manual overrides and resolve disagreements

Give reviewers clear actions: accept the descriptive category, replace it, mark it uncertain, or escalate. Preserve the original suggestion alongside the reviewed category and a concise reason for any override. The reviewer should check the original document, not merely endorse the model's explanation. ABA Opinion 512 warns against uncritical reliance on generative AI output and discusses accuracy review appropriate to the task. [1]

Do not let a later batch run silently replace an attorney's reviewed label. Changed documents and taxonomy revisions should create a new review task that preserves the prior decision. A repeated correction may indicate a category definition problem, a missing source feature, or a limitation of the tool. Resolve the cause before expanding the workflow.

Keep override notes factual and limited. Classification comments should explain the document type or source issue rather than become an uncontrolled repository of legal advice. If a separate legal assessment is necessary, route it through the matter's approved process. Acceptance of a category should mean only that the descriptive classification has been reviewed.

Test that category changes cannot grant access

The classifier should have only the permissions needed to process the approved intake set and write suggestions into the review queue. It should not be able to invite users, change access groups, move files into externally visible locations, or approve release. NIST SP 800-53 Revision 5 separates access enforcement based on approved authorizations from least-privilege limits for users and processes. Use those controls as design references, not as a claim of certification. [3]

Have the administrator test the actual behavior with synthetic files and separate test accounts. Change a category, accept a suggestion, and apply an override. None should make the file, preview, extracted text, or queue note visible to a previously unauthorized account. Check whether moving between folders inherits permissions, since a descriptive reorganization must not become an indirect release action.

Maintain an independent human authorization process for disclosure and access. A reviewed classification may help counsel find a document, but it cannot replace assessment of the relevant restrictions and required approvals. This is a recommended workflow boundary, not a statement that every transaction has the same disclosure obligations.

Specify stopping conditions and a manual fallback

Stop the affected pilot or processing batch if an unauthorized account can see protected material, a label change alters access, or the tool processes documents outside its approved scope. Pause affected entries when source references cannot be verified or extraction omits material content. Preserve the originals and queue history, then route any suspected disclosure incident through the firm's established response process.

Pause expansion when repeated errors exceed the criteria agreed before testing, reviewers cannot clear the queue responsibly, or the tool's terms or configuration change materially. Resume only after the accountable attorney and administrator resolve the issue and test the affected behavior again. NIST treats monitoring and reassessment of metrics and controls as continuing activities, not a one-time acceptance event. [2]

The fallback is a manual queue using the same inventory and taxonomy. Keep new suggestions distinguishable from reviewed classifications. Do not reduce human review merely because the trial produced plausible explanations or because a deadline is approaching.

Hand off a reviewed index, not a disclosure instruction

The implementation outcome is a reviewed document classification queue that does not authorize disclosure or access. Its handoff should identify reviewed categories, unresolved exceptions, missing source material, and the owner of each outstanding task. Keep access decisions in their separate process. That gives counsel an organized index without converting a descriptive label into a legal conclusion.

Acquivo is a software platform that delivers the entire sell-side M&A process, valuation, marketing materials, buyer matching, and deal management, on one platform for business owners, M&A attorneys, and boutique investment banks.

Acquivo is user-operated software with no success fee. The classification framework above is a recommended process, not an assertion that Acquivo provides a particular AI model, permission control, or integration. Attorneys retain responsibility for legal judgment and applicable professional obligations; authorized users retain control over process decisions. Acquivo does not represent clients or make disclosure decisions on their behalf.