Generative AI in Document Review: Where It Helps and Where It Creates Risk

Generative AI can reduce the time spent organizing, summarizing, and exploring large document collections. It can also misread context, create inaccurate summaries, expose confidential information, and produce results that are difficult to defend. The practical question is not whether legal teams should use AI. It is which tasks are suitable for it, how the results will be tested, and which decisions must remain with qualified professionals.

At On The Record e-Discovery Services, we help litigation teams in Coral Gables and throughout Florida manage electronic evidence from collection through production. Our eDiscovery services support legal teams through the different stages of the discovery process, including document processing, review, project management, and production. Our approach is straightforward: AI can support document review, but it should not replace legal judgment, established quality controls, or a defensible discovery process.

Where Does Generative AI Provide the Most Value?

The safest uses generally involve lower-risk tasks where errors can be found and corrected before they affect a legal decision.

Generative AI can speed up document review by drafting summaries, suggesting search terms, and grouping records by topic. However, it may omit important details, miss relevant language, or place ambiguous documents in the wrong category. Reviewers should compare summaries with the original records, test search terms against known responsive documents, and validate document groups through sampling.

AI can also help create timelines and flag records that may contain privileged information. Because it may connect the wrong date to an event or overlook protected communications, every timeline entry should link to its source, and an attorney should confirm final privilege decisions.

During production preparation, AI may assist with repetitive organizational work. Before releasing any material, the legal team should complete documented checks for confidential content, metadata, missing records, and formatting errors.

A useful rule is simple: the greater the consequence of an error, the more human oversight the task requires.

Can AI improve early case assessment?

Early case assessment is one of the stronger uses for generative AI document review. A legal team may receive thousands of emails, reports, messages, and attachments before it understands the people and events involved.

AI can suggest recurring names, dates, organizations, and subject areas. It may also prepare a preliminary chronology. These results should be treated as leads rather than established facts.

For example, an AI-generated timeline may state that a contract was approved on May 12. A reviewer should confirm whether May 12 was the approval date, the email date, or simply a date mentioned in an attachment. That small distinction can become important later.

Can AI help develop better searches?

Keyword searches depend on the terms selected by counsel. Employees may use abbreviations, project names, nicknames, or technical language that the legal team does not initially know.

AI can examine a controlled sample and suggest related terminology. Professionals then test those suggestions against documents already known to be responsive. If a proposed search fails to retrieve a key document, the search needs more work before it is applied to the full collection.

This testing step is often overlooked. A term that sounds relevant is not automatically an effective discovery term.

Is AI useful for summaries?

AI can create quick summaries of long email threads, contracts, reports, and repeated forms. This helps reviewers decide where to focus their attention.

The summary should always remain connected to the original record. Generative systems can omit exceptions, confuse an attachment with its parent email, or present an inference as fact. A sound review platform should allow the reviewer to move directly from the summary to the supporting document.

Where Does AI Create the Greatest Risk?

How can client confidentiality be compromised?

Uploading discovery materials to an unapproved public AI tool can expose client information. Before using any platform, the firm should determine:

• Whether prompts and documents are retained
• Whether client data is used for model training
• Where the information is stored and processed
• Who can access the uploaded material
• Whether audit logs and permission controls are available
• How information is deleted when the matter ends

A paid account does not automatically make a tool suitable for confidential legal work. The contract, security controls, retention policy, and actual configuration all matter.

The Florida Bar’s Ethics Opinion 24-1 says lawyers using generative AI must take reasonable precautions to protect confidential information. It also recommends obtaining informed client consent before disclosing confidential information to a third-party generative AI system.

Why should AI not make final privilege decisions?

Privilege review depends heavily on context. A routine-looking email may be privileged because it was created to request legal advice. An attachment may have a different status from the email carrying it.

AI can flag potentially sensitive documents, but uncontrolled final decisions create serious risk. A false negative may expose protected communications. A false positive may cause responsive information to be improperly withheld.

Experienced managed document review teams use written privilege protocols, attorney supervision, family-document analysis, quality-control sampling, and escalation procedures. AI may help prioritize the work, but an attorney should confirm consequential privilege decisions.

What happens when AI produces a convincing error?

Generative AI creates probable language rather than guaranteed facts. It can combine details from different records, invent a quotation, misidentify a participant, or describe an unavailable attachment as though it reviewed it.

The polished wording makes these errors harder to notice. For that reason, every fact placed in a case chronology, witness outline, privilege log, or production decision should link back to a specific source.

Is Generative AI the Same as Technology-Assisted Review?

No. Technology-assisted review, commonly called TAR, generally uses reviewer coding decisions to prioritize or classify documents. Its performance can be evaluated through sampling and measures such as recall and precision.

Generative AI creates new text in response to instructions. Its output may vary when the prompt, model, settings, or surrounding context changes.

For large review populations, established TAR methods may be more suitable for defensible classification. Generative AI may be better for summaries, issue exploration, and search-term development. One is not automatically superior. The choice depends on the data, legal issues, error tolerance, and intended use of the results.

What Does Legal AI Compliance Require in Florida?

Legal AI compliance involves more than cybersecurity. Lawyers must consider competence, confidentiality, supervision, client communication, reasonable fees, and verification.

The American Bar Association’s Formal Opinion 512 addresses these duties when lawyers use generative AI. It makes clear that using AI does not remove the lawyer’s responsibility for the final work.

Before approving AI in legal document review, document review law firms in Florida should answer:

1. What task will the system perform?
2. What confidential information will it receive?
3. Who will verify its output?
4. How will accuracy be measured?
5. Which decisions remain with an attorney?
6. How will the workflow be documented?
7. How will an incorrect result or data exposure be handled?
8. How will AI-related costs and time savings affect billing?

NIST’s voluntary AI Risk Management Framework also provides a useful structure for governing, measuring, and managing AI risk. It does not replace legal requirements, but it can help firms build repeatable internal controls.

How Do Professionals Build a Defensible Workflow?

Start with a limited, low-risk task. Instead of allowing AI to classify an entire production, test it on search suggestions or summaries from a controlled document set.

Create a baseline reviewed by qualified professionals. Compare the AI results with that baseline, record false positives and missed documents, and determine whether the error rate is acceptable for the specific task. A standard suitable for internal organization may not be appropriate for privilege review.

Keep a record of the tool, version, approved users, instructions, testing method, sampling results, and significant workflow changes. Quality checks should continue when new custodians, languages, or file types are added.

These controls should also fit within the larger discovery process. Effective eDiscovery project management helps coordinate review requirements, deadlines, workflows, and quality-control procedures instead of treating AI as a separate step.

A practical cost-saving measure is to establish review checkpoints early. Finding a classification problem after 500 documents is much less expensive than discovering it shortly before production.

Frequently Asked Questions

Can generative AI replace attorneys during document review?

No. It may assist with organization and analysis, but attorneys remain responsible for privilege, legal judgment, supervision, and representations made to clients or courts.

Is it safe to upload discovery documents to a public AI tool?

Not without reviewing its confidentiality, retention, security, and training policies. Confidential records should only enter an approved environment with appropriate protections and consent where required.

Can AI lower review costs?

It can reduce repetitive work when used for a defined, tested task. Savings may disappear if the system requires extensive correction or creates a privilege or production problem.

When should an eDiscovery professional become involved?

Professional help is recommended when a matter involves sensitive information, multiple custodians, mobile devices, large data volumes, privilege concerns, unfamiliar formats, or a short production deadline.

How Should Legal Teams Move Forward?

Generative AI works best as a controlled assistant, not an independent reviewer. Legal teams should define its role, assess the consequences of an error, protect client data, validate results against human-reviewed documents, and keep attorneys responsible for important decisions.

If your firm or organization in Coral Gables is considering generative AI document review, contact us to discuss your matter. Our team can help you evaluate where AI may improve efficiency, where established review methods remain safer, and how to build an accurate, secure, and defensible process from collection through production.

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