Beyond Hallucinations: The Expanding Universe of AI-Related Litigation Risks for Corporate Counsel and Their Clients

By: Brad J. Krupicka, Partner

The legal profession’s introduction to generative AI came with a jolt. When a New York attorney submitted a brief citing non-existent cases manufactured by ChatGPT, the resulting sanctions made headlines and spawned countless CLE programs. However, if the risk is assumed to end with fabricated case law, this overlooks the broader and more significant issues.

The real risk is not limited to lawyers using AI for legal research—it extends to employees across the entire organization using AI for a variety of tasks.

The Evidence Problem Nobody’s Talking About

AI tools have become ubiquitous across corporate America, yet most organizations have no systematic way to track which documents, communications, or data were generated or substantially modified by artificial intelligence. A sales team might be using AI to draft customer communications. An HR department could be generating performance reviews with assistance from language models. Contract administrators may be letting AI “clean up” agreement language before execution.

Each of these documents could become evidence in future litigation. If it does, the organization could likely be faced with significantly more complexity than encountered in traditional document disputes.

Consider a breach of contract case where the disputed agreement was partially drafted by AI, then edited by humans, then refined again by AI. Which version governs the terms of the agreement? Can the organization even reconstruct the chain of revisions? If the AI introduced terms that neither party consciously approved, how can mutual agreement be established? These aren’t hypothetical scenarios—they’re emerging in discovery right now, and most companies can’t answer basic questions about their AI usage patterns.

When Synthetic Becomes Fraudulent

The line between negligence and fraud has always been context-dependent, but AI introduces new complications. Traditional document fraud requires intent—someone knowingly creates a false document and introduces it as genuine. But what happens when an employee uses AI to “reconstruct” a lost email or “fill in” missing details from a meeting they attended, genuinely believing the AI’s output reflects what actually occurred?

This scenario also plays out. An executive used a generative tool to recreate the substance of a conversation, convinced the AI had accurately captured the discussion based on his prompts. The opposing party had a recording. The discrepancies weren’t trivial. The question became: was this fraud upon the court, or merely negligent reliance on technology the executive didn’t fully understand?

The answer matters enormously. Fraud upon the court can trigger criminal liability, disqualification of counsel, and sanctions that extend beyond the immediate case. Yet the executive’s subjective belief in the AI’s accuracy—however unreasonable—complicated the intent analysis. Courts are still developing frameworks for these situations, which means early cases will be decided on facts and circumstances that may not favor defendants.

Discovery in the Age of Synthetic Documents

For litigators, this landscape demands a fundamental shift in discovery practice. No longer can it be assumed that a document produced by opposing counsel was created through traditional means. Every piece of evidence now requires scrutiny about its provenance.

Interrogatories should specifically address AI usage. Ask whether any party or employee used generative AI tools to create, modify, or supplement documents being produced. Request metadata showing creation dates, editing history, and software applications used. Demand preservation of all prompts used to generate AI content—these prompts are often more revealing than the outputs themselves.

In depositions, probe how witnesses created key documents. “Did you use any software tools to help draft this email?” is no longer sufficient. One must now ask specifically about AI assistants, language models, and generative tools. Many deponents won’t volunteer this information because they don’t think it’s relevant or because AI usage has become so routine they barely register it.

The Authentication Gauntlet

Federal Rule of Evidence 901 requires authentication of documents before admission. Traditionally, this meant showing that a document is what its proponent claims it to be—usually through witness testimony, distinctive characteristics, or chain of custody evidence.

AI-generated documents complicate every authentication method. A witness can testify that an email came from their account, but can they testify that they—rather than an AI assistant—wrote it? Metadata can show when a file was created, but can it reveal how much of the content was human-generated versus machine-generated? Chain of custody becomes meaningless when the document’s origin point is a black-box algorithm.

Expect to see increased reliance on expert testimony regarding AI detection and document forensics. These experts will need to analyze linguistic patterns, consistency with known writing samples, and technical markers that might indicate AI involvement. The costs of this additional layer of authentication will be substantial, and the reliability of current AI-detection tools remains questionable.

Practical Steps for Corporate Counsel

Ideally, AI governance policies should have been implemented already; however, it is important to establish them as soon as possible rather than delay any further. Start by inventorying where AI tools are being used across an organization. Risk an organization does not know exists usually can’t be managed.

Implement clear policies requiring disclosure when AI substantially contributes to any document that might have legal significance. This includes contracts, formal communications with customers or partners, regulatory filings, and internal documents that could become relevant in employment or commercial disputes. The disclosure doesn’t need to be external—but it needs to exist in your records.

Train employees on the difference between using AI as a research tool versus using it as a content generator. The former carries manageable risks; the latter creates potential evidence that may be difficult to authenticate or defend.

Establish verification protocols. Any AI-generated content that will be submitted to courts, regulators, or counterparties in formal negotiations should be independently verified by a human with relevant expertise. This verification should be documented.

The Road Ahead

In the early stages of AI-related litigation, the courts are still developing the doctrines that will govern these disputes. Early cases will establish precedents that may be difficult to overcome later. This makes it especially important to get ahead of these issues rather than waiting for them to arise in active litigation.

The lawyers who submitted those fabricated cases learned an expensive lesson about verification obligations. But the broader lesson—that AI introduces systemic risks throughout the litigation lifecycle—hasn’t fully penetrated corporate consciousness yet. The organizations that recognize this reality and act accordingly will have significant advantages when disputes inevitably arise.

The question isn’t whether AI-generated evidence will be featured in upcoming litigation. The question is whether your organization will be prepared to authenticate its own documents and challenge any opponent.

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AI litigation risks extend far beyond fabricated legal research to encompass authentication challenges, fraud concerns, and discovery complications arising from AI-generated evidence across corporate operations.

Actionable Guidance for Corporate Counsel:

1. Conduct an AI Usage Audit: Map where generative AI tools are deployed across departments—sales, HR, legal, contracts, compliance. Create an inventory of tools and typical use cases.
2. Implement Mandatory Disclosure Protocols: Require employees to flag when AI substantially contributes to documents with potential legal significance. Build this into document management systems.
3. Develop Verification Checkpoints: Establish review procedures for AI-generated content before it’s submitted externally or becomes part of the formal record. Document who verified what and when.
4. Update Discovery Response Procedures: When responding to discovery, specifically review whether produced documents involved AI generation. Consider whether voluntary disclosure of AI usage strengthens or weakens your position.
5. Revise Outside Counsel Guidelines: Explicitly address AI usage in litigation materials and require disclosure of AI involvement in brief writing, evidence preparation, or discovery analysis.

For Litigators Facing AI-Generated Evidence:

1. Expand Standard Interrogatories: Include specific questions about AI tool usage in document creation, modification, and communication generation.
2. Request Prompt Preservation: Demand preservation of all prompts used to generate AI content—these reveal intent and can expose fabrication.
3. Depose on Technology Usage: Ask detailed questions about what tools witnesses used to create key documents, including specific AI assistants and their typical workflows.
4. Engage Forensic Experts Early: Consider retaining experts in AI detection and document forensics before challenging authenticity, as these analyses take time.
5. Challenge Authentication Proactively: Don’t assume opposing counsel has properly authenticated AI-generated materials. File motions in limine to exclude documents with questionable provenance.

Risk Mitigation Priorities:

• Fraud upon the court charges can arise from negligent AI reliance, not just intentional fabrication
• Chain of custody for AI-generated documents requires new protocols beyond traditional methods
• The distinction between AI-assisted and AI-generated content matters for authentication purposes
• Current AI detection tools have limitations—don’t rely solely on automated detection

Early case precedents will shape this area significantly—avoid being the cautionary tale