By Ramyar Daneshgar
Security Engineer | USC Viterbi School of Engineering

Disclaimer: This article is for educational purposes only and does not constitute legal advice.

1. Executive Summary

The discovery battle unfolding in The New York Times Company v. Microsoft Corp. and OpenAI represents one of the most consequential privacy disputes of the modern AI era. In November 2025, OpenAI petitioned a federal judge in the Southern District of New York to overturn an order requiring it to turn over approximately 20 million ChatGPT conversations as part of the Times’ copyright infringement lawsuit. OpenAI warned that complying with the order would expose sensitive user information, even if stripped of usernames and identifiers, because AI conversations often contain embedded personal or confidential business data.

The Times argues the opposite: that anonymized logs represent standard, targeted discovery necessary to prove the scale of alleged copyright infringement. It asserts that strong protective orders, combined with the fact that OpenAI already uses chat logs internally, sufficiently mitigate user-privacy concerns.

This dispute is not merely a procedural fight. It raises structural questions about the future of AI governance:
• whether user conversations with AI systems should be treated like emails or medical records for discovery purposes;
• whether “anonymization” meaningfully protects privacy when data comes from interactive, context-rich conversations;
• and whether companies offering generative AI tools must redesign their entire data-retention architecture to withstand litigation risk.

For cybersecurity and privacy counsel, the case is a watershed moment. The court’s ultimate decision will reverberate across the entire ecosystem of SaaS companies that store user inputs, whether those inputs contain legal memos, source code, medical histories, trade secrets, or internal strategy documents. It signals that AI prompts and outputs are no longer ephemeral exchanges; they are discoverable evidence.

2. Case Background: The Copyright Suit That Became a Data-Governance Battle

The underlying lawsuit was filed on December 27, 2023, when The New York Times accused OpenAI and Microsoft of unlawfully using millions of Times articles to train GPT-based systems without permission. The complaint claimed that ChatGPT could reproduce Times content nearly verbatim, including paywalled stories, and alleged direct copyright infringement, contributory infringement, DMCA violations, and unfair-competition theories under federal law.

By April 2025, Judge Sidney Stein allowed the Times’ fundamental copyright claims against OpenAI and Microsoft to proceed. That decision preserved the heart of the case: whether training on copyrighted news articles constitutes unauthorized copying and whether the resulting model outputs infringe when they replicate protected material.

As the case moved into discovery, the Times sought large-scale access to chat logs to demonstrate what it considers the most important element of the case: how the model behaves in real-world use. The Times argues that isolated demonstrations or curated examples are insufficient and that proving infringement requires analyzing an expansive dataset of user prompts and outputs.

To accomplish this, it requested approximately 20 million anonymized logs-a massive corpus that, in its view, would reveal whether GPT routinely outputs copyrighted content and whether those outputs correlate with specific categories of inputs. The court initially granted this request.

This move forced the case out of the realm of copyright theory and into the far more complex terrain of data retention, privacy, and the permissible limits of discovery in large-scale AI systems.

3. OpenAI’s Position: A Privacy Catastrophe Disguised as Discovery

OpenAI’s November 2025 filings warn that the Times’ request would set a dangerous precedent. They emphasize that chat logs contain intensely personal data, because users often rely on ChatGPT as a confidant-sharing medical histories, financial information, family disputes, contract drafts, or internal corporate documents. Even if explicit identifiers are removed, the content itself may uniquely identify a person or a company.

OpenAI argues that:

3.1 Millions of non-parties would be swept into a federal lawsuit

The logs represent the conversations of individuals and businesses wholly unrelated to the dispute. OpenAI maintains that discovery cannot justify exposing millions of uninvolved users to legal scrutiny, especially when those users were not warned that their messages might one day be processed as part of a mass discovery order. According to OpenAI, “99.99%” of the logs requested have no relevance to the alleged infringement claims.

3.2 Anonymization is illusory in conversational data

OpenAI’s filings highlight that anonymization may fail when conversations involve marital disputes, geopolitical details, employer-specific acronyms, internal procedures, source code, or medical details. When combined with external data sourcesor even the linguistic patterns within the chat-identification becomes possible. The nature of AI conversations makes them akin to structured narrative fingerprints.

3.3 User trust is at risk

OpenAI argues that its relationship with users depends on confidence that conversations-deeply personal and often spontaneous-will not be exposed. Even the perception that court orders can pry open years of user histories could damage its credibility and harm the broader AI ecosystem. OpenAI also notes that it is no longer under a prior preservation order requiring indefinite retention of chat logs, signaling its desire to limit storage of sensitive content.

3.4 Discovery must be proportional

Under Rule 26(b) of the Federal Rules of Civil Procedure, discovery must be proportional to the needs of the case. Requiring tens of millions of logs, OpenAI argues, is unnecessary and excessive. Targeted sampling, controlled testing, or producing model-weights-related evidence would be more proportional, less invasive, and adequately probative.

Together, these arguments frame the order not as a disclosure matter, but as an unprecedented privacy intrusion with systemic consequences.

4. The New York Times’ Position: This Is Routine Discovery With Protection

The Times argues that OpenAI is overstating the privacy risks and using the rhetoric of user protection as a shield against producing relevant evidence. It notes that the requested logs are:

• Anonymized (i.e., with direct identifiers removed).
• Subject to a strong protective order, which restricts who can view the data, how it is used, and imposes sanctions for misuse.
• Similar to the types of sensitive ESI-emails, internal messages, patient data, corporate files-that courts routinely handle in civil litigation.

The Times also points to OpenAI’s own representations about how it handles user data. OpenAI’s terms state that user inputs may be used for improving services, for legal compliance, and, in some cases, for training. This, NYT argues, undermines OpenAI’s contention that privacy expectations were firmly established in its favor.

The Times further asserts that OpenAI has already mishandled or lost some relevant training-data evidence earlier in discovery, and therefore broader access is justified to ensure the integrity of the record. One court filing referenced incidents where OpenAI engineers accidentally erased evidence tied to training datasets-an error NYT argues merits heightened transparency.

In short, the Times contends that this is not a “privacy case”; it is a copyright case where OpenAI is trying to obscure evidence behind an exaggerated privacy narrative.

5. The Broader Reality: AI Prompts Are Now Discoverable ESI

Regardless of who prevails, the case makes one point inescapable: AI chats are discoverable evidence.

In industries ranging from finance to healthcare to law, employees paste proprietary or sensitive information into AI tools. Courts have begun to treat these conversations the same way they treat internal emails, Slack messages, or cloud-stored files. Legal commentators emphasize that AI chat logs now fall squarely within the realm of electronically stored information (ESI) subject to civil discovery.

But AI conversations introduce additional complexity. Each chat is a narrative containing context, emotion, instructions, and details that may uniquely identify individuals. “Anonymization” of such data risks being superficial, because any sufficiently detailed narrative can re-identify the speaker.

Academic research supporting this concern shows that:
• AI providers frequently reuse user inputs.
• Privacy disclosures around AI interactions remain vague.
• Even anonymized AI datasets are subject to re-identification using statistical inference or linkage attacks.

Thus, companies cannot treat AI chats as ephemeral. They are records. And any record can be compelled.

6. The Legal Landscape: Privilege, Confidentiality, and the Discovery Standards at Play

The OpenAI case forces counsel to grapple with issues that have no clear precedent.

6.1 Attorney–client privilege

If lawyers or internal legal teams paste privileged communications into an AI tool, courts may have to decide whether privilege was waived. This question is now central to bar-ethics guidance nationwide, and the OpenAI case underscores how these logs could surface in litigation involving the AI provider-or the AI user.

6.2 Trade secrets and confidential business information

Corporate employees routinely paste:
• source code,
• negotiation strategies,
• vulnerability reports,
• incident response notes, and
• proprietary datasets
into chat interfaces, believing the tool is a secure workspace.

In a discovery dispute like this one, those inputs could become reviewable by opposing counsel.

6.3 Proportionality under Rule 26(b)

The OpenAI case pushes courts to define what “proportionality” means in the context of AI training and behavior. Courts have never been asked to review millions of chat logs from a proprietary AI model as part of copyright litigation. This case will shape the acceptable boundaries for future disputes.

6.4 Privacy law intersections

AI chat logs contain personal data that may be regulated under:
• the GDPR (purpose limitation, minimization, retention limits),
• CPRA/CCPA (sensitive data, opt-out rules), and
• emerging state laws with enforcement momentum.

If a court compels production, counsel must evaluate retention obligations, legal-basis exceptions (such as “defense of claims”), and conflicts between litigation holds and data-deletion requests.

7. What This Means for Companies Building or Using AI Systems

For any SaaS or enterprise organization, this case is not an anomaly. It is a preview of discovery norms.

7.1 Rethinking logging and data minimization

Most AI systems log inputs by default, often indefinitely, to support evaluation, debugging, and model improvement. The OpenAI case demonstrates that these logs carry massive legal exposure. Organizations should reassess the necessity of long-form logging and consider architectural alternatives such as token-level logging, partial redaction at ingestion, or on-device processing for sensitive use cases.

7.2 Privacy notices and transparency

Privacy notices must explicitly describe:
• whether prompts and outputs are logged,
• for how long they are stored,
• for what purposes they are reused, and
• under what circumstances they may be disclosed to courts or regulators.

These disclosures are now essential to shielding against consumer protection claims and aligning with best practices in data-governance transparency.

7.3 Developing AI-specific discovery playbooks

Companies should create structured playbooks addressing:
• how to identify AI logs across systems,
• how to search, retrieve, and filter those logs,
• how to anonymize or pseudonymize data defensibly, and
• how to propose sampling protocols in litigation.

Courts are increasingly expecting sophistication in managing AI-generated ESI, and companies cannot improvise once litigation begins.

7.4 Contractual protections

MSAs and DPAs should include AI-specific terms addressing:
• retention limits,
• logging configurations,
• discovery cooperation obligations,
• privacy-preserving disclosure methods, and
• allocation of costs associated with AI-related discovery.

These terms help companies avoid bearing the full burden of multi-million-record production.

8. Policy Implications: The Need for Federal AI Privacy Standards

The issues raised in OpenAI v. NYT are echoing throughout regulatory and academic discourse. Research emphasizes that AI providers frequently reuse or retain user inputs and that the current privacy labeling of AI systems is inconsistent and opaque.

Regulators may respond by:
• mandating stricter limitations on retention of conversational data;
• requiring explicit disclosures of how AI inputs are reused;
• defining standardized processes for responding to discovery demands involving AI logs; and
• harmonizing privacy obligations for interactive AI systems with those that already apply to email, messaging, and cloud services.

Without such guidance, cases like OpenAI v. NYT will define the baseline-court by court, lawsuit by lawsuit.

9. Conclusion

The OpenAI v. New York Times discovery dispute is more than a clash over evidence. It is a referendum on how society will treat the billions of conversations users have with AI systems every day. Are these intimate exchanges subject to the same discovery rules as corporate emails? Are they more sensitive than traditional ESI because they contain richer personal context? And must companies redesign the very architecture of their AI systems to avoid producing deeply personal records in future lawsuits?

The court’s decision will answer some of these questions, but the broader message is already clear:
AI chat logs are no longer merely a product feature-they are a liability vector.

Companies that do not treat them as such will face the same litigation exposure now confronting OpenAI.