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Sam Altman beside a glowing data stream and stacked court papers, illustrating the OpenAI sanctions motion

The New York Times Seeks Sanctions Against OpenAI Over Deleted Chat Logs and Discovery Dispute

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The newspaper and other publishers allege that OpenAI hid evidence about how ChatGPT used copyrighted news content.

 

The New York Times and several other publishers asked a federal judge to sanction OpenAI, accusing the company of misleading the court about its ability to search training data and ChatGPT records while continuing to delete or compress billions of user conversations during the litigation.

The motion, filed in federal court in New York, is the latest development in the publishers’ copyright lawsuit against OpenAI and Microsoft. The plaintiffs argue that OpenAI obstructed discovery, failed to preserve evidence, and made inaccurate statements about what information it could retrieve from its systems.

Two questions shaped the case from the beginning: how much news content OpenAI used to train its models, and how often ChatGPT reproduces or relies on copyrighted material in its responses. The motion argues that records of ChatGPT conversations and internal analyses could help answer those questions.

The publishers allege that OpenAI repeatedly told both the court and the plaintiffs that it lacked practical tools to search its training datasets and ChatGPT output logs for copyrighted material. According to the filing, a second deposition of OpenAI executive Vincent Monaco later revealed that the company had already built tools capable of searching those datasets and conducted related analyses before the lawsuit was filed.

The motion also focuses on ChatGPT “output logs,” or records of user conversations, which the publishers say OpenAI continued to delete or compress even after the court ordered the company to preserve them. 

A separate dispute concerns a sample of 20 million ChatGPT conversations that the court ordered OpenAI to produce as part of the discovery process. The publishers contend that OpenAI deleted part of that sample, replaced other conversations, and applied billions of redactions, making the material difficult to use.

Further, the filing references an internal OpenAI effort, Project Giraffe. The publishers say the project included tests and datasets designed to measure when ChatGPT reproduced copyrighted material, contradicting OpenAI’s earlier claims that it lacked the tools to conduct such searches. The motion argues that OpenAI failed to disclose those capabilities during discovery.

The publishers are asking the court to:

  • Bar OpenAI from using the disputed sample of 20 million ChatGPT conversations to support its defense that ChatGPT does not systematically reproduce copyrighted material;
  • Assume that ChatGPT records either contained, or would have shown if properly preserved and produced, substantial amounts of copyrighted news content;
  • Prevent OpenAI from arguing the opposite at trial;
  • Instruct the jury that those conclusions should be treated as established and that OpenAI deleted billions of chat logs; and
  • Require OpenAI to pay attorneys’ fees, expert costs, and other expenses related to the discovery fight.

The court has not ruled on the motion, and the allegations in the filing have not been proven.

Robot icon above an AI search bar on a dark digital interface, illustrating AI as the top driver of data security decisions.

AI Becomes the Top Driver of Enterprise Data Security Decisions

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Organizations now rank protecting AI systems and the data they use ahead of preventing traditional data leaks when choosing data security tools.

Key Takeaways

  • 64.4% of respondents said protecting the data used by AI systems is their top reason for evaluating data security tools, surpassing the prevention of the unauthorized removal of sensitive data (56%).
  • Better visibility into the data used by AI systems ranked as the most important capability in a data security posture management (DSPM) platform at 57.8%.
  • Respondents identified moving data to train AI systems as the leading reason sensitive information ends up in unauthorized locations (40.9%).
  • 76.4% of respondents expressed concern about prompt injection attacks targeting AI systems.

Enterprise security priorities are shifting as organizations expand their use of AI, according to the report Leveraging DSPM and Artificial Intelligence to Solve Data Security Challenges from Enterprise Management Associates (EMA). The research found that organizations now prioritize protecting AI systems over preventing traditional data leaks when choosing data security tools.

The report surveyed 225 IT professionals, security practitioners, data governance leaders, and technology executives across North American enterprises about their approaches to protecting sensitive data in AI-enabled environments. Much of the research focuses on DSPM: software that helps organizations discover, classify, and protect sensitive data across cloud and on-premises environments.

AI becomes the top security priority

The survey found that 64.4% of respondents ranked protecting the data used by AI systems, including prompts and training data, as their top priority when evaluating data security tools. That surpassed preventing traditional data leaks at 56%. According to EMA, it is the first time an AI-related priority has become the leading reason organizations evaluate such security tools.

The shift reflects the capabilities organizations value most. Better visibility into the data used by AI systems and the responses they generate ranked first at 57.8%. It finished ahead of more traditional capabilities such as automatically identifying sensitive data, tracking where data moves, and discovering where sensitive data is stored.

AI training creates new data security risks

The report found that AI is also changing how sensitive data moves through organizations. 40.9% of respondents identified moving data to train AI systems as the leading reason sensitive information ends up in unauthorized locations. That exceeded developer testing environments (25.8%) and backup or replication errors (16%).

AI threats continue to grow

Security leaders also reported growing concern about threats targeting AI systems. More than three-quarters of respondents expressed concern about prompt injection attacks targeting AI systems. In addition, 57.8% said emerging AI regulations are driving stronger AI governance efforts, placing them ahead of established compliance frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act.

Governance still trails AI adoption

While organizations are investing in security features designed for AI systems, governance has not advanced at the same pace. Only 37.3% of respondents said their organizations have a fully implemented AI governance framework, while 56.4% reported their frameworks are only partially implemented. Several other surveys fielded in 2026 report similar findings relating to this “governance gap.”