TL;DR:
A July 14, 2026 order in Dow Jones & Company, Inc. v. Perplexity AI, Inc. (SDNY, No. 24-cv-07984-KPF) granted Perplexity’s motion to seal key discovery data related to its data repositories and “Highly Confidential – Inspection Data,” including Dow Jones Snapshot data and live-system details. The ruling underscores the court’s willingness to protect sensitive AI infrastructure information during cross-border discovery, while still permitting targeted production under a protective framework. For trial teams, the decision reinforces the importance of precise protective orders and disciplined data-handling protocols when AI systems and data repositories drive key litigation milestones. This development is timely for practitioners navigating AI discovery, data governance, and evidentiary protections in federal court. Objection Academy can help teams rehearse objections and build trial-ready responses to issues around sealed data, data repositories, and inspection data in AI-related disputes.
What happened
On July 13 2026 and the following days, the Southern District of New York issued a memorandum-endorsed order regarding Dow Jones & Company, Inc. and NYP Holdings, Inc. v. Perplexity AI, Inc., Case No. 24-cv-07984-KPF. The court granted Perplexity’s motion to seal in connection with disputes over the production of data repositories and related inspection data in discovery. The July 14, 2026 order (Docket 171 in the Dow Jones matter) explicitly states that the court granted the sealing application and directed the maintenance of certain docket entries under seal. The order references that Perplexity had sought to file a Sur-Reply under seal along with a supporting declaration, describing Perplexity’s live systems infrastructure, access methods, and inspectable data, all designated as Highly Confidential – Inspection Data under the Protective Order in place in the case. The decision is rooted in the court’s balancing of the presumption of public access with the need to protect sensitive business information that could cause competitive harm if disclosed. See Dow Jones & Company, Inc. v. Perplexity AI, Inc., No. 24-cv-07984 (KPF), July 14, 2026 order (PDF) and related docket entries. (cases.justia.com)
Key specifics confirmed by the order include:
- The sealing covers data repositories and related inspection data contemplated by the Sur-Reply and its declaration, including discussions about the structure and contents of Perplexity’s live systems infrastructure. (cases.justia.com)
- The court acknowledged that some information designated as Highly Confidential – Inspection Data may be sealed to protect Perplexity’s competitive interests. (cases.justia.com)
- Production schedules and conditions surrounding certain data points, such as Dow Jones Snapshot RAG data and New Snapshot RAG data, were addressed in the related briefing and order, with the court granting sealing where appropriate. (cases.justia.com)
The Dow Jones decision sits within a broader SDNY line of cases grappling with AI-powered discovery, data repositories, and the protection of sensitive system designs and logs. The July 14, 2026 order is a clear signal that federal courts will honor intentional confidentiality measures around AI data infrastructures when presented with compelling commercial or security concerns. (docs.justia.com)
Why it matters for trial teams
- Practical guardrails for AI discovery: The Dow Jones v Perplexity order demonstrates that a party can press for protective measures around core AI data assets, including source-code-related materials, system logs, and data-repository structures, without sacrificing the ability to progress discovery. Trial teams should anticipate the possibility of comparable sealing requests and plan accordingly. (cases.justia.com)
- Protective orders as strategic leverage: The court’s reliance on the Protective Order and the designation of “Highly Confidential – Inspection Data” shows how carefully crafted protective orders can shape what is discoverable and what remains shielded from public view. This has direct implications for how teams draft, negotiate, and implement protective orders at the outset of AI-related disputes. (cases.justia.com)
- Data governance and evidentiary scope: Lawyers must map the evidentiary relevance of data repositories used by AI systems to the permissible scope of discovery, while simultaneously building robust redaction and sealing strategies for sensitive data. The Dow Jones order highlights the need to articulate the data categories at issue and to tie them to protective-ordered access protocols. (cases.justia.com)
Practical steps for litigators
- Audit protective orders early: Review the current protective order to confirm whether it contains a clearly delineated category for “Highly Confidential – Inspection Data” and ensure it covers data repositories, access controls, and inspection data used in AI contexts.
- Prepare data-specific safeguards: For any anticipated production of AI-related data, draft a data-review protocol that specifies who may view the data, where the data may be stored, and how copies or logs may be filtered, redacted, or sealed.
- Plan deposition and briefing strategy: When scheduling depositions or briefing on ESI and AI data, coordinate with opposing counsel to address seal requests up front, including the timing of production, methods of secure delivery, and the process for resolving disputes about seal or redaction.
- Build a tight evidentiary narrative: In parallel with the protective-order framework, develop a trial-ready evidentiary plan that accounts for the sealed data. Prepare objections and preservation strategies to safeguard privileged or confidential materials while ensuring admissible evidence meets the parties’ burden.
- Leverage training for objections: Use targeted practice to sharpen objections during discovery disputes over AI data, data-repository disclosures, and inspection-data designations. A training platform focused on trial objections can help counsel respond quickly to motions to seal or unseal, request redactions, or challenge overbroad data requests.
Objection Academy’s role in this context
In AI discovery settings, trial teams confront frequently nuanced objections about scope, relevancy, and confidentiality. Objection Academy offers practical, repeatable drills that help attorneys practice the exact objections likely to arise when data repositories and inspection data are at issue. By rehearsing responses to sealing motions, privilege-logging challenges, and authentication questions around AI-generated or AI-assisted data, trial teams build courtroom readiness for real-world disputes. In addition, Objection Academy positions trial teams to leverage objection-based workflows in a way that supports efficient, credible examination of data-driven evidence, with MCLE options where applicable. This training complements protective-order strategy by ensuring counsel can pivot from negotiation to persuasive advocacy at trial, when sealed data becomes part of the record.
Takeaways for trial teams
- Expect sealing to be a live tool in AI discovery: Federal courts will protect sensitive AI data assets when properly designated and narrowly tailored through protective orders.
- Build data governance into the litigation plan: From day one, map data categories, review procedures, and seal mechanisms to avoid delays and disputes.
- Train for objections and presentation: Use robust objection training to manage sealing disputes, redactions, and the admissibility of AI data in a transparent, trial-ready manner.
- Leverage Objection Academy to sharpen readiness: Regular objection drills and evidence-focused practice prepare litigators to handle AI data issues in the courtroom with confidence.
Sources:
- Dow Jones & Company, Inc. et al v. Perplexity AI, Inc., No. 24-cv-07984 (KPF), SDNY. Order granting sealing and related entries, July 14, 2026. PDF and docket: Justia. (cases.justia.com)
Note: This timely development sits at the intersection of AI-enabled discovery and traditional evidentiary protections, offering a clear example for trial teams of how to balance transparency with confidentiality in complex digital evidence disputes. For practitioners seeking to build trial-ready skills around such issues, Objection Academy provides training aligned with these evolving courtroom dynamics.