New York Trade-Secret Evidence: Connect Access, AI Use and Code Changes
An IP partner does not need another collection of code similarities; the useful question is which connection to the alleged secret the record can support. A prompt, access event and later commit may appear in sequence without establishing how the code arose. For New York trade-secret teams, a source-linked provenance timeline turns those fragments into a focused comparison brief. Each link shows its supporting record, alternative explanation and remaining limitation. That gives counsel a concrete basis for the next technical inquiry without treating chronology as proof of misappropriation. Federal discovery rules already provide tools for this problem. Rule 26 makes relevance and proportionality central and permits protective orders under Rule 26(c); Rule 45 addresses nonparty production where vendors hold evidence. Evidence Rule 901 may become relevant when digital records or system processes must be authenticated. None of those provisions turns every prompt into a trade secret or every AI output into proof of misappropriation. From my forensic lens, the defensible approach is a narrow provenance record tied to access, use and the disputed technical path.
1. Define the Alleged Secret and the AI Workflow
Do not start with every repository Identify the information alleged to be secret and the development or business process in which AI could have encountered it. A broad demand for every prompt across an engineering organization can bury the relevant trail. Map the systems, users and periods connected to the specific code, documentation, model weights, datasets, business logic or confidential know-how at issue.
Map authorized and unauthorized paths separately
Record which users were permitted to access the material, which AI tools were approved, which integrations were available and what policy or technical restrictions existed. Then identify departures:
unapproved tools, personal accounts, copied repositories, external plugins or exports. The difference between an authorized workflow and an alleged misuse often matters more than the mere presence of AI.
2. Preserve Source-Code and Repository Evidence
Keep native history intactPreserve repository history, commits, branches, pull requests, issue tickets, timestamps and access records where relevant. A later snapshot of source code can show what exists now but not necessarily when a disputed feature appeared or how it evolved. Work from forensic or verified copies rather than
altering the repository during examination.
Connect AI outputs to final code
If generated code or suggestions were incorporated, preserve material prompts, outputs, developer edits and commit history that can show the path into the product. Do not assume textual similarity alone proves origin or copying. Provenance analysis should account for common code patterns, public sources, independent development and human modification.
3. Treat Prompts as Context, Not Automatic Proof
Read the whole interaction A prompt may mention confidential information without proving that the model retained, disclosed or used it in the way alleged. Conversely, a short prompt may rely on an attached file or retrieval system that contains the critical content. Preserve attachments, retrieved passages, conversation context and relevant system configuration when they materially affect interpretation.
4. Use Protective Boundaries for Technical Review
Minimize exposure during comparison Trade-secret discovery often requires giving experts or opposing teams access to sensitive material under carefully controlled conditions. Apply the same discipline to AI-related artifacts. Limit datasets, restrict users, log access, separate source and working copies, and follow protective orders or negotiated protocols. An isolated review environment can support these controls without changing the legal status of the information. Keep vendor access visible If a SaaS platform processed the material, identify contractual terms, support access, subprocessors, retention and export options. Vendor involvement does not automatically destroy secrecy or create liability, but it is part of the factual access picture. Counsel can then evaluate reasonable-measures and disclosure issues using accurate technical facts.
5. Build a Provenance Timeline
Create a timeline linking access events, prompts, model or feature versions, repository changes, file transfers, outputs, human edits and releases. The timeline should identify confirmed facts, disputed facts and gaps. This prevents the technical report from converting sequence into causation without evidence.
6. Prepare for Expert Comparison
A forensic expert may compare code, metadata, repository history, prompts, logs and system behavior, but conclusions should remain within the method's support. Similarity is not automatically copying; access is not automatically use; a hash proves integrity after hashing, not authorship. State limitations explicitly and preserve alternative explanations for counsel and the court.
Recurring Trade-Secret Finding Structure
asserted connection, supporting artifact, observed fact, alternative explanation and unresolved question. Measure the proportion of asserted timeline links supported by identified records, while listing untested alternatives separately. Do not convert that proportion into a probability of copying. Before a further technical examination, define the narrow comparison that could resolve one missing link and confirm permitted access, relevant expertise and source availability. A recurring internal review can reveal repeated evidence-intake gaps without combining sensitive source material across matters. The practical output is a better-scoped comparison brief, not a legal conclusion or a promise that a missing source can be recovered.
Author: Pouya Shafabakhsh Co-Founder, CAIO & Principal Forensic AI Auditor, Radsam Academy of AI Sovereign Governance. Independent AI governance and forensic-audit analysis. Educational information only; not legal advice.
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FAQ
What practical problem does AI trade-secret evidence solve for legal teams?
AI trade-secret evidence matters when it changes a legal, operational, or evidentiary decision. Start with the actual workflow and consequence. Then choose controls proportionate to the matter.
Which authorities should counsel verify before relying on AI trade-secret evidence?
Start with binding law, court rules, professional duties, and contracts relevant to AI trade-secret evidence. Separate guidance from mandatory authority. Recheck time-sensitive requirements before relying on them.
Which records best explain AI trade-secret evidence if later challenged?
Preserve the sources, material outputs, versions, reviewers, approvals, and exceptions tied to AI trade-secret evidence. Keep the record proportionate. It should explain consequential decisions without collecting everything.
Where should human review occur within AI trade-secret evidence workflows today?
Human review should occur before AI trade-secret evidence affects advice, evidence, filings, or external action. The reviewer needs authority to correct or stop the process. Record material overrides.
How should vendors support AI trade-secret evidence without expanding unnecessary risk?
Technology supporting AI trade-secret evidence should fit the defined workflow and data sensitivity. Review access, retention, security, exports, and vendor dependencies. Do not treat architecture as a legal guarantee.
When does independent review add value for AI trade-secret evidence decisions?
Independent review helps when AI trade-secret evidence raises unresolved technical or evidentiary questions. Define a narrow mandate first. Keep legal conclusions with counsel and technical findings within verified limits.
Which misconception most often distorts decisions about AI trade-secret evidence today?
Do not assume one tool, log, metric, or control settles AI trade-secret evidence. Context and corroborating evidence still matter. State technical findings proportionately and preserve important limitations.
A practical worksheet
Illustrative exercise. A fictional trade-secret dispute concerns whether specified confidential material entered an AI-assisted coding workflow. The worksheet traces available records without deciding misappropriation. Secret. Use counsel’s defined description of the material at issue to focus the technical inquiry and identify relevant access periods. Path. Connect supported access events, prompts or retrieval records and outputs, distinguishing observed links from untested hypotheses. Code. Relate the relevant output to repository history and specific changes, recording alternative explanations and limits in the available evidence. Decision. Prepare a timeline for focused comparison under appropriate access controls; similarity, access and provenance each require careful interpretation within the actual record.
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