Can a confidential conversation with an Artificial Intelligence platform be subpoenaed and used to convict you in a court of law? A recent landmark ruling in the United States says yes.

Bradley Heppner, the former CEO of financial firm GWG Holdings, faced multiple federal charges, including conspiracy to commit securities fraud, wire fraud, making false statements to auditors, and falsifying corporate records. Following his indictment, Heppner input extensive, sensitive details of his case into Anthropic's AI assistant, Claude, generating a 31-page document detailing case analyses and prospective defense strategies.

Subsequent to a search warrant executed by the FBI, federal agents seized the AI chat logs directly from Heppner’s personal devices. US prosecutors moved to introduce these records into evidence to verify whether Heppner had concealed assets or information during the investigation.

On February 17, 2026, the U.S. District Court for the Southern District of New York (S.D.N.Y.) issued an official memorandum ruling that these 31 AI chat logs are not protected by attorney-client privilege or the work-product doctrine. Consequently, the prosecution was granted lawful access to introduce them as trial evidence.

I. Judicial Rationale: Why AI Communications Lack Privilege

Heppner’s defense counsel argued that the strategic consultations with Claude constituted privileged legal preparation and should be immune from government scrutiny. The court decisively rejected this argument, ruling that user inputs and AI outputs operate under the same evidentiary standards as a standard search engine log. The court outlined three primary justifications:

1. Ineligibility of the Entity: Claude is an algorithmic model, not a licensed attorney. Attorney-client privilege is legally predicated on a trusted, qualified relationship between human professionals. Claude's terms of service explicitly disclaim providing formal legal counsel, invalidating any claim of a legally recognized retainer or agency relationship.
2. Absence of a Reasonable Expectation of Confidentiality: Anthropic’s privacy policy expressly reserves the right to collect user inputs for model training and to disclose data to third parties, including government regulatory and law enforcement bodies. By agreeing to these terms, the user forfeits any "reasonable expectation of privacy" under the law.
3. Nature of Use: Because the platform explicitly states it does not provide professional legal opinions, Heppner’s interactions were classified as independent pro se research utilizing a digital utility, rather than seeking counsel from a credentialed professional.

（source： U.S. Air Force）

II. Global Regulatory Landscape: Privacy Terms of 10 Major AI Platforms

Under standard cross-border legal frameworks, electronic data is a globally recognized category of statutory evidence. Unlike common-law jurisdictions, many civil-law systems lack a broad application of "attorney-client privilege" exemptions. If a party inputs admissions of guilt, structural corporate vulnerabilities, or operational execution steps into an AI, these logs can be legally collected as electronic evidence and directly leveraged in sentencing.

1. Model Training & Data Opt-Out Policies

• International Platforms: Across standard consumer tiers (excluding premium enterprise or dedicated API accounts), user inputs are activated for model optimization by default, requiring proactive manual intervention from the user to opt out.
• Domestic Platforms: Leading providers reserve the structural right to utilize user queries for algorithmic alignment, creating heightened data-discoverability risks during litigation.

（source：gov.uscourts.nysd）

2. Mandatory Disclosures Under Criminal Investigations

A comprehensive analysis of the privacy agreements across all ten major international and domestic platforms confirms a uniform compliance standard: Every platform reserves the right to disclose user data to law enforcement, national security, or regulatory agencies without user consent when executing a valid legal order.

• OpenAI (ChatGPT): Discloses records to comply with subpoenas, search warrants, or court orders, and to investigate potential terms-of-service violations or fraudulent activity. Subject to global regulatory scrutiny, including a May 2026 Office of the Privacy Commissioner of Canada (OPC) joint report finding data practices non-compliant prior to recent platform updates.
• Anthropic (Claude): Explicitly reserves the right to disclose records to regulatory authorities. This provision served as a foundational basis for the Heppner ruling. Furthermore, its designation under strategic supply chain frameworks exposes it to rigorous data disclosure oversight.
• Google (Gemini) & Microsoft (Copilot): Both platforms enforce strict compliance procedures requiring disclosure under valid legal processes across consumer and standard enterprise endpoints. Microsoft publishes annual transparency reports documenting government data access volume.
• Domestic LLMs (DeepSeek, Doubao, Yuanbao, Tongyi Qianwen, KIMI, ERNIE Bot): All operate under explicit statutory exemptions regarding user consent. Under local data security frameworks, platforms are legally mandated to cooperate without user authorization during criminal inquiries, national security threats, public interest exemptions, or asset freezing mandates (e.g., assisting in unfreezing over RMB 4 million in illicitly flagged deposits).

III. Strategic Takeaways for Enterprise Users & Legal Practitioners

1. Enforce Strict Data Anonymization: Never input personally identifiable information (PII), banking credentials, sensitive trade secrets, or unmasked case details into public AI environments. Manually adjust platform configurations to opt out of data-retention and training programs.
2. Deploy Enterprise-Grade, Zero-Retention Architectures: For corporate environments handling protected data, bypass consumer models entirely. Utilize enterprise instances or API endpoints that provide contractually guaranteed "Zero Data Retention" (ZDR) and explicitly exclude user inputs from model optimization pools.
3. Recognize the Risk of Algorithmic Subpoenas: Understand that when case data is processed on an external server, it generates an enduring digital footprint. Under global regulatory compliance exemptions, regulatory and judicial bodies possess the authority to compel platforms to hand over these server-side logs during an active investigation.
4. Mandate Professional Human Oversight: AI outputs must never be treated as definitive legal or professional authority. As shown in recent California appellate sanctions where an attorney was fined $10,000 for submitting 21 AI-fabricated precedents, all generative material must undergo rigorous verification by qualified counsel prior to formal submission.
5. Strict Professional Guardrails for Attorneys: Processing client materials through public LLMs can constitute a direct breach of an attorney's professional duty of confidentiality. Counsel must formally advise clients against inputting case details into public models to protect case strategy and isolate liability exposure.

Conclusion

The ruling in the Heppner case does not reshape fundamental evidentiary laws; rather, it applies long-standing doctrines of privilege and privacy to the frontiers of generative technology. As enterprises integrate AI into their operational workflows, maintaining an accurate equilibrium between technological agility and regulatory compliance is paramount. The realization that AI chats can serve as evidence in a prosecution underscores a clear directive: proactive digital risk management remains an indispensable asset.

Disclaimer & Copyright: This article is co-authored by Mandy Wu and Yu Yuting. The insights shared are for general compliance trends only and do not constitute formal legal advice.As a specialized cross-border legal institution, Neo-Ark Law Firm provides comprehensive global compliance and rights-protection support for expanding enterprises. For more international legal updates, please visit the Neo-Ark Law Firm Official Websites (https://www.neoarklawyers.com/news).

China's first effective judicial ruling on reputation infringement caused by Artificial Intelligence (AI) has triggered intense discussion across global tech and legal sectors. Nanjing attorney Li Xiaoliang successfully sued Baidu after its generative AI platform fabricated a response stating he had been "sentenced to three years in prison," complete with an incorrectly matched photograph.

Baidu’s defense—arguing that "AI hallucinations are inherently uncertain, technologically neutral, and part of an evolving developmental phase"—was rejected by both trial and appellate courts. The judiciary ruled that generative AI outputs are subject to defamation and tort laws, ordering Baidu to issue a formal written apology. As of May 2026, Baidu has failed to comply, prompting the plaintiff to file for compulsory enforcement.

This landmark case establishes a critical judicial precedent for the boundaries of platform liability regarding algorithmic errors.

（source：Baidu）

I. Decoding "AI Hallucination": The Root Cause

AI hallucination refers to instances where Large Language Models (LLMs) generate content that appears fluent and factually persuasive but is entirely false or unsupported by training data.

Rather than a simple software bug, hallucination is a byproduct of the probabilistic token-prediction mechanism inherent to LLMs. Instead of retrieving verified facts like a traditional search engine, an LLM predicts the next most statistically probable word.

Primary Types of AI Hallucinations:

• Fact Distortion: Fabricating non-existent events, financial statistics, or legal metrics.
• Source Fabrication: Inventing fictional citations, academic papers, or legal precedents.
• Logical Disconnects: Outputting flawed arguments through a superficially coherent reasoning chain.
• Identity Commingling (Misattribution): Conflating individuals with similar names or backgrounds. (The Li Xiaoliang case fell into this category, as the AI misattributed a non-existent criminal profile to a licensed attorney).

II. Framework of Liability: The Legal Reality of Algorithmic Errors

Under current PRC civil and regulatory frameworks, AI hallucinations do not operate in a legal vacuum.

1. Civil Tort Liability (PRC Civil Code, Article 1024)

The right to reputation is protected irrespective of whether the infringing content was authored by a human or generated by an algorithm. When an LLM outputs defamatory, false information that points to an identifiable individual or entity, it satisfies the criteria for systemic reputation infringement.

2. Administrative Regulatory Duties

Pursuant to the Interim Measures for the Management of Generative Artificial Intelligence Services, service providers must deploy robust measures to improve data quality, ensuring the truthfulness, accuracy, and objectivity of training sets. Platforms must act swiftly upon receiving infringement notices to mitigate liability.

3. Fault-Based Liability Principle

Judicial consensus dictates that generative AI providers are subject to a fault-based liability standard. Fault is determined by evaluating whether the platform had the technical capability and commercial opportunity to prevent the harm but failed to meet industry-standard duty of care.

Judicial Benchmark: In the Li case, the court noted that peer LLMs (such as Doubao and DeepSeek) did not generate the same defamatory error when queried, proving that the defendant failed to meet the baseline compliance standards of the industry.

III. International Precedents: The Proliferation of Fictional Citations

The liabilities surrounding AI hallucinations are escalating globally, particularly within high-stakes professional fields:

• China (Domestic Commercial Dispute): In 2025, the Tongzhou District People’s Court of Beijing discovered that litigation documents submitted by a plaintiff's counsel contained entirely fictional judicial precedents generated by an LLM (e.g., matching real case serial numbers to completely fabricated facts). The court rejected the briefs and formally sanctioned the attorney.
• United States (Sanctions for AI Citations): In California, attorney Amir Mostafavi was fined $10,000 by the Court of Appeal after using ChatGPT, Claude, Gemini, and Grok to draft and cross-verify an appellate brief. Despite using multiple models, the final text contained 21 completely fabricated case citations. The court published a formal disciplinary opinion warning the bar against unverified algorithmic reliance.

IV. Strategic Compliance Advice: Rights Protection & Platform Risk Management

For Users & Enterprise Victims of AI Infringement:

1. Immediate Evidence Preservation: Secure timestamped screen recordings, source codes, and interface captures. Continuous logging over an extended duration is essential to prove the persistence of the infringement.
2. Formal Cease-and-Desist Notifications: Transmit formal legal letters via official platform channels. A platform’s failure to implement swift takedown or filtration mechanisms upon notice increases its punitive risk exposure.
3. Targeted Litigation Claims: Litigants may petition for immediate cessation of generation, public retractions, and compensatory damages. Note: Claims for economic compensation must be backed by quantifiable evidence of financial loss or clinical psychological duress.

For AI Platform Developers & Operators:

1. Data Provenance & Source Auditing: Implement rigorous compliance gates for training datasets, checking for data integrity and intellectual property alignment.
2. Real-Time Safety & Alignment Filtering: Deploy advanced reinforcement learning (RLHF) and dynamic retrieval-augmented generation (RAG) to cross-check outputs against verified external knowledge bases.
3. Responsive Takedown Protocols: Build low-friction, rapid-response reporting mechanisms to isolate, modify, or delete infringing parametric weights and generated data strings upon user complaint.

（source：JD Supra）

Conclusion

As generative technologies scale globally, the judiciary is drawing definitive compliance boundaries: commercial platforms cannot harvest the financial benefits of AI traffic while outsourcing the legal liabilities to technology's inherent flaws.

Large language models may experience hallucinations, but legal liability remains an absolute reality.

Disclaimer & Copyright: This article is co-authored by Mandy Wu and Yu Yuting. The insights shared are for general compliance trends only and do not constitute formal legal advice.As a specialized cross-border legal institution, Neo-Ark Law Firm provides comprehensive global compliance and rights-protection support for expanding enterprises. For more international legal updates, please visit the Neo-Ark Law Firm Official Websites (https://www.neoarklawyers.com/news).