The rapid integration of generative artificial intelligence into pre-litigation research and case strategy has created a dangerous illusion that queries—including documents uploaded into an AI model—are protected in the same way as a confidential consultation with a licensed attorney.
In reality, uploading factual claims, litigation theories, or client secrets into commercial AI tools can create disclosure risks and may undermine important evidentiary protections, depending on the tool, its settings, the circumstances of use, and the applicable law.
This article examines why information uploaded to AI platforms may fail to meet the legal standards associated with privilege and outlines practical protocols for safeguarding sensitive legal work product.
Understanding the privilege problem
Evidentiary privileges, specifically the attorney-client privilege and the work product doctrine, are strictly construed exceptions to the general rule that relevant evidence may be subject to disclosure. Their application depends on the facts and the legal framework governing the particular matter.
A key question is whether an interaction with an AI system preserves the confidentiality and purpose required for the relevant protection. A commercial AI platform is not itself an attorney, and the use of a third-party technology provider can introduce additional questions about access, retention, processing, and disclosure.
When a party inputs case facts or legal theories into a commercial generative AI model, the interaction may fail to satisfy the elements required to establish privilege.
The core question
- Is the communication between a client and an attorney?
- Was it intended to be, and actually kept, confidential?
- Was it made primarily to obtain or provide legal advice?
Why commercial AI tools require careful handling
AI systems can involve multiple technical layers, including application providers, model providers, cloud infrastructure, logging systems, administrators, and security tools. The precise handling of prompts and uploaded files therefore matters.
Legal teams should not assume that a general-purpose AI service automatically provides the same confidentiality protections as a professional legal relationship. Before entering sensitive material, users should understand the provider's terms, data controls, retention practices, account configuration, and organisational policies.
A practical case example
In the case of United States v. Heppner, Bradley Heppner, CEO of Beneficient, was indicted for securities fraud, wire fraud, and related charges involving an alleged scheme that misappropriated more than $300 million.
Prior to his arrest, Heppner independently ran approximately 31 prompts through Anthropic's public AI model, Claude. His prompts incorporated details of the government's investigation, including strategic legal insights he had learned from counsel.
He generated the documents to analyse his exposure and discuss the outputs with his defence team. Defence counsel did not direct or supervise the searches. Federal agents later seized electronic devices containing the AI-generated documents during execution of a search warrant.
Defence counsel asserted privilege over the files, leading to a dispute about whether privilege attached to the AI-generated material. The example illustrates why legal professionals should avoid assuming that an AI interaction is automatically protected merely because the underlying subject matter is legal.
Practical safeguards for legal professionals
Classify information first
Identify confidential, privileged, personal, regulated, and commercially sensitive information before using an AI system.
Use approved tools
Organisations should maintain an approved AI-tool list and define which systems may be used for sensitive work.
Minimise sensitive inputs
Use anonymisation, redaction, synthetic examples, or summaries where the original confidential material is not necessary.
Review provider controls
Check retention, training use, access controls, deletion options, security commitments, and contractual terms.
Keep human oversight
AI outputs should support professional judgment rather than replace legal review, especially where confidentiality or rights are involved.
Responsible AI starts before the prompt
The safest AI workflow is not simply about choosing a powerful model. It begins with information governance, clear policies, appropriate technical controls, and informed professional judgment.
Professional note: This article is provided for educational and informational purposes. It is not legal advice and should not be relied upon as a substitute for advice from qualified counsel in the relevant jurisdiction.