AI, Privilege, and Confidential Business Information: Implications from the Heppner Case
Earlier this month, a landmark ruling was issued by Judge Rakoff of the Southern District of New York in the case of United States v. Heppner. This case raised critical questions regarding the use of generative AI platforms in legal contexts, particularly focusing on the implications for life sciences teams who often handle sensitive data.
The Heppner Case Overview
The defendant, Heppner, utilized a public generative AI platform, Claude, to prepare reports outlining his defense strategy. Although he independently prepared these documents, he later shared them with his attorneys, arguing that they should be protected by attorney-client privilege and the work product doctrine.
Key Takeaways from the Ruling
The court’s decision highlighted several important points:
- Privilege is Narrow: The judge emphasized that privilege only covers direct, confidential communications between a client and their attorney, or work product prepared under their direction. Using a public AI tool independently breaks this chain.
- No Reasonable Expectation of Confidentiality: Engaging a third-party AI platform means one cannot expect confidentiality. The platform’s privacy policy may allow it to store or disclose user inputs, including confidential business information (CBI).
- No Retroactive Privilege: Sharing AI-generated outputs with an attorney does not retroactively confer privilege. Once sensitive information is input into a public AI tool, that information may be considered disclosed.
Implications for Life Sciences Companies
Life sciences firms frequently manage sensitive data such as:
- Regulatory submissions
- Audit responses
- Clinical trial results
- Manufacturing records
As AI tools become integrated into workflows, there is a growing temptation to use them for summarizing or drafting documents containing CBI. However, the risks associated with this practice are significant:
- Regulatory and Litigation Risks: Disclosing CBI through public AI tools can lead to loss of protection during litigation and regulatory audits.
- Trade Secret Protection: Public disclosure can jeopardize the status of trade secrets.
- Internal Risks: Employees in various departments may be unaware of these risks, necessitating comprehensive training and policy updates.
Practical Steps for Protection
To safeguard trade secrets and sensitive information, companies should consider the following:
- Avoid Public AI Tools: Do not use public or commercial AI platforms for processing sensitive information.
- Training for All Teams: Ensure all employees understand the risks associated with AI tool use and the importance of proper CBI handling.
- Update Internal Policies: Prohibit the use of unapproved AI tools for sensitive information and ensure compliant usage within controlled environments.
- Incident Response Plans: Update incident response strategies to address situations where sensitive information may be inadvertently shared with public AI tools.
- Document AI Governance: Maintain records demonstrating how your organization protects CBI within AI-enabled workflows.
- Vendor Due Diligence: When using third-party AI vendors, review their privacy policies and data handling practices to ensure control over your data.
- Which AI tools are approved for use, and who makes these decisions?
- What types of data can employees input into AI tools?
- Who reviews and approves AI-generated outputs?
- What training is necessary for staff in using AI and protecting data?
- How is compliance monitored and enforced?
- What is the escalation process for AI-related incidents or errors?
Checklist for Internal AI Policy Updates
Consider the following questions when updating internal AI policies:
Conducting a data inventory can help identify where CBI is stored and which workflows might be tempted to use AI tools, targeting training where it is most needed.
Looking Ahead
As agencies like the FDA increasingly adopt AI in their review processes, the risks associated with AI use and the necessity for robust internal policies will grow. The bottom line is clear: while AI tools are transforming workflows, they do not alter the fundamentals of privilege and confidentiality. Inputting CBI into public AI tools can result in significant legal and regulatory repercussions.
