AI Policy and Governance

Developing effective policies governing the use of AI is essential for law firms to manage ethical, legal, and security risks associated with AI technologies. This section provides Texas attorneys with practical guidance, key considerations, and actionable steps for developing and implementing effective AI policy and governance within their firms.

Establishing Internal AI Usage Policies

A well-defined internal AI usage policy is the cornerstone of responsible AI adoption in a law firm. It provides clear guidelines for attorneys and staff, ensuring consistency, compliance, and risk mitigation. Policies should be comprehensive, regularly updated, and effectively communicated and enforced.

Key Elements of an Internal AI Usage Policy:

Purpose and Scope

  • Clearly articulate the firm's stance on AI adoption - e.g., embracing AI to enhance efficiency while strictly upholding ethical and professional obligations.
  • Define which AI tools are covered (e.g., what AI, predictive coding tools, AI-powered research platforms) and which personnel the policy applies to (all attorneys, paralegals, staff).

Permissible Uses and Restrictions

  • Specify approved use cases for AI (e.g., initial document review, drafting routine correspondence/pleadings  for attorney review, summarizing documents, preliminary legal research, e-discovery assistance, and so on).
  • Explicitly prohibit or restrict uses that pose high risks, such as:
    • Entering confidential or sensitive client information into unapproved or public-facing AI tools (e.g., ChatGPT, unless using a verified, secure, enterprise-level version with contractual data protection guarantees and explicit client consent).
    • Relying solely on AI output without independent verification, especially for legal analysis, factual assertions, or citations.
    • Using AI for tasks that constitute the unauthorized practice of law.

Competence and Training

  • Require attorneys and staff to obtain sufficient training to understand how specific AI tools work, as well as their capabilities, limitations, and risks (e.g., "hallucinations," bias, knowledge cut-offs).
  • Mandate ongoing education to stay abreast of evolving AI technology and associated ethical/regulatory guidance.
  • Designate responsible individuals or committees for overseeing training programs.

Supervision and Verification

  • Establish a mandatory process requiring independent review and verification by a licensed attorney for all AI-generated content used in client matters or submitted to courts.
  • Outline specific verification steps, such as checking legal citations against reliable sources, confirming factual accuracy, reviewing analysis for logical soundness, and ensuring compliance with relevant rules and standards.
  • Emphasize that AI is a tool to augment human expertise, not replace professional judgment.

Data Handling and Confidentiality

  • Include strict protocols for handling client data when using AI tools.
  • Prohibit the input of Protected Health Information (PHI), personally identifiable information (PII), or other confidential data into AI tools unless the vendor provides contractually guaranteed, verifiable data security, privacy, and confidentiality measures compliant with HIPAA, TDPSA, and other applicable laws.
  • Specify that prompts and data entered into AI tools must not inadvertently disclose client identity or confidential information unless appropriate safeguards and consents are in place.

Vendor Assessment and Approval 

  • Criteria should include:
    • Data security measures (e.g., encryption, access controls, compliance certifications like SOC 2 Type II, ISO 27001, ISO 42001).
    • Data usage policies (e.g., ensuring the vendor does not use firm or client data to train its models unless explicitly agreed upon with necessary consents).
    • Compliance with relevant data privacy laws (TDPSA, HIPAA, etc.).
    • Auditability and transparency of the AI system's processes.
    • Reliability and performance history.
    • A clear data retention policy.
    • Terms in the agreement supersede any potential conflicting terms in the public end-user license agreement.

Ethical Compliance

  • Require consideration of the ethical implications of AI bias and discrimination.

Enforcement and Monitoring

  • Define consequences for policy violations.
  • Establish methods for monitoring compliance with the policy, potentially through regular audits or designated oversight roles.

Documentation

  • Require documentation of key AI-related decisions, such as vendor assessments, client consents, and verification steps taken for AI-generated work product.

Guidelines for Policy Establishment

Form a working group. Include attorneys from different practice areas, IT personnel, and compliance staff.

Review existing policies. Ensure AI policies integrate seamlessly with existing data security, privacy, and IT policies.

Draft the policy. Keep language clear, concise, and actionable.

Obtain leadership buy-in. Firm leadership must champion the policy.

Train all personnel. Comprehensive training is critical for effective implementation.

Implement monitoring. Regularly check for compliance.

Review and update. Schedule periodic reviews (at least annually, or more frequently as AI evolves) to update the policy based on new technology, ethical guidance, and regulatory changes.

Key Resources