The Law Firm AI Governance Framework: 10 Controls Every Firm Needs

Control 1 — Acceptable Use Policy

Control 1 — Acceptable Use Policy: A written policy defining which AI tools are approved for use, for what purposes, and under what conditions. Unapproved tools should be explicitly prohibited for client work, and the policy should specify the approval process for adding new tools.

Vendor due diligence standard

Control 2 — Vendor Due Diligence Standard: A defined process for evaluating AI vendors before approval, including security assessment (SOC 2, ISO 27001 certification), data handling review (how client data is processed, stored, and used for model training), and professional responsibility analysis.

Confidentiality protocol for data

Control 3 — Confidentiality Protocol: A data classification framework that defines what categories of client data may be processed by each approved AI tool, with explicit restrictions on processing of privileged communications by cloud-based AI systems without appropriate data protection agreements.

Attorney supervision standard

Control 4 — Attorney Supervision Standard: A defined protocol for attorney review of AI-generated content before it is transmitted to clients or filed with courts. The protocol should specify minimum review requirements, not leave supervision entirely to individual attorney judgment.

Output validation process

Control 5 — Output Validation Process: A QA workflow for AI outputs that includes error classification, sampling strategies, and escalation procedures for identified errors. The ABA's supervision rules require that errors be caught and corrected; the validation process is the engineering mechanism for satisfying that requirement.

Mandatory AI training program

Control 6 — Training Program: Mandatory training for all attorneys and staff using AI tools, covering both technical competence (how to use the tools effectively) and professional responsibility (obligations that govern AI use). The 8am Legal Industry Report identifies training gaps as a leading cause of AI adoption failure.

Incident response plan process

Control 7 — Incident Response Plan: A defined process for responding to AI errors that reach clients, including client notification protocols, root cause analysis, and remediation procedures.

Performance monitoring for systems

Control 8 — Performance Monitoring: Regular review of AI system performance against defined metrics, with a defined process for suspending systems that fall below acceptable performance thresholds.

Governance committee responsibilities

Control 9 — Governance Committee: A designated body responsible for AI policy, vendor approval, incident review, and policy updates, with defined membership, meeting cadence, and decision authority.

Client communication policy guidance

Control 10 — Client Communication Policy: Guidance on when and how to disclose AI use to clients, consistent with applicable state bar guidance and client preferences.