How to Assess Your Legal AI Governance
Four questions legal organizations can ask to assess the maturity of their AI governance programs.
As legal AI adoption accelerates, governance is becoming less about drafting guidelines and more about operationalizing them. Clients are asking how AI is used on their matters. Firm leadership wants evidence of adoption and impact. Security and compliance teams need confidence that access controls and data protections are working as intended.
The effectiveness of an AI governance framework ultimately depends on its ability to provide visibility, enforce controls, and support oversight as AI use scales across the organization.
A useful way to assess your legal AI governance posture is to evaluate four core areas: permissions, collaboration, auditability, and adoption visibility.
1. Can You Enforce Least-Privilege Access?
Governance begins with controlling who can access AI tools, workspaces, and data. As teams, matters, and client relationships evolve, firms should be able to adjust permissions quickly without creating administrative bottlenecks or relying on manual processes for every change.
Firms assessing their governance posture should examine how permissions are managed across users, workspaces, and resources like documents and files. Access should align with clearly defined roles, administrators should have visibility into who can access what, and permissions should be easy to adjust as teams, matters, and client relationships evolve. The underlying objective is to ensure individuals have access only to the resources required for their work while maintaining the flexibility needed to support legal operations.
Harvey extends this principle to external data access through its Connector Library. Administrators can determine which connectors are enabled, who can use them, and under what conditions, while Harvey manages authentication, credential handling, and access controls — giving firms the flexibility to adopt new capabilities while maintaining administrative oversight.
Signs of maturity:
- Administrators can quickly adjust access as teams and matters change
- Role-based permissions are applied consistently across the organization
- Access rights are regularly reviewed and easy to audit
- Permissions can be revoked without operational disruption
2. Is External Collaboration Governed?
Legal work increasingly involves collaboration across organizations, including clients, co-counsel, and experts.
Without clear controls, collaboration can become difficult to manage and even harder to revoke. Firms assessing their governance posture should evaluate whether external access occurs through approved, governed workflows rather than informal sharing practices. Collaboration environments should be clearly defined, approval processes should be consistent, and data boundaries should remain intact throughout the lifecycle of an engagement. Just as importantly, firms should be able to revoke access quickly when relationships or project requirements change.
Harvey Shared Spaces enables firms to collaborate securely across organizational boundaries with granular permissions, admin approvals, and audit trails. Spaces Analytics complements these controls with visibility into workspace engagement and usage, helping administrators support collaboration while maintaining governance and oversight.
Signs of maturity:
- External access is granted through defined approval workflows
- Collaboration occurs within approved environments
- Data-sharing boundaries are clearly enforced
- Access can be revoked when business relationships change
A Practical Guide to Legal AI Governance for Law Firms
Explore the policies, controls, and capabilities behind effective legal AI governance.
3. Can You Demonstrate Oversight in Practice?
When clients, leadership, or regulators ask how AI is being used, teams should be able to provide answers backed by data rather than anecdotal reports.
Visibility into platform activity is foundational to effective oversight. Firms need to understand who is using AI tools, how frequently they are being used, where activity is concentrated, and whether usage aligns with organizational policies. As governance programs mature, this visibility often expands into detailed logging, reporting capabilities, and integrations with existing security and compliance systems.
Harvey’s Command Center gives Innovation, Legal Ops, and firm leaders a centralized view of AI activity across the organization. With visibility into platform usage and governance insights, administrators can answer questions about how AI is being used with confidence.
“Command Center gives administrators visibility into how Harvey is being used across the firm — adoption trends, high-value use cases, underutilized groups, and where training is needed — along with clearer insight into whether the platform is being used consistently with firm policies. Overall, it moves Foley from anecdotal assessments of AI usage to data-driven management of deployment, training, governance, and value creation.”
Sean Monahan
Director of Practice Innovation at Foley Lardner
Signs of maturity:
- Leadership can access reliable information about platform usage
- Activity data can be exported and reviewed when needed
- Usage trends are monitored over time
- Governance discussions are informed by evidence rather than assumptions
4. Do You Understand Adoption and Business Impact?
Governance is not only about managing risk. It is also about enabling adoption.
Firms that understand how AI is being used can make better decisions about training, investment, and future deployment. Assessing governance maturity therefore requires examining whether the organization has visibility into adoption patterns across teams, practice groups, and use cases. Understanding which capabilities are gaining traction, where adoption is lagging, and who is driving successful usage can help firms target enablement efforts and make more informed decisions about future investments.
With usage analytics, benchmarking, and agentic insights, organizations can use Command Center in Harvey to identify adoption trends, measure impact, and make more informed decisions about where to invest and scale AI across the business.
Signs of maturity:
- Adoption trends can be measured across teams and practice groups
- Usage data informs training and enablement strategies
- Leadership has visibility into adoption progress
- AI investments can be tied to measurable outcomes and business goals
Governance is About More Than Controls
Technology controls are only one part of an effective legal AI governance program.
Governance committees, training programs, model evaluation processes, and client engagement policies remain essential. Operational controls make these efforts more effective by providing the evidence, consistency, and visibility needed to support them as adoption grows.
The strongest governance programs combine policy, process, and platform-level controls into a single framework.
Looking for a deeper dive into legal AI governance? Read our guide, The Legal AI Governance Imperative in Practice, for practical considerations on building visibility, control, and oversight into your AI program.
