Editor’s Note:
This article is part of a comprehensive series on Data Governance and AI Data Governance for Law Firms. The series is designed to help legal professionals understand and implement effective governance frameworks. Each article builds on foundational concepts to address specific challenges, from assessing governance needs to managing risks associated with AI tools. The series aims to provide practical, actionable guidance tailored to the legal sector's unique demands. It empowers law firms to safeguard client data, ensure regulatory compliance, and enhance operational efficiency. Stay tuned for upcoming articles as we delve deeper into this critical topic.

A number of law firms–both large and small, have embraced at least some aspect of generative artificial intelligence (GenAI) in their firms.It is no longer a futuristic concept for law firms—it’s here, reshaping how lawyers research, draft, and strategize. But with great power comes even greater responsibility. GenAI tools, while revolutionary, are not foolproof.What happens when your AI system makes a critical error in legal research that affects client advice? Who's liable when an AI-assisted document review misses a crucial clause?

Welcome to the era of AI Data Governance—a field where technology, law, and ethics intersect. For legal professionals, mastering AI data governance isn’t just a matter of compliance; it’s a business imperative that builds trust, safeguards reputations, and paves the way for innovation. In this article, we’ll explore how lawyers can navigate the complexities of AI governance, ensuring their firms stay ahead of the curve while upholding the highest standards of integrity and professionalism.

Data Quality in AI Systems: What Lawyers Need to Know

When selecting AI tools for

legal practice, data quality is paramount. The quality of an AI system’s training data and its handling can significantly impact its reliability and usefulness. Key considerations include:

1. Provider Transparency

• Does the provider clearly disclose their data sources?

• Are their data validation processes documented and available for your review?

• Do they provide documentation about their training standards, data privacy and security measures?

2. Data Recency

• How frequently is the model updated?

• Can it access current legal information?

• Can it cite recent cases and statutes?

• Is the cutoff date for the model's knowledge clearly stated?

3. Domain Expertise

• Was the system developed specifically for legal applications?

• Does the development team include legal professionals?

• Are there collaborations with law firms to refine the system?

• Can they demonstrate an understanding of legal-specific requirements?

4. Track Record and Validation

• Are there case studies or testimonials from other law firms?

• Has the model been independently evaluated?

• What quality assurance measures are in place, and how are errors handled?

Maintaining high data quality standards in legal AI systems isn't merely a technical requirement—it's a professional obligation. Just as lawyers carefully validate their research and verify their sources, firms must approach AI data quality with the same rigor. By systematically evaluating providers, monitoring performance, tracking data lineage, and managing errors, firms can confidently leverage AI while upholding their duty of competent representation. Regular assessment of these quality measures ensures AI systems remain reliable tools that enhance, rather than compromise, the practice of law.

While ensuring data quality forms the foundation of AI governance, controlling access to these systems is equally crucial. Let's examine how Role-Based Access Control (RBAC) provides a structured framework for managing AI tool usage.

Role-Based Access Control (RBAC) for AI Tools

Think of RBAC like security clearances in a government agency-not everyone needs access to top-secret information. Similarly, not every AI tool or model needs access to all firm data. For example, an AI tool used for basic document formatting shouldn't have access to sensitive client financial information. By implementing RBAC, firms can create a hierarchy of access permissions that match their existing human-based security protocols. Just as a junior associate might have different system access than a senior partner, different AI tools should have varying levels of data access based on their function and necessity.

Practical Implementation

1. Hierarchical Permissions: Mirror existing human-based security protocols, granting access based on roles like junior associates, paralegals, or senior partners.

   
   

2. Data Segregation: Use “digital offices” to separate AI environments by practice area or function (e.g., litigation support, contract review). This prevents data cross-contamination, maintains client confidentiality, and makes it easier to audit AI tool usage.

3. Access Monitoring: Maintain sophisticated logging systems to track AI data interactions—including what data was processed, who authorized access, and the purpose. These logs become crucial for both security, function audits, and demonstrating compliance with client confidentiality and other compliance requirements.

4. Emergency Controls: Develop protocols to quickly restrict or terminate access in case of breaches, matter closures, or client relationship terminations. Also, develop routine maintenance procedures (when a matter closes, the client relationship ends, or the attorney leaves the firm).

   
   

Each of these elements works together to create a comprehensive AI data access control system. The goal is to harness AI's benefits while maintaining the same level of data security and confidentiality that clients expect from traditional legal services. This requires thinking about AI tools not as standalone applications but as members of your legal team who need appropriate supervision and access controls.

With access controls established, firms must then address the broader challenge of protecting sensitive information across all AI interactions. Security and privacy considerations extend far beyond traditional data protection measures.

Security and Privacy: Protecting Client Data in AI Systems

The security and privacy challenges of AI systems differ significantly from traditional data protection. Think of it like the difference between protecting a physical document and protecting a conversation. GenAI interactions are dynamic, often leaving traces in unexpected places.

Encryption and Data Protection Unlike traditional systems where data simply sits in storage, AI systems actively process and analyze information. This creates new security requirements at three critical points: data in transit (moving to/from the AI system), data in use (being processed by AI), and data at rest (stored in AI systems). Each point requires specific protection strategies. For example, while standard encryption works for data in transit and at rest, special techniques like homomorphic encryption might be needed for data being actively processed by GenAI.

Client Confidentiality in AI Interactions Law firms must treat AI systems like very efficient but potentially indiscreet team members. Just as you wouldn't discuss sensitive client matters in a public space, you need to carefully control what information is shared with AI tools. Each AI interaction should be treated as a potential disclosure, requiring the same level of scrutiny you'd apply to sharing information with a new associate. This means implementing strict protocols for what types of information can be input into AI systems and under what circumstances.

Third-Party AI Tool Management When using third-party AI tools, firms face a unique challenge: maintaining security over data that's being processed outside their direct control. It's similar to working with outside counsel but with more complex technological considerations. Firms must:

• Carefully vet AI providers' security credentials

• Review and understand providers' data retention policies

• Ensure providers' security measures meet or exceed firm standards

• Maintain clear data processing agreements

• Regular audit third-party AI tool usage and security

However, even the most robust security systems are only as strong as their users' understanding and compliance. This brings us to the critical role of comprehensive training.

Training and Security Awareness The strongest security measures can be undermined by simple human error. Comprehensive training programs help attorneys and staff understand and use GenAI effectively. This starts with a three-tiered training program that addresses both AI security and effective AI use:

1. Foundational Training

   Every member of the firm, regardless of role, needs this essential baseline knowledge. Think of it as your firm's "AI driver's license"–no one touches an AI tool without first mastering these basics. This foundation ensures everyone speaks the same language when it comes to AI security and understands their role in protecting client data.

   
   

   • Basic AI concepts and capabilities

   • Firm-approved AI tools and their proper use cases

   • Data sensitivity classification

   • Security protocols for different data types

   • Common AI errors and how to spot them

     
     

2. Role-Specific Training

   Building on the foundational training, this tier recognizes that different roles interact with AI in distinct ways. Just as a trial attorney and a tax attorney require different specialized knowledge, each role needs customized AI training that addresses their specific responsibilities and challenges.

   
   

   Your training must be customized to the roles and responsibilities you assign to different team members. Here is a good starting point that you can modify as needed:

   
   

   • Attorneys: Approved AI tools and use cases, legal research validation, output verification, ethical considerations

   • Paralegals: Approved AI tools and use cases, legal research and validation, output verification, ethical considerations, document processing protocols, data handling procedures

   • IT Staff: Security monitoring, incident response, system maintenance

   • Administrative Staff: Basic security practices, approved AI tool usage, output verification

     
     

3. Ongoing Security Practices

   The rapidly evolving nature of AI technology demands continuous learning. This tier keeps the firm current and competent, functioning like mandatory CLE for AI security and cybersecurity in general. Regular engagement with these practices ensures the firm stays ahead of emerging threats and opportunities. The program should include practical exercises, clear documentation, and regular testing to ensure comprehension and compliance.

   • Monthly security updates and refreshers

   • Real-world case studies of AI incidents

   • Hands-on workshops with approved AI tools

   • Regular assessments and certification requirements

   • Updates on new AI capabilities and risks

When properly implemented, this three-tiered program creates a self-reinforcing culture where security becomes instinctive rather than burdensome. Staff at all levels should view AI security practices as fundamental to client service and professional responsibility, just as they do attorney-client privilege or ethical and professional obligations. Regular assessment of training effectiveness, combined with swift updates to address emerging threats or evolutions of AI models, ensures your firm maintains both technological competence and client trust in an AI-enabled legal landscape.

As firms implement these security measures and training programs, documenting AI usage becomes essential for both accountability and risk management.

Compliance and Documentation: A Practical Approach

Documentation of AI use in law firms should be meaningful rather than exhaustive. Think of it like documenting research or client communications—you don't record every internal conversation or preliminary research query, but you do maintain records of significant work that impacts client matters. The goal is to create an audit trail that demonstrates responsible AI use and protects both the firm and its clients without creating unnecessary administrative burdens.

Best Practices for Documentation

• Significant AI Usage: Record instances where AI contributes materially to legal work, similar to citing sources in legal writing.

• AI Decision Documentation: Maintain records of human oversight, including who reviewed outputs and how decisions were verified.

• Regulatory Compliance Records: Ensure documentation demonstrates adherence to laws like GDPR, HIPAA, and client confidentiality requirements.

• Quality Control Logs: Track significant errors, patterns, and resolution processes to inform training and policy updates.

While the previous sections outline core governance requirements, law firms must also navigate challenges specific to AI technology that traditional frameworks may not fully address.

Unique Challenges: AI-Specific Data Governance Issues

Law firms implementing AI face several unique challenges that traditional data governance frameworks don't fully address. Understanding and proactively managing these challenges is crucial for successful AI adoption.

• Data Persistence and Control Unlike traditional software that simply stores and retrieves data, AI systems interact with data in more complex ways. Think ofit like the difference between putting a document in a filing cabinet versus sharing it with a colleague who might remember its contents. When you input information into an AI system, it's not always clear how that information might be retained or used. For example, if you use an AI tool to analyze a confidential settlement agreement, how can you be certain that information isn't inadvertently preserved in the system's memory? Firms must implement strict protocols about what data can be shared with AI systems and ensure their AI providers have clear data retention and deletion policies.

• AI Hallucinations and Accuracy AI systems can sometimes generate convincing but incorrect information, a phenomenon known as hallucination. We’ve all heard of an AI system confidently citing a non-existent case or misstating a legal principle. Firms need robust verification processes for AI outputs and clear documentation of how AI-generated information is validated. This often means treating AI like a very intelligent first-year associate–helpful and ambitious but requiring careful supervision and fact-checking.

• The Black Box Problem Many AI systems operate like a black box-data goes in, results come out, but the decision-making process remains opaque. This poses particular challenges for law firms, who may need to explain their methodologies to courts, clients, or regulators. For instance, if an AI system helps identify relevant documents in discovery, you need to be able to explain and defend that selection process. Firms must develop processes for documenting AI decision-making and ensuring sufficient transparency for ethical and regulatory compliance.

• Integration with Legacy Systems All law firms should have existing document management systems and established data governance protocols. Integrating AI tools with these legacy systems can be like trying to fit a modern electrical system into a historic building; it requires careful planning and sometimes significant adaptation. Ensure that AI integration doesn't create security gaps or inconsistencies in data handling. This often means updating existing protocols and potentially upgrading legacy systems to maintain consistent governance standards.

Each of these challenges requires specific strategies and solutions, but they all share a common theme: the need for thoughtful, strategic governance that protects client interests while enabling the benefits of AI technology.

Embracing AI Governance: The Path Forward

The integration of AI into legal practice is inevitable and transformative. Success requires strategic governance that balances innovation with responsibility and automation with professional judgment.

By implementing frameworks for data quality, access control, security, training, and compliance, law firms can master the governance puzzle while maintaining client trust. Forward-thinking firms will view AI governance as a competitive advantage—a way to deliver superior legal services in an AI-enabled profession.

The future belongs to those who master not just the technology but the governance frameworks that make its benefits possible.

© 2025 Amy Swaner. All Rights Reserved. May use with attribution and link to article.