Generative AI is reshaping how enterprises manage investigations, litigation, and records, with data as the essential fuel for this transformation. Yet, the excitement of AI’s potential needs to be balanced with its inherent risks. The need to prepare and govern data before applying AI tools has never been more critical, from preventing data loss and minimizing biases to protecting privacy and maintaining regulatory compliance.

This isn't just about preparing data for training models—it's about setting up robust practices for managing data as business as usual so that organizations protect their data for any potential AI application. To harness AI while avoiding pitfalls, enterprises must make data readiness the cornerstone of their approach. Here's how effective information governance protects against AI harm, strengthens compliance, and minimizes risk.

1. Inventory Your Data Assets with Litigation and Investigations in Mind

AI readiness starts with fully understanding your data landscape. Whether it's case files, legal correspondence, evidence repositories, or sensitive client data, knowing the scope and sensitivity of the information being fed into AI tools is essential.

Taking a comprehensive inventory allows organizations to:

    • Identify which data is relevant and necessary for specific litigation or investigations.
    • Establish where sensitive or protected information resides, such as privileged documents.
    • Classify data based on sensitivity, legal hold requirements, or client confidentiality.

An up-to-date inventory ensures that only pertinent information is input into AI systems for legal use cases, reducing the risk of mishandling sensitive data and maintaining compliance throughout the AI lifecycle.

2. Govern Your Data to Avoid Ethical Issues and Ensure Compliance

Information governance is crucial to using generative AI in sensitive areas like investigations or litigation. Without robust governance, the AI models used in managing case documents or conducting automated reviews could inadvertently produce biased or unethical outcomes.

For ethical, compliant AI use, organizations should:

    • Audit the data used in investigations, legal review, and record management regularly to confirm its completeness, quality, and relevance.
    • Apply role-based access controls, ensuring only authorized legal teams can input or retrieve specific data.
    • When inputting data into generative AI models, use anonymization or pseudonymization to protect personal and client-identifiable information.

These practices help maintain confidentiality and compliance with privacy regulations like GDPR or CCPA, and ensure that generative AI enhances legal processes without introducing unnecessary risk.

3. Reduce ROT Data to Minimize Legal Risks and Enhance AI Precision

Redundant, obsolete, and trivial (ROT) data often becomes a significant liability, particularly when AI is used to process large volumes of information for litigation or investigation purposes. When ROT data finds its way into AI workflows, it introduces unnecessary clutter, increases the risk of security breaches, and can mislead AI analysis—undermining the quality of automated legal assessments.

Reducing ROT data ensures that only relevant information is utilized, making AI models more precise and reducing exposure to potential legal and compliance risks. Automated information governance tools that sort, classify, and dispose of ROT data can also free up valuable resources and mitigate potential complications.

4. Focus on Data Quality to Improve Legal AI Outputs

Quality data is fundamental for AI to be effective in legal applications. When poor-quality data—such as incomplete case files, inconsistent evidence, or outdated records—is input into AI, it can lead to subpar outcomes, flawed legal conclusions, and potential harm to a case’s integrity.

To ensure high-quality data, legal teams should:

    • Clean and validate data before inputting it into AI tools used for case analysis, document review, or automated record-keeping.
    • Standardize document formats and ensure consistency across all inputs to avoid errors during AI processing.
    • Regularly verify the accuracy of AI-generated outputs to ensure that decisions or insights derived from AI are legally sound and defensible.

High-quality data results in more reliable AI-generated outputs, which is crucial for maintaining credibility in high-stakes investigations and litigation.

5. Manage Data Lifecycle to Stay Compliant and Mitigate Risks

In a legal context, data isn’t static—information is collected, processed, archived, and disposed of regularly. Effective data lifecycle management is crucial to ensuring compliance, especially when leveraging generative AI for legal investigations, litigation, or record-keeping.

Implementing robust lifecycle management includes:

    • Setting appropriate retention schedules to comply with legal hold obligations and regulatory requirements.
    • Monitoring data usage throughout its lifecycle, especially when sensitive or client-related information is used for AI-driven workflows.
    • Ensuring secure data disposal to prevent unauthorized access or exposure at the end of a data's lifecycle.

Strong data lifecycle management is key to reducing risks, maintaining compliance, and guaranteeing that your AI initiatives are built on reliable, well-managed information throughout legal processes.

Ensuring Data Readiness for Generative AI in Legal Operations

Generative AI is changing the game for investigations, litigation, and records management. Still, responsible data preparation will determine whether organizations thrive with AI or fall victim to its risks. By understanding and managing the data landscape, enforcing strong governance, eliminating ROT, ensuring data quality, and managing the entire data lifecycle, enterprises can confidently navigate the AI journey while minimizing harm, reducing compliance risks, and delivering valuable, ethical outcomes.

At ActiveNav, we recognize that the success of AI in sensitive areas like litigation and investigation begins with a foundation of reliable data management. Our solutions are designed to help organizations manage, classify, and prepare their data with confidence, ensuring that every input, analysis, and AI outcome upholds the highest standards of compliance, security, and ethical integrity.

Ready to ensure your AI initiatives are built on solid ground? We’re here to help.