Artificial Intelligence (AI) is rapidly transforming the legal landscape, offering unprecedented opportunities for efficiency and innovation. However, this powerful technology also introduces new challenges to established information governance (IG) processes. Ignoring these challenges can lead to significant risks, including data breaches, compliance violations, and reputational damage.
“AI Considerations for Information Governance Processes,” a recent paper published by Iron Mountain, delves into these critical considerations, providing a framework for law firms and legal departments to adapt their IG strategies for the age of AI.
Key Takeaways:
- AI Amplifies Existing IG Risks: AI tools, especially machine learning algorithms, often require access to and process vast amounts of sensitive data to function effectively. This makes robust data security, privacy measures, and strong information governance (IG) frameworks absolutely paramount. Any existing vulnerabilities or weaknesses in your current IG framework can be significantly amplified by the introduction and use of AI, potentially leading to data breaches, privacy violations, and regulatory non-compliance.
- Data Lifecycle Management is Crucial: From the initial data ingestion and collection stage, through data processing, storage, and analysis, all the way to data archival or disposal, a comprehensive understanding and careful management of the AI’s entire data lifecycle is essential for maintaining data integrity and ensuring compliance. This includes knowing exactly how data is used for training AI models, for analysis and generating insights, and for any other purposes within the AI system.
- Vendor Due Diligence is Non-Negotiable: If you’re considering using third-party AI vendors or cloud-based AI services, conducting rigorous due diligence on these vendors is non-negotiable. This due diligence should focus heavily on evaluating their data security practices, their compliance with relevant industry standards and certifications, and their contractual obligations and guarantees regarding data protection and privacy.
- Transparency and Explainability are Key: “Black box” AI systems that make decisions without any transparency or explainability can pose significant risks. It’s crucial to understand how AI algorithms make decisions, especially those that impact individuals, to ensure fairness, accuracy, non-discrimination, and compliance with ethical guidelines and legal requirements. This often requires techniques like model interpretability and explainable AI.
- Proactive Policy Development is Essential: Organizations need to proactively develop clear policies, procedures, and guidelines for AI usage within their specific context. These should address critical issues such as data access and authorization controls, data retention and storage policies, data disposal and deletion protocols, as well as model training, validation, and monitoring practices.
The Time to Act is Now:
AI is not a future concern; it’s a present reality. Law firms and legal departments must proactively adapt their information governance processes to mitigate the risks associated with AI and unlock its full potential.