What are the essential things to consider when assessing a third-party tool or in-house solution? Our checklist below provides useful guidelines to follow to ensure your AI solution is implemented successfully. With thanks to Bugra Ozer, Data Science and AI Governance Lead, UK, Dentons, for contributing to this checklist.

• Hosting and infrastructure
  • Assess technical solution architecture map, hosting arrangements and data localization requirements.
  • Evaluate the risk of vendor lock-in and the capability to migrate data and AI models.
• Data privacy and security
  • Ensure clear data-handling policies, including what data is collected, how it is stored, whether it's used in training and retention timelines.
  • Ensure adherence to privacy regulations (e.g., GDPR, CCPA) and assess robust breach notification process and incident response plan.
  • Check if the vendor provides logs that can be reviewed for audit or regulatory compliance (e.g., model decisions, data transformations).
• Model transparency and bias management
  • Ensure the vendor has a documented process for updating or retraining models (e.g., version control, testing protocols, clear release notes).
  • Require explainability and interpretability of AI models as much as possible.
  • Inquire about ongoing monitoring of model performance, data drift detection, and re-validation to maintain reliability and fairness.
  • Demand bias mitigation strategies, regular audits, and clear model versioning to maintain an ongoing record of changes and performance.
• Vendor risk tiering and assessment
  • Smaller vendors— assess wider, place stronger emphasis on financial stability, technology maturity, and governance frameworks.
  • Growing vendors—verify scalability, client references, and co-innovation potential.
  • Established providers—prioritize advanced security measures, global support capabilities, and contract flexibility.
• Ethical and regulatory compliance
  • Confirm that ethical AI principles are embedded in the vendor’s development and deployment processes.
  • Look for evidence of compliance certifications (ISO 27001, SOC 2, etc.) and third-party audits.
  • Check whether the vendor aligns with emerging AI frameworks (e.g., NIST, EU AI Act)
• Contractual terms and data stewardship
  • Define data ownership, deletion, and retention policies in legally binding agreements.
  • Specify IP rights and set clear exit clauses to ensure smooth termination and data portability if the partnership ends.

To explore this topic in more detail, check out the full webinar recording of, “Exploring AI use in legal functions” here.