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Policy Gap Analysis Automation

1 post · Aug 2026

What this trail covers

Harvey's Policy Gap Analysis capability has been enhanced to automate the process of comparing organizational policies against regulatory benchmarks. This includes identifying external, internal-consistency, control, and operational gaps. The system leverages AI to synthesize benchmarks into discrete obligations, compare entire policy estates, and check for evidence of operational adherence. The analysis can be rerun continuously as benchmarks or policy estates change, transforming it from a per-project effort to an ongoing, automated process. This iteration focuses on improving the AI's ability to interpret regulatory change, map obligations to existing policies and contracts, and draft policy updates, while maintaining attorney oversight and providing an audit-ready record.

All Harvey trails
  1. How to Run a Compliance-Ready Policy Gap AnalysisThis post details the technical approach to automating policy gap analysis. It introduces the concept of identifying four types of gaps: external, internal-consistency, control, and operational. The operational gap is highlighted as a key differentiator, requiring evidence of practice beyond document review. The post outlines a six-step process: defining benchmarks and obligations, assembling policy sets, comparing policies to obligations (leveraging AI for synthesis and comparison), checking for operational evidence, prioritizing gaps, and recording findings with an audit trail. It emphasizes the use of AI for bulk analysis and continuous reruns, transforming a manual, sampled project into a repeatable, automated workflow. The integration of Harvey Vault and Harvey Agents is mentioned as enabling this automation.
  2. AI for Regulatory Compliance: Monitor, Interpret, ActThis post details the application of AI to the 'interpret' and 'act' stages of regulatory change management, specifically within the Policy Gap Analysis capability. It describes how AI synthesizes regulatory updates, assesses applicability based on organizational factors (entity, activity, jurisdiction), and translates interpretations into actionable items like policy updates and obligation mapping. The contribution focuses on the workflow enhancement from monitoring alerts to informed action, emphasizing the role of AI in drafting and analysis while retaining human accountability for final decisions and risk assessment. It also highlights the importance of traceable records and enterprise-grade security for compliance workflows.
  3. Corporate Compliance AI: Keeping Up With Regulatory ChangeThis post details how Harvey's Corporate Compliance AI assists in-house teams by accelerating the interpretation of regulatory change, mapping obligations to policies and controls, and drafting policy updates. It highlights the AI's role in synthesizing regulatory developments, producing plain-language summaries, identifying applicability factors, breaking down regulations into discrete obligations, and generating draft revisions for policies and contracts. The contribution also emphasizes the importance of source-grounded responses and enterprise-grade security for compliance work, and clarifies the division of responsibilities between AI and legal professionals.