Snowflake Internal Audit Director Amrita Kapoor: AI Shifts Auditing from Historical Sampling to Full-Population Continuous Monitoring

Internal audit has long been viewed as a retrospective function—confirming whether controls were effective in the past through historical sampling. Amrita Kapoor, Director of Internal Audit at Snowflake, argues that AI has changed this dynamic: auditing can shift from point-in-time checks to continuous, automated full-population monitoring. Her team provides specific mechanisms and quantifiable results to support this transition.

Kapoor emphasizes that the foundation for all of this is data infrastructure. Snowflake’s billing, revenue, human resources, access logs, and audit log data are already centralized on the Snowflake platform, allowing the audit team to execute tests directly against full populations without stitching together multiple systems. She notes that if data were scattered across disconnected systems, none of this would be possible. This implies that AI here does not replace data governance but rather builds upon it once governance is complete.

The specific approach involves building reusable skills and applications using Snowflake CoCo (the AI coding agent). For revenue stream scoping, a skill scans key dimensions of revenue in near real-time and sends snapshots on demand, saving over 500 hours annually. Order-to-cash testing utilizes AI_EXTRACT from Cortex AI Functions to extract revenue recognition terms, start/end dates, and billing terms from order documents; a CoCo-built application then cross-validates these against the billing system, testing every order rather than relying on samples. Key report testing employs a Report-as-a-Service architecture integrated with audit logs for continuous monitoring, achieving annual testing savings of over 450 hours.