aiqbench analysis
Snowflake's internal audit team leveraged its native data platform and AI coding agent to automate evidence collection, enabling 100% population testing instead of sampling and saving thousands of hours annually. This case study demonstrates how enterprise-grade AI infrastructure can transform back-office operations from historical checks into continuous, automated monitoring systems. The approach underscores the critical role of unified data lakes in facilitating scalable AI adoption for complex compliance workflows.
How Snowflake’s Internal Audit Team Cut Fieldwork By 50% with AI
See how Snowflake's internal audit team used AI to save thousands of hours a year, and what it took to get there.
Internal audit has long been seen as a backward-looking function that sifts through historical samples to confirm that controls worked. On Snowflake's Internal Audit team, AI has changed that. We have cut the time we spend on evidence collection, testing and audit report production by 50%. Overall, we have saved more than 3,500 hours in audit in 2026 and over 1,500 hours for control owners across the business. Whether it’s reviewing system access logs or analyzing key financial transaction patterns, AI allows us to shift from a historical "point-in-time" check to continuous, automated monitoring. We no longer just identify what went wrong six months ago; we can surface key risks for specific controls and use cases and ask targeted questions. Here’s a look at how AI is helping the Snowflake Internal Audit team rewrite the audit playbook, backed by real-world data and measurable impact. Fieldwork time for evidence collection…
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