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If you have ever spent a week gathering screenshots and spreadsheets before a manager's review, you know the feeling: most of the time goes to digging, and very little goes to thinking. Snowflake's internal audit team describes that same problem on a much bigger scale, and how AI helped them shrink it.
What internal audit does, and why it's slow
An internal audit team is a group inside a company that checks whether its processes work. Those processes are often called "controls," meaning checkpoints such as requiring two people to approve a payment.
Traditionally, auditors check past records by pulling a small set of random examples. This is called sampling (checking a slice instead of everything). Snowflake says this backward-looking approach is changing, because AI makes it practical to look at far more of the data.
What Snowflake says has changed
According to Snowflake's announcement, the team has seen several results:
- Faster fieldwork: Time spent on evidence collection and report production fell by 50%, from months to weeks.
- Full coverage in some areas: For certain checks, such as reviewing system access logs (records of who logged into which system and when), the team moved from samples to testing every record in the population.
- Hours saved: More than 3,500 hours in 2026 inside the audit team, plus over 1,500 hours for control owners, the staff who run each checkpoint across the business.
- Earlier warnings: Instead of finding problems months later, the team can surface risks as they happen.
Snowflake also gives examples, including reviewing order management, revenue scoping (deciding which revenue areas to examine) and key access controls.
How people stay in charge
Snowflake describes a "human-in-the-loop" model (a setup where a person reviews the AI's work before it counts). In its words, AI prepares and auditors conclude. The AI processes data and spots patterns, but an auditor checks any draft or flagged exception before anyone relies on it, and auditors own every conversation with business leaders.
The company also notes that the data was ready to use. Billing, revenue, HR and access logs already lived in one place, so the team could test full populations without combining messy sources by hand. Third-party tools run inside Snowflake's own account, which Snowflake says keeps the processing secure and contained.
One term from the announcement: SOX (the Sarbanes-Oxley Act) is a US law that requires public companies to keep reliable financial controls. Snowflake says AI helps its team stay ahead of that compliance work.
Wrap-up
Snowflake's story shows a practical pattern: AI speeds up the data work, while people review the output and make the decisions. You don't need Snowflake's setup to borrow the idea. Today, pick one recurring report or approval at work, write down which steps are pure copying and checking, and ask whether that information already lives in one place. That list is a good first step toward a faster, more reliable process.