Snowflake Achieves 50% Reduction in Audit Fieldwork with AI
Discover how Snowflake's Internal Audit team harnessed AI to cut fieldwork time by half, enabling real-time data analysis and proactive risk management. This innovative approach redefines traditional auditing practices and boosts overall efficiency.
Key Facts
- Snowflake cut audit fieldwork time by 50%, enhancing operational efficiency and agility.
- Transition to 100% population testing reveals a shift from reactive to proactive risk management.
- 3,500+ hours saved in audits indicates significant cost reduction and improved resource allocation.
- AI-driven insights allow auditors to focus on strategic risks, enhancing overall business governance.
- Consolidated data in Snowflake enables real-time visibility, reducing delays in operational audits.
Summary
Summary
Snowflake's Internal Audit team faced inefficiencies in their audit processes, which were traditionally time-consuming and reactive. By implementing AI through Snowflake CoCo, they reduced fieldwork time for evidence collection and report production by 50%, saving over 3,500 hours in audit activities in 2026.
Background
Snowflake is a cloud-based data-warehousing company that has grown rapidly within the technology sector. Prior to the deployment of AI, the Internal Audit team relied on historical data sampling to confirm the effectiveness of controls, resulting in lengthy audit processes that focused on past events rather than proactive risk management.
Challenge
The primary challenge was the inefficiency of the traditional audit process, which involved extensive manual evidence collection and testing. This approach limited the team's ability to provide timely insights and effectively manage risks.
Solution
The Internal Audit team implemented AI using Snowflake CoCo, which automated data processing, pattern recognition, and initial analysis. This allowed auditors to shift from sampling to testing entire populations of data. The team built applications and dashboards that streamlined evidence collection and reporting, enabling continuous monitoring of key controls and risks.
Results
The deployment of AI led to significant time savings and efficiency improvements:
- Fieldwork time for evidence collection and report production decreased by 50%, from months to weeks.
- The team saved over 3,500 hours in 2026, with 1,000+ hours saved on risk scoping and 2,500+ hours on testing and documentation.
- Control owners across the business saved an additional 1,500+ hours.
- The audit process transitioned from random sampling to 100% population coverage, enhancing risk visibility and assurance.
Key Insights
- Automating routine tasks allows auditors to focus on strategic risk management and high-impact areas.
- Centralizing data in a single platform, like Snowflake, is crucial for effective continuous monitoring.
- Maintaining human oversight in the AI process ensures accountability and trust in the findings.
Customer Testimonial
"I'm proud of what my team is building at Snowflake. With the right data foundation, even one of the most traditional functions in a company can change how it works." — [Name not provided], [Title not provided]
Entities Mentioned
Companies
Products
Technologies
Organizations
Key Concepts
Definitions
- AI
- Artificial Intelligence used to automate data processing and analysis in auditing.
- SOX
- Sarbanes-Oxley Act, a U.S. law that mandates strict reforms to improve financial disclosures from corporations.
- continuous monitoring
- An ongoing process of auditing that allows for real-time risk assessment and compliance checks.
- full population testing
- Testing that includes all data points rather than a sample, providing comprehensive assurance.
- human-in-the-loop model
- A system where human oversight is integrated into AI processes to ensure accountability and accuracy.
Use Cases
- →Automated key order-to-cash controls testing
- →Revenue stream scoping
- →Automated reconciliation testing
- →SOX controls testing and documentation
- →Operational audits
- →Continuous assurance for strategic risk
Frequently Asked Questions
How has AI impacted the internal audit process at Snowflake?
AI has significantly reduced fieldwork time for evidence collection and report production by 50%, allowing auditors to focus on higher-risk areas and strategic insights.
What are the benefits of using Snowflake CoCo?
Snowflake CoCo enables the creation of automated workflows and skills for continuous monitoring, which streamlines evidence collection and enhances audit efficiency.
How does continuous monitoring improve audit quality?
Continuous monitoring allows for real-time visibility into risks and compliance, enabling auditors to catch issues before they escalate and ensuring comprehensive coverage of all data.
What role do auditors play in the AI-driven audit process?
Auditors are responsible for reviewing AI-generated outputs, making final evaluations, and ensuring that all conclusions are accurate and reliable.
What steps should organizations take to implement AI in their audit functions?
Organizations should start with high-impact controls, integrate their data into a single platform like Snowflake, and develop automated skills while maintaining auditor oversight.