Identity and access governance and IT general controls automation: The most common points of SOX failure are user access reviews. Financial institutions can automate key access reviews by integrating Snowflake’s native security features with identity providers such as Okta or Azure AD. This allows dashboards to instantly surface dormant accounts, orphaned users or role assignments that violate policies to dramatically reduce manual review burdens, Kapoor says.
Automated alerting and remediation workflows: “Monitoring is useless if no one acts on the findings,” Kapoor says. “This makes integration between collaboration and ticketing systems essential, as real-time alerts can be set up via Slack, email or API calls to ticketing systems like Jira and ServiceNow.” The systems can automatically create remediation tickets assigned to control owners when unauthorized transactions occur, establishing the documented, closed-loop audit trail auditors love.
Dynamic Data Masking and row-level security: Financial institutions must protect sensitive data, including personally identifiable information, from their own auditors. Snowflake’s Dynamic Data Masking protects such data based on a viewer’s role, so auditors can verify controls are working — as when a bank account is reconciled — without seeing account numbers or customer names.
Time Travel and Fail-Safe for data integrity: SOX mandates that financial data not be altered or deleted without a trace, so Snowflake offers Time Travel and Fail-Safe capabilities, letting users query up to 90 days and seven days, respectively. “If a balance changes mysteriously, the user can query exactly what the table looked like at 2 p.m. yesterday, versus 2:05 p.m., to find the exact transaction that caused the change,” Kapoor says.
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Best Practices for Implementing SOX Automation
Rather than trying to automate everything at once, financial institutions should start where transaction volumes are high or manual errors frequent — monitoring key financial transactions processing or user access reviews, Kapoor says.
Make sure to rationalize before your institution automates.
“Automation won’t fix a bad process,” Kapoor says. “Clean house first, and review your current controls to see which are redundant or outdated.”
Ensuring cross-system integration so software can talk to the entire tech stack — HR systems, IT service desks, financial databases and other critical systems — is essential to being effective, Kapoor says.
Finally, while automation can be used to flag issues, financial institutions should have a qualified professional — their “human in the loop” — making final calls on remediation and design, Kapoor says.
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How To Choose and Implement the Right SOX Automation Solution
SOX monitoring can be thought of in three parts.
“This monitoring consists of vulnerability management, configuration management and data protection monitoring at a minimum,” Thames says. “There are other monitoring and automation that should be implemented within networks, such as intrusion detection, but these three components are central to having a safe and secure environment for financial data.”
From there it’s a matter of financial institutions finding a platform that scales with their data, integrates across their systems and stands up to auditor scrutiny, Kapoor says.
Vendor solution criteria for financial institutions to consider include:
- Prebuilt connectors for integrating into their specific ERP and cloud environments
- An architecture that can scale to handle high-volume data ingestion from perhaps millions of monthly transactions
- A self-service interface allowing auditors to easily pull evidence and audit data
- The ability to explain why risks were flagged, if the tool uses artificial intelligence for that work
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Common Challenges and How To Overcome Them
Auditors may assume automated results are a black box, so financial institutions must document their logic and prove their accuracy early on through parallel testing — manual versus automated — for the initial cycle, Kapoor says.
Another hurdle institutions face is data quality issues. Messy underlying data leads to messy automation results, so a prerequisite data cleansing and standardization phase is essential to any automation project.
Finally, expect internal cultural resistance to SOX automation solutions.
“Teams may fear automation is meant to replace them,” Kapoor says. “To overcome this, it’s important to reframe the project as a way to remove rote work — allowing staff to focus on high-value risk analysis rather than data entry.”
