Sigma for Financial Services Teams

No. Sigma never stores, extracts, or caches customer data. Every action in Sigma generates SQL that executes live against your cloud data warehouse (Snowflake, Databricks, BigQuery, or Redshift), and only the rendered result is returned to the user's browser. Your data never leaves your secure environment.

No. Sigma inherits your warehouse's existing security policies including row-level security, dynamic data masking, and role-based access controls automatically. No secondary entitlement layer is required. If a user doesn't have access to a column or row in Snowflake, they cannot see it in Sigma.

Sigma provides audit logs capturing user activity, content access, and administrative actions. Logs can be exported to your SIEM or security monitoring platform. For query-level lineage, Snowflake's query history provides a complete, warehouse-authoritative audit trail of every SQL statement Sigma issued on your behalf.

Sigma supports formal software development lifecycle (SDLC) workflows through workbook version tagging and workspace-level environment separation. Teams can tag specific workbook versions as "production," restrict user access to tagged versions only, and enforce a review-and-approve promotion process from development to QA to production, meeting the controls required by regulated financial institutions.

Every calculation, formula, and transformation in Sigma is transparent. Users can inspect the underlying logic at any cell, and Sigma surfaces the exact SQL sent to the warehouse. This full calculation transparency, combined with Snowflake's query audit log, enables regulated firms to demonstrate data lineage, reproducibility, and access history to internal model risk teams and external regulators.