We’re implementing embedded Cognos Analytics in our SaaS product that serves multiple enterprise customers. Security is our top concern - we need absolute data isolation between tenants while maintaining performance and user experience.
Tenant isolation is challenging because we’re using a shared database architecture for efficiency, but customers demand guarantee that their data can’t be accessed by other tenants. Row-level security seems like the solution, but we’re concerned about performance impact and implementation complexity at scale.
Encryption requirements include data at rest, data in transit, and potentially encryption of data in memory for highly sensitive customers. Audit logging must capture every data access with tenant context for compliance and security investigations.
How are others implementing secure multi-tenant embedded analytics in cloud environments? What security patterns and architectures have proven effective? I’m particularly interested in balancing security requirements with performance and operational simplicity.
Row-level security performance depends heavily on implementation. We use materialized views with tenant filtering built in, rather than applying RLS dynamically on every query. The views are refreshed every 15 minutes, which is acceptable for our use case. This approach reduced query time by 60% compared to dynamic RLS while maintaining security. Cache the tenant context at session level to avoid repeated permission checks.
We use a hybrid isolation model - separate Cognos instances per tenant for enterprise customers (who pay premium) and shared instances with strict row-level security for smaller customers. This gives us flexibility in the security vs. cost tradeoff. Enterprise customers get dedicated resources and complete logical isolation, while smaller customers share resources but still have secure data separation.
For encryption, use cloud-native services rather than implementing your own. AWS KMS, Azure Key Vault, and GCP Cloud KMS all provide robust encryption key management. Enable transparent data encryption (TDE) at the database level for data at rest. Use TLS 1.3 for data in transit. For memory encryption, Intel SGX or AWS Nitro Enclaves can provide runtime encryption, but they’re complex and may not be necessary unless you’re in highly regulated industries.
Audit logging is critical for compliance (GDPR, HIPAA, SOC2). Log every data access with: tenant ID, user ID, timestamp, data accessed, action performed, and result (success/failure). Store logs in immutable storage (AWS S3 with object lock or Azure Blob immutable storage) to prevent tampering. Implement log analysis for anomaly detection - unusual access patterns often indicate security breaches. We detected a compromised account within 15 minutes using this approach.