Many organizations are tempted to extend their data platform, such as Databricks Unity Catalog, into the role of enforcing fine-grained access control for AI agents and applications. Theoretically, this feels efficient: one centralized place for policies, governance, and enforcement.
But for regulated environments, this approach introduces significant risk, complexity, and long-term architectural limitations. The reality is simple: AI access control does not belong in the data platform; it belongs in the AI application layer.
1. Regulatory Pressure is Rising and Data Platforms aren’t Built for It
Regulated organizations today operate under overlapping frameworks like the EU AI Act, GDPR, and DORA. While Databricks Unity Catalog provides strong governance, it lacks a critical requirement for modern data protection:
- No native Format and Type Preserving Encryption (FPE): Standard encryption often breaks schemas. For example, a
birth_datecolumn like ’12-Jan-1990′ becomes a long, unreadable string that breaks PowerBI reports and analytics workloads.
Purpose-built platforms like SecuPi solve this by applying dynamic and persistent FPE and Type-safe encryption without breaking schemas, allowing privacy enforcement without disrupting operations.
2. AI Is Context-Rich — and Data Platforms Are Context-Blind
Modern AI applications rely on more than just identity; they rely on dynamic context:
- Purpose: Why is the data being accessed? (e.g., marketing vs. support)
- Geography: Where is the user? (e.g., secure network vs. mobile)
- Source: Integration with IAM systems like SailPoint or Entra.
- Sensitivity: The real-time classification of the data.
Enforcing this in the data platform creates fragility. In contrast, SecuPi enforces policies at the application or MCP tools layer, where context already exists cleanly and in real time.
3. Identity Sprawl Becomes an Operational Nightmare
Forcing every end-user identity into a data platform leads to “Identity Sprawl.” For a large organization, this means:
- Maintaining thousands of employee and millions of customer identities in Databricks.
- High costs for onboarding/offboarding and risk of dormant accounts.
SecuPi avoids this by using Attribute-Based Access Control (ABAC) that operates at the application level, resolving identity sprawl by utilizing service-level identities for the data platform while maintaining granular control externally.
4. Don’t Repeat the Mistakes of Traditional Applications
The industry moved away from managing access in databases decades ago for two reasons:
- Performance: Per-user access breaks connection pooling and scalability.
- Flexibility: Database-level policies are rigid and platform-specific.
The correct pattern is proven: Keep access control in the analytics tools and AI application layer, not the data platform.
5. Compliance and Audit Shouldn’t Require SQL Expertise
Extracting insights from standard data platform logs often requires manual SQL effort. SecuPi changes this model by providing:
- Instant, no-code built-in compliance reporting.
- Enriched audit logs with identity and sensitivity context, allowing audit teams to operate without engineering support.
6. Security Can Also Drive Cost Optimization
Data platforms are expensive. Inefficient queries and misuse of compute resources often go unnoticed. SecuPi provides full visibility into query behavior, offering immediate cost-saving opportunities by blocking or limiting inefficient access in real time.
7. Flexibility Is Critical in a Multi-Platform World
Organizations use a mix of Databricks, Snowflake, Azure, and Redshift. Policies embedded in one platform are hard to extend. SecuPi operates as a unified control layer across all environments, ensuring consistent enforcement and reducing vendor lock-in.
The Bottom Line for CDOs
Use Databricks Unity Catalog for coarse-grained data governance, but use SecuPi for context-rich fine-grained access control, encryption, and sovereignty across your AI applications.