Transitioning from pilot AI projects to industrial operation requires rethinking security approaches. According to the Microsoft 2026 Work Trend Index, 49% of interactions with AI agents involve cognitive work, including data analysis and decision support. However, integrating these tools as external "free agents" often ignores existing access policies (RBAC/RLS), creating serious risks to data integrity.
From "free agents" to managed integration
The problem lies in the lack of a strict link between AI queries and system access policies. Secure architecture involves moving away from direct database access in favor of API gateways and adapters that transform AI output into structured formats compliant with corporate data schemas.
NIST AI RMF 1.0: structuring risks
To minimize threats, the NIST AI Risk Management Framework 1.0 methodology should be applied, which structures risk management through four functions: Govern, Map, Measure, and Manage. The implementation of AI agents must be designed through the lens of these functions at the initial stages of integration.
The role of API gateways in centralizing access control
API gateways are a critical layer for centralizing authentication and traffic monitoring. As noted by Kong experts, this layer ensures user token verification before any request from an AI agent to services is executed, preventing unauthorized access to sensitive data.
Model-driven architecture as a tool for security policy compliance
An effective solution is a model-driven architecture, where RBAC and RLS policies are an integral part of object metadata. The UnityBase platform allows for this approach, ensuring strict data visibility limitations. This guarantees that an AI agent is restricted in its actions by the platform's metadata, which physically prohibits access to PII (personally identifiable information) in the absence of appropriate rights. Solutions built on the UnityBase platform (e.g., Megapolis.DocNet) use these mechanisms to automatically enforce security policies.
Ensuring data integrity during automation
According to Enterprise Integration Patterns, the use of adapters allows for the transformation of AI model output into a structured format before writing to the accounting system. This minimizes the risk of unvalidated data entering the corporate database.
| Maturity level | Approach description |
|---|---|
| Level 1: Ad-hoc | Direct AI agent access to DB, no control |
| Level 2: API-wrapped | Using API without user context verification |
| Level 3: RBAC-enforced | API gateway checks access rights before each request |
| Level 4: Metadata-driven | RLS and access rights limited by metadata at the platform level |
FAQ
How to limit an AI agent's access to sensitive data?
Use API gateways for mandatory RBAC token verification and implement visibility restrictions at the object metadata level (model-driven architecture).
Can an AI agent be integrated into an existing RBAC system without code changes?
Yes, through API gateways that act as intermediaries and translate user context into access rights before accessing the system.
How to ensure data integrity when recording AI output?
Use integration adapters that validate and structure data according to corporate schemas before entering it into the system.