Infrastructure readiness: why 13% of companies outperform the market
According to the Cisco AI Readiness Index 2025, only 13% of organizations worldwide are classified as "Pacesetters." These leaders outperform competitors through infrastructure readiness rather than just model adoption. The primary challenge for CTOs today is not AI capabilities, but ensuring data sovereignty when integrating LLMs into hybrid cloud environments. When data is transmitted to third-party AI providers without proper control, critical security and compliance risks emerge.
LLMs as a "black box": architectural risks in hybrid clouds
The "black box" problem arises when corporate databases interact directly with LLMs via standard APIs. According to the ENISA Threat Landscape 2025, organizations subject to NIS2 regulations account for a significant portion of those affected by security incidents. Direct integration often ignores the principle of least privilege, complicating compliance with requirements for protecting critical assets.
The "Gatekeeper" pattern: separating data contours and intelligent processing
To minimize risks, it is advisable to implement the "Gatekeeper" architectural pattern. In this model, the LLM acts solely as a stateless processor. The data management platform acts as an intermediate layer: it verifies user permissions, filters data according to security policies, and only then forms the context for the LLM. Using RAG (Retrieval-Augmented Generation) allows only anonymized, specific data fragments to be passed to the model, minimizing access to sensitive information.
NIS2 compliance: why RLS and RBAC must be embedded in the system core
NIS2 compliance requires robust asset protection and auditing. The UnityBase platform, a joint development by companies within the Intecracy Group alliance, ensures this level of control through built-in Domain metadata mechanisms. Applying RLS (Row-Level Security) and RBAC (Role-Based Access Control) at the system core level guarantees that no request to the LLM exceeds the user's access rights. Isolating business logic in a private contour while using external APIs only for non-sensitive tasks is the architectural foundation for secure AI integration.
Checklist for AI integration architectural readiness
- Presence of a data filtering mechanism (RLS) before sending to the LLM.
- Status of the LLM as a stateless processor (no training on corporate data).
- Centralized auditing of all requests to AI models.
- Separation of contours: business logic in a private segment, AI processing in an isolated contour.
- Automated cost monitoring and governance policies in accordance with the Azure Well-Architected methodology.
FAQ
How can NIS2 compliance be ensured when using third-party LLM APIs?
Implement a Gatekeeper architecture where the data platform acts as an intermediary, providing auditing, access control (RBAC/RLS), and data filtering before transmission to the LLM.
How can Row-Level Security be implemented for data passed to an RAG system?
RLS must be implemented at the system core level, for example, through the UnityBase platform's Domain metadata, which ensures record filtering before context formation for vector search.
Why is direct integration of corporate databases with LLMs risky?
Direct integration creates a risk of confidential data leakage, as the LLM may gain access to redundant records, which contradicts GDPR and NIS2 requirements regarding the limitation of privileges.