Organizations are increasingly moving from AI experimentation to production implementation. However, many such initiatives stall due to the lack of a foundational IT infrastructure capable of ensuring the necessary level of security and data governance. Instead of integrating AI into a secure environment with clear access segmentation and auditing, enterprises often treat it as an isolated application, creating unacceptable security risks.
Why AI pilot projects stall: the barrier between experimentation and production security
Attempting to connect large language models (LLMs) directly to corporate databases without an intermediate management infrastructure layer often leads to projects being blocked by security teams. According to the Cisco AI Readiness Index 2025, only 13% of organizations are classified as "Pacesetters"—leaders that systematically outperform competitors in AI readiness and the ability to derive practical value from it. Most organizations remain stuck at the prototype stage.
Modern corporate AI often relies on Retrieval-Augmented Generation (RAG) architecture, which involves dynamic model queries to internal systems. Without a robust management framework, this can lead to uncontrolled leakage of confidential information and violations of access rights.
Infrastructure skeleton for LLMs: why public API access is insufficient
Simple connection to public APIs of commercial models does not satisfy enterprise requirements due to the inability to maintain full control. Secure deployment requires infrastructure built on the principles of AI Fortification and Cloud Reinforcement, as highlighted in the Cisco Cybersecurity Readiness Index 2025. This secure perimeter includes:
- Local or private cloud AI Gateways: intermediate nodes that intercept prompts to models and sanitize them of personally identifiable information (PII) before processing.
- Hybrid computing environment: the ability to run models on-premises or in a secure private cloud for handling the most critical data.
- Metadata management: a centralized description of business rules and data structures that strictly limits the information interpretation space for the model.
Access control and auditing: protecting against AI "over-curiosity"
The greatest infrastructure risk of LLM integration is that the model may gain access to data not intended for a specific user and inadvertently disclose it in its response. To prevent this, the architecture must support fundamental data-level access control mechanisms:
- Role-Based Access Control (RBAC): the AI model must interact with data exclusively within the scope of the user role initiating the request.
- Row-Level Security (RLS): applying security policies at the individual record (row) level in the database, ensuring the AI processes only the information the requester is authorized to see.
In addition to access segmentation, maintaining detailed audit trails is mandatory. All AI requests, generated responses, and database transactions must be logged to detect potential information leaks or internal policy violations in a timely manner.
Cost optimization and NIS2 compliance: regulatory requirements
Implementing AI requires significant computational resources. The Microsoft Azure Well-Architected framework, in its Cost Optimization section, emphasizes the need for strict governance policies and budget limits, which are key to preventing uncontrolled cloud spending.
Alongside financial control, organizations face increasing regulatory pressure. According to the ENISA Threat Landscape 2025 report, a significant portion of the 4,875 analyzed cybersecurity incidents affected critical entities subject to the European NIS2 directive. This obliges enterprises to build AI infrastructure with encryption, strict authentication, and continuous auditing to protect corporate systems.
Practical implementation: platform approach and Intecracy Group expertise
To build an infrastructure framework that ensures data governance and access control, enterprise companies should rely on established technological platforms. One such solution is the low-code platform UnityBase, a joint development of the companies within the Intecracy Group alliance (an alliance of independent companies linked by partner agreements and share exchanges). The platform serves as a reliable infrastructure layer thanks to a unified domain metadata model that generates REST APIs and provides built-in security mechanisms: RBAC, RLS, ACL, and detailed audit logging.
For high-load systems or projects with elevated security requirements, the official UnityBase documentation recommends using Enterprise (EE) or Defence (DE) editions, which support advanced authentication tools and integration with corporate directories.
Applied systems such as the Megapolis.DocNet electronic document management system function on top of UnityBase. Its built-in AI center automates document recognition and classification, with models operating within strict user permission boundaries, minimizing the risk of unauthorized access to confidential information through intelligent interfaces.
Creating reliable systems also requires a certified development approach. Softengi, a member of the alliance, provides AI consulting and AI agent development services in accordance with the ISO/IEC 42001:2023 standard, ensuring processes meet global requirements. For continuous monitoring of AI reliability, quality assessment modules—similar to those implemented in the Nectain platform—can be integrated into the infrastructure to continuously compare algorithm accuracy against human-verified reference data.
Organization infrastructure maturity scale for secure AI implementation
| Maturity Level | Infrastructure Characteristics | Security and Access Control |
|---|---|---|
| Level 1: Experimental | Ad-hoc use of external public APIs (e.g., ChatGPT). No integration with local databases. | Zero request auditing, no systematic protection against corporate information leakage. |
| Level 2: Controlled | Use of private cloud model instances. Basic request proxying. | Basic logging. Attempts to restrict access only at the UI level, not the data level. |
| Level 3: Integrated | Hybrid infrastructure with local database connectivity. Secure AI gateways in place. | Implementation of RBAC and RLS at the data access level. Transaction auditing. Cost optimization tools. |
| Level 4: Strategic | Full automation and integration of AI agents into key business processes (LLMOps). | End-to-end auditing, automated budget control, full compliance with NIS2 and ISO 42001 standards. |
FAQ
How can corporate AI be made compliant with NIS2 security requirements?
To comply with NIS2, the infrastructure must include robust data encryption during transit and at rest, strict user authentication, detailed logging and monitoring of all model requests, and the deployment of critical systems on-premises or in secure private clouds.
Why do standard application access rights not protect against leaks via LLMs?
If access rights are configured only at the application level and the AI connects to the database via a general system account, the model has the technical capability to 'see' all data. Only by integrating Role-Based Access Control (RBAC) and Row-Level Security (RLS) policies directly into the queries can you ensure the model processes only information authorized for the specific user.
Which infrastructure requirements of the ISO 42001 standard are critical for the enterprise segment?
The standard requires traceability and transparency of AI systems. This means having an infrastructure for mandatory logging of the model lifecycle, documenting data access architecture, regular assessment of response accuracy, and hallucination risk management.