In 2025, the focus of artificial intelligence implementation in the enterprise environment shifted from LLM experimentation to building robust infrastructure foundations. According to the Cisco AI Readiness Index 2025, based on a global study of over 8,000 leaders across 30 countries and 26 industries, data readiness is a key barrier to deriving business value from AI. Many enterprises face risks because they perceive AI agents as a "black box," overlooking the importance of a contextual data map.
Why data readiness is about context, not volume
For most corporate systems, AI agents process data as unstructured text. However, in an enterprise environment, a lack of understanding regarding corporate hierarchy, permissions, and business logic turns an agent into a risk vector. According to the ENISA Threat Landscape 2025, digital infrastructure and services accounted for approximately 27.7% of all recorded data breaches. This underscores the need for Machine Trustworthiness—the system's ability to guarantee that an agent acts strictly within authorized permissions.
The problem of "blind" automation: lack of a business map
Without a domain data model, AI agents often make critical errors:
- RBAC violations: An agent attempts to access confidential financial reports, ignoring corporate policies due to a lack of metadata restricting its queries.
- Hallucinations due to context: An LLM interprets fragmented data as facts, leading to reporting errors.
- Lack of infrastructure constraints: An agent may initiate database operations for which it lacks permissions, as the access interface contains no built-in business constraints.
Domain models as an infrastructure safeguard
To minimize the "blast radius," metadata—such as RBAC, RLS (Row-Level Security), and business rules—must be integrated at the architectural level. This is analogous to FinOps principles, where management becomes a shared responsibility between engineering and business. The Cisco Cybersecurity Readiness Index 2025 highlights the importance of solutions like Identity Intelligence for ensuring infrastructure security.
UnityBase: building architecture for secure AI operations
The UnityBase platform, developed by the Intecracy Group alliance, provides a ready-made metadata model that integrates RBAC and RLS directly at the system architecture level. Solutions built on this platform allow AI agents to interact with data only within a clearly defined business context. Instead of "bolting on" security over an LLM, developers use UnityBase's built-in mechanisms to generate REST API and ORM layers that pass through a centralized control layer.
AI agent readiness levels
| Level | Infrastructure characteristics |
|---|---|
| Level 1 | Chaotic data: LLM connected to raw databases without filters. |
| Level 2 | Basic security: access exists, but context is missing. |
| Level 3 | Domain model: metadata defines permissions and relationships. |
| Level 4 | AI-native infrastructure: RLS/RBAC at the metadata level restrict the agent. |
Remember that an AI agent requires supervision. Managed automation is a balance between the tool's autonomy and the strict governance policies of your domain model.
FAQ
How can I restrict an AI agent's access to confidential data?
Use RLS (Row-Level Security) and RBAC at the domain data model level, which automatically filters access to database rows regardless of the AI's query.
Does a domain model prevent AI hallucinations?
It restricts the model's context to structured business objects, allowing AI to work with reliable relationships rather than random text fragments.
What is the role of UnityBase in ensuring the security of AI integrations?
UnityBase provides built-in domain model and access control mechanisms (RBAC/RLS) that ensure agent actions comply with corporate rules at the architectural level.