A governed platform — audit, RBAC, usage accounting, and guardrails — that scales across every team and domain, without sending a byte off-prem.
Enterprise AI governance is the layer that makes AI deployable in regulated organizations: a complete audit trail of every model interaction, role-based and attribute-based access control, usage accounting per team and domain, and guardrails that enforce policy on every prompt and response. Hosted AI APIs provide none of this by default.
Enterprise AI rollouts fail not on capability but on governance. Audit trails, access control, usage accounting, and policy enforcement are afterthoughts in hosted APIs — but requirements for regulated organizations.
The full enterprise AI platform: inference, RAG, guardrails, governance, and audit — with multi-tenancy and domain isolation for every team.
Add cross-organizational AI collaboration when teams need to work across boundaries without sharing raw data.
Explore Prism AIEnterprise AI governance requires four capabilities: (1) a complete, queryable audit trail of every model interaction — who asked what, when, and what the model returned; (2) role-based and attribute-based access control that limits which users, teams, or domains can access which models; (3) guardrails that enforce organizational policy on every prompt and response; and (4) usage accounting that shows which teams are consuming AI capacity, for cost allocation and capacity planning.
An AI audit trail logs every interaction with the model — the user identity, the prompt, the model version, any RAG context retrieved, the response, and a timestamp. In Cube AI, this log is stored on-premises, fully queryable, and cannot be modified or deleted by the users whose interactions are being recorded. It satisfies the logging requirements of frameworks including EU AI Act, GDPR, and DORA.
RBAC (role-based access control) for AI restricts which users can call which models, access which knowledge bases, and see which outputs. In a multi-team deployment, RBAC ensures that the marketing team's AI instance is isolated from the legal team's — preventing data leakage between domains. ABAC (attribute-based access control) adds dynamic rules, such as allowing access only from a specific IP range or with a specific data classification attribute.
NVIDIA NeMo Guardrails is an open-source framework for enforcing behavioral constraints on language model outputs. In Cube AI, guardrails are applied on every prompt and response: they can block jailbreaks, enforce topic restrictions, redact PII, and ensure the model stays on-task. Unlike application-layer filtering, guardrails run on the inference path and cannot be bypassed by the user.
In Cube AI's multi-tenant architecture, each business unit or team is assigned a domain: an isolated environment with its own models, knowledge bases, usage quotas, and access policies. Domain isolation ensures that one team's data — including their RAG documents and interaction logs — is never visible to another team, even on shared infrastructure.
Hosted AI APIs offer inference and billing — nothing more. They provide no audit trail your compliance team can query, no access control beyond an API key, no policy enforcement beyond basic content filtering, and no accountability about where your data goes. Enterprise AI is inference plus governance: the full platform needed to deploy AI in a regulated organization.
Talk to the team about enterprise deployments, governance requirements, and regulatory rollouts.
