Multiverse Computing is launching SentinelAI, a production AI governance and control layer designed to give enterprises real-time visibility into and control over how AI models are used. It is deployed on-premises and tailored to each organization’s specific systems and requirements. This gives teams one consistent control layer across the models and AI applications their organization already uses — without losing visibility or control over their data, and without tying governance to a single provider.
Closing the AI governance gap
Enterprise AI adoption has outpaced the governance infrastructure built to support it. As AI becomes more deeply embedded in everyday workflows, it creates new pathways for data leakage. Sensitive data can surface in source code, credentials, customer records, internal documents, or prompts. Traditional security and data-loss-prevention tools were built for more predictable systems; AI interactions are unstructured, context-dependent, and constantly changing as information moves between users, applications, APIs, and models.
Many AI governance programs still focus primarily on what happens before a model is deployed, using model evaluations, red-team exercises, usage policies, and prompt-level guardrails. These measures matter, but they can't anticipate every interaction a system will encounter in production, especially as AI systems expand into multi-model workflows, retrieval systems, and autonomous applications. Real users introduce unexpected inputs, sensitive context, and data combinations that may never have appeared in testing. A law firm might submit confidential client materials to an AI model to summarize or compare documents, while a banker might use customer financial data to analyze a portfolio or identify trends. These types of confidential data are already being deployed to the cloud; without the right controls, organizations may lack clear visibility and control over where it goes, how it is processed, and whether it should have been shared at all.
Effective protection requires visibility and control where AI interactions actually occur. It must also inspect both sides of the exchange: what users and applications send to a model, and what the model sends back.
The control layer between every application and model
Deployed inside an organization’s own infrastructure, SentinelAI sits between an organization’s AI applications, users, agents, and the models they call. Each request and response passes through the same governed pipeline that can detect sensitive data and re-identification risk and enforce custom policies set by the organization, such as allowing, blocking, redacting, or flagging an interaction for review.
SentinelAI is designed to help organizations identify and address risks including:
- Personally identifiable information and confidential data appearing in prompts or responses
- Credentials, source code, and internal documents being sent to unauthorized models
- Prompt-injection and jailbreak attempts intended to override system instructions
- Harmful, unsafe, or non-compliant model behavior
- Unmonitored AI spend and token usage that lead to runaway costs across teams
- Gaps in records needed for internal reviews and regulatory audits
Every step is logged, giving compliance, legal, and security teams a structured, audit-ready record of how their systems behave in production — what data went in, what came out, and what was done about it. A control dashboard provides visibility into a unified record of all actions, giving teams a continuous evidence layer that eliminates the need to manually build an audit trail for each review.
One governance layer across the AI stack
Enterprises don’t always rely on only one model, and governance controls tied to a single model vendor can leave organizations with fragmented policies and inconsistent monitoring. SentinelAI is model-agnostic and designed to work across commercial, open-source, in-house, and Multiverse’s own compressed models, applying consistent policies even as the AI stack changes. Because the gateway intercepts traffic transparently, it can be introduced without rewriting existing applications.
Deployed where the data lives
SentinelAI is Kubernetes-native and model agnostic. It works with any AI provider via OpenRouter or in-house model, and is deployable on-premise, on an organization’s own infrastructure. The governance infrastructure, detection models, and audit records remain in the customer’s environment, with no ongoing connection to Multiverse required. No data leaves the environment, which matters for organizations weighing AI adoption against sovereignty and data-residency requirements. When the underlying models are also deployed locally, the full governed workflow can operate without a cloud connection.
The governance engine uses lightweight Multiverse language models that are purpose-built for classification and risk detection. This is the core differentiator: governance powered by specialized Multiverse models, adding contextual judgment beyond static keyword, regular-expression checks, or third-party APIs that would require data to leave a customer's environment.
Tailored governance
Multiverse works with each organization to map models and data flows, understand its sensitive data and compliance requirements, and define what policies should be enforced, encoding those requirements as runtime policies. SentinelAI is then deployed and validated against real traffic inside the customer's environment. Following policy refinement and knowledge transfer, the customer can operate and extend the governance layer independently.
Enabling AI adoption with greater control
SentinelAI gives organizations a practical way to protect sensitive data, apply consistent controls, and maintain audit-ready evidence, all without giving up flexibility over which models they run.
To learn more, contact business@multiversecomputing.com, or visit multiversecomputing.com.