Govern Internal LLM Applications

Deeploy enables organizations to deploy internal LLM applications with full governance, control, and EU AI Act compliance.

How Can Organizations Use Internal LLMs While Staying In control?

Organizations increasingly deploy LLMs like ChatGPT and Mistral AI for internal use cases, from customer support to document analysis. However, governing these applications while ensuring EU AI Act compliance presents significant challenges. Without proper controls, organizations risk regulatory violations, data breaches, and uncontrolled AI proliferation. Deeploy provides centralized governance infrastructure that enables organizations to deploy internal LLM applications safely while maintaining full compliance and control.

The Challenge of Deploying Internal LLM Applications

Lack of visibility: Teams across organizations adopt different general purpose AI models without coordination, creating shadow AI that leadership cannot track or govern effectively.
Prompt injection vulnerabilities: LLM applications face unique security risks including prompt injection attacks that traditional security measures don't address.
Inconsistent governance across providers: Organizations use multiple LLM providers (OpenAI, Mistral, Anthropic) but lack unified governance frameworks that work consistently across platforms.
Data privacy concerns: Sensitive information flows to external LLM providers without proper safeguards, creating potential data breaches and compliance violations.

Key Requirements for Internal LLM Governance

Effective LLM governance requires technical controls that operate in real-time rather than periodic audits. Organizations must implement guardrails that filter inappropriate inputs and outputs, preventing prompt injection attacks and blocking harmful content generation.

Risk classification: Assess each internal LLM use case against EU AI Act risk categories to determine appropriate governance intensity, from minimal oversight for low-risk applications to comprehensive controls for high-risk deployments.
Usage monitoring and audit trails: Real-time tracking of LLM interactions, input-output logging, and performance metrics that enable rapid incident investigation when issues occur.
Guardrails and safety controls: Implement input filters, output validation, and content screening that prevent sensitive data leakage, detect prompt injection attempts, and ensure LLM responses align with organizational policies.

How Deeploy enables Internal LLM Governance

Deeploy provides comprehensive governance infrastructure specifically designed for organizations deploying internal LLM applications while maintaining EU AI Act compliance. The platform enables rapid implementation of RAG use cases and other LLM applications with built-in controls that automatically enforce compliance requirements.

Complete LLM visibility: Central registry tracks all internal LLM applications, integrations, and use cases organization-wide, eliminating shadow AI and providing comprehensive oversight over generative AI adoption.
Automated EU AI Act compliance: Pre-built control frameworks translate regulatory requirements into technical controls with automated checks ensuring models meet required standards during their operation.
Guardrails for safe operation: Configure guardrails once and apply them to multiple LLM deployments, ensuring security standards across your entire AI portfolio without manual intervention.
Integrated monitoring and alerts: Real-time performance tracking, drift detection, and anomaly alerts enable teams to identify issues before they impact operations.

Frequently Asked Questions

About Governing Internal LLM Applications

Can we govern LLMs from different providers through a single system?

Yes. Deeploy integrates with multiple LLM providers including OpenAI, Mistral AI, and other generative AI platforms, providing centralized governance regardless of underlying model. Organizations maintain consistent oversight, compliance controls, and monitoring across all internal LLM applications without managing separate governance processes for each provider.

How do guardrails prevent sensitive data from reaching external LLMs?

Guardrails implement configurable filters that scan inputs before they reach LLM APIs, blocking patterns matching sensitive data categories like personal information, financial records, or proprietary business data. Deeploy’s guardrails operate in real-time, preventing data leakage while maintaining user experience.

What visibility does Deeploy provide into internal LLM usage?

Deeploy delivers complete visibility through centralized model registry, real-time usage monitoring, and compliance dashboards. Organizations see which LLM applications are deployed, who owns them, risk classifications, compliance status, usage patterns, and performance metrics.

Does Deeploy support RAG implementations for internal knowledge bases?

Yes, Deeploy enables organizations to implement RAG use cases that combine LLMs with internal knowledge bases. The platform provides the governance infrastructure needed to deploy these applications safely, including controls for data handling, output validation, and human oversight.