Bring Your Own LLM

Expanded Definition

Bring Your Own LLM is an operating model for enterprise AI in which the organisation selects and connects its own large language model to a workflow, instead of accepting a fixed vendor model as the default. In practice, the pattern is used to improve control over data handling, model behaviour, routing, and auditability, but it does not remove the need for identity controls around the model, its service accounts, and any upstream tools it can reach. The definition is still evolving across vendors, especially where the boundary sits between model hosting, orchestration, and policy enforcement. Guidance from NIST AI 600-1 Generative AI Profile and NIST AI Risk Management Framework is useful here because BYO LLM only works safely when the model is governed as a managed asset, not treated as a plug-in. The most common misapplication is assuming that choosing a different model automatically improves security, which occurs when organisations replace the provider brand but leave the same weak credentials, unlogged prompts, and unrestricted tool access in place.

Examples and Use Cases

Implementing BYO LLM rigorously often introduces more integration and governance overhead, requiring organisations to weigh model flexibility against the cost of policy enforcement, observability, and lifecycle management.

• An enterprise routes customer-support prompts to an internal model because it needs tighter data residency and prompt logging than a default provider can guarantee, while mapping access to its own approval workflow.
• A regulated financial team connects a vetted open-weight model to document summarisation, then layers policy checks so the model cannot call tools beyond the approved case-management scope, consistent with OWASP Agentic AI Top 10.
• A software organisation uses BYO LLM for code review so it can tune context windows and reduce vendor lock-in, but it still must protect the model endpoint and API keys the same way it would protect any other NHI.
• Security teams study incidents such as AI LLM hijack breach and the LLMjacking research to understand how exposed credentials can turn a model integration into an attacker-controlled execution path.
• Architecture teams compare internal BYO LLM governance with the control expectations discussed in the OWASP NHI Top 10 and related agentic application guidance before granting production access.

Why It Matters in NHI Security

BYO LLM matters because the model itself becomes part of the enterprise attack surface once it is connected to identity, data, and tools. If the service account, token, or orchestration layer is overprivileged, the model can amplify a small mistake into broad data exposure or unauthorised action. That risk is not theoretical: NHIMG research on AI Agents: The New Attack Surface report shows that 80% of organisations report AI agents have already acted beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing credentials. The same lesson applies to BYO LLM deployments, especially when teams assume the model choice is the control rather than the identity and policy envelope around it. For deeper risk framing, security teams should also align to NIST AI 600-1 Generative AI Profile and the CSA MAESTRO agentic AI threat modeling framework when defining guardrails for execution authority, logging, and human review. Organisations typically encounter the operational impact only after a model has already accessed data it should not have touched, at which point BYO LLM governance becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• How should security teams handle onboarding when customers bring their own identity provider?
• How should security teams govern vendor access in Bring Your Own Cloud deployments?
• Why does bring-your-own-cloud deployment matter for IAM automation?
• Who should own response when LLM abuse becomes a phishing channel?