Security teams should treat LLMs like any other high-risk platform capability and establish clear approval, data handling, and control requirements before deployment. That means validating security readiness, limiting exposure of sensitive data, and testing how the model handles prompts, outputs, and integrations. Governance should also cover privacy, compliance, and ongoing review as the technology and its use cases evolve.
Why This Matters for Security Teams
LLM governance has to start before deployment because the risk is not limited to model quality. Enterprise LLMs can expose sensitive prompts, leak regulated data through integrations, and turn ordinary API access into a broad attack path when secrets, plugins, or retrieval layers are misconfigured. Current guidance from NIST AI Risk Management Framework and OWASP Agentic AI Top 10 both point to the same operational reality: governance must cover data, access, outputs, and downstream systems, not just the model endpoint.
That is especially important because early abuse often comes through credentials and connected services rather than the model itself. NHIMG research on LLMjacking shows how quickly exposed cloud credentials are targeted, while DeepSeek breach analysis highlights how hidden secrets and exposed records can amplify an AI incident into a larger compromise. In practice, many security teams encounter LLM misuse only after a pilot has already connected to production data or external tools without a formal control gate.
How It Works in Practice
Pre-deployment governance works best as a staged approval process. Security teams should define the intended use case, classify the data the LLM may see, map every integration, and test whether prompts or outputs can trigger unsafe behaviour. That includes reviewing whether the model can retrieve files, call APIs, write code, or pass data to another system. The question is not only “Can the model answer?” but also “What else can it reach?”
A practical control set usually includes:
- Data minimisation so prompts exclude secrets, customer records, and sensitive internal context unless there is a clear business need.
- Environment separation so sandbox testing is distinct from production and connected with distinct identities.
- Secret scanning and access review for every connector, token, and service account used by the LLM workflow.
- Red-team testing of prompt injection, data exfiltration, and unsafe tool use before approval.
- Logging and review so prompt, tool, and output activity can be investigated after launch.
For identity and access, teams should treat the LLM stack like a high-risk workload and use short-lived, tightly scoped access rather than long-lived credentials. The AI LLM hijack breach research and the NIST Cybersecurity Framework 2.0 both support an approach where governance includes protect, detect, and respond controls before broad rollout.
These controls tend to break down when teams connect an LLM to broad internal search, write-back permissions, or unmanaged third-party plugins because the model can chain actions faster than the approval process can react.
Common Variations and Edge Cases
Tighter LLM governance often increases friction for product teams, requiring organisations to balance speed of adoption against data exposure, review time, and integration complexity. That tradeoff is real, especially for customer-facing copilots, internal knowledge assistants, and code-generation workflows where business pressure pushes for rapid deployment.
Best practice is evolving for agentic and multi-tool LLM systems. There is no universal standard for this yet, but guidance from CSA MAESTRO agentic AI threat modeling framework and OWASP NHI Top 10 suggests stricter review for systems that can take actions, not just generate text. That means stronger approval thresholds for write access, external tool invocation, and any use case that can alter records or trigger workflows.
Edge cases also matter. A read-only chatbot may still become risky if its retrieval layer includes privileged documents. A harmless prompt template can become unsafe if users can inject instructions into it. And a model that is safe in one region may create compliance issues in another if logs, embeddings, or vendor processing move data across borders. Security teams should therefore reassess governance whenever the model, the connector set, or the data classification changes. In practice, the hardest failures appear when a “low-risk” pilot quietly becomes an operational dependency without a new control review.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Covers secret exposure and credential misuse in LLM-connected systems. |
| OWASP Agentic AI Top 10 | A1 | Addresses prompt injection and unsafe tool use before deployment. |
| CSA MAESTRO | Maps directly to pre-deployment threat modeling for agentic AI workflows. | |
| NIST AI RMF | GOVERN | Govern function fits approval, accountability, and oversight for enterprise LLM use. |
| NIST CSF 2.0 | PR.AC-4 | Least privilege is essential for LLM connectors, plugins, and service accounts. |
Model each LLM integration as a distinct threat surface and gate release on risk acceptance.
Related resources from NHI Mgmt Group
- How should security teams govern non-human identities in cloud environments?
- How should security teams govern an AI gateway that brokers LLM traffic, MCP servers, and agents across enterprise environments?
- How should security teams unify identity controls across human and non-human access in complex enterprise environments?
- How should security teams use password managers to reduce breach risk in third-party environments?
