The rapid spread of multiple AI models across an organisation’s cloud estate, often through managed services, embedded copilots, and custom workflows. It becomes a governance issue when each model introduces new identities, data paths, and permissions that security teams must inventory and control.
Expanded Definition
AI model sprawl is not just a count of models. In NHI security, it is the growth of separate model endpoints, managed copilots, fine-tuned variants, and workflow-integrated agents that each carry distinct access paths, secrets, and data handling rules. The security problem emerges when teams treat these as interchangeable services instead of discrete identities with unique blast radii.
Definitions vary across vendors on whether sprawl includes only externally managed models or also internally hosted inference stacks, but the operational concern is consistent: every additional model can expand your attack surface and complicate governance. The right lens is closer to NIST Cybersecurity Framework 2.0 than to pure inventory management, because the issue spans identify, protect, detect, and respond functions. NHI Management Group also treats model sprawl as a control problem when it creates unmanaged non-human access, as discussed in the Ultimate Guide to NHIs — Key Challenges and Risks.
The most common misapplication is assuming all model deployments can share a single policy set, which occurs when organisations overlook model-specific data access, tool permissions, and secret dependencies.
Examples and Use Cases
Implementing controls for AI model sprawl rigorously often introduces slower rollout and more review overhead, requiring organisations to weigh deployment speed against the cost of untracked model expansion.
- A finance team enables multiple hosted LLMs for analysis, each with different API keys and data residency settings, creating separate governance obligations.
- An engineering group adds a custom agent to a CI pipeline, but the agent inherits broad repository access and a long-lived token, increasing non-human identity risk.
- A customer support organisation uses embedded copilots across ticketing, knowledge, and chat systems, making it hard to trace which model touched which data.
- A machine-learning team fine-tunes a new model for one department while the original model remains active elsewhere, leaving duplicate inference paths and inconsistent retention rules.
- The DeepSeek breach illustrates how model expansion can intersect with exposed records, insecure training data, and backend credentials when governance is fragmented.
In practice, model sprawl is often evaluated alongside standards such as the NIST Cybersecurity Framework 2.0, because inventory, access control, and incident response must all account for model-specific behaviour.
Why It Matters in NHI Security
AI model sprawl becomes a security issue because each model can introduce new secrets, permissions, telemetry, and third-party dependencies that are easy to miss in manual reviews. When models are launched faster than governance can catalogue them, security teams lose sight of which systems can call tools, reach sensitive datasets, or generate outputs that affect downstream actions. That makes containment harder when one model is compromised or misconfigured.
This matters acutely in NHI programs because model sprawl usually arrives with secret sprawl. NHIMG research shows organisations maintain an average of 6 distinct secrets manager instances, a fragmentation pattern that undermines centralised control and makes model-linked credentials harder to govern. Entro Security’s LLMjacking research also shows how quickly exposed AWS credentials are targeted, with attackers attempting access in an average of 17 minutes.
Once a model has been over-permissioned, shared broadly, or tied to exposed keys, the issue is no longer theoretical. Organisations typically encounter the operational cost only after a data leak, unauthorized tool call, or abusive inference bill, at which point AI model sprawl becomes operationally unavoidable to address.
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 address the attack and risk surface, while NIST CSF 2.0 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-02 | Model sprawl often expands secret and identity sprawl across AI services. |
| NIST CSF 2.0 | ID.AM-1 | AI model sprawl is fundamentally an asset inventory and governance problem. |
| NIST CSF 2.0 | PR.AA-1 | Each model should have explicit authenticated access to only required resources. |
Assign least-privilege access to model services and review permissions whenever a model is added.
