A third-party AI dependency is any external service, platform, or partner that influences how AI is trained, deployed, monitored, or used. These dependencies expand the governance boundary because control evidence, ownership, and revocation may sit outside the core enterprise team.
Expanded Definition
Third-party AI dependency describes any outside model provider, hosting layer, data supplier, plugin, evaluation service, or integration partner that affects an AI system’s behaviour or security posture. In NHI governance, the dependency matters because the enterprise may consume the capability without directly controlling the underlying identities, secrets, logs, or revocation paths.
Definitions vary across vendors, but the practical boundary is simple: if a third party can change model outputs, retention, access, or operational continuity, it is part of the governance surface. This includes direct API access to foundation models, managed agents, retrieval services, and embedded AI features inside another product. Guidance in the OWASP Non-Human Identity Top 10 is useful here because the security issue is often not the model itself, but the non-human credentials and trust relationships that bind the enterprise to it.
The most common misapplication is treating a vendor AI feature as a standard SaaS dependency, which occurs when teams fail to map the hidden identities, data flows, and emergency cutoff points behind the service.
Examples and Use Cases
Implementing third-party AI dependency governance rigorously often introduces procurement and operational friction, requiring organisations to weigh speed of adoption against visibility into how external systems store, train on, or route sensitive data.
- An enterprise uses a hosted model API for customer support and must track which service account, token, and IP allowlist controls grant access to the provider.
- A developer platform embeds an AI coding assistant, creating a dependency on a separate model vendor and its retention policy, billing identity, and incident response process.
- A retrieval-augmented workflow sends internal documents to an external embedding or search service, making data handling terms and revocation capability part of the control review.
- A partner-operated agent runs tasks on behalf of the business, and its tool permissions must be reviewed alongside the partner’s own NHI hygiene and change-management practices.
- The DeepSeek breach illustrates how a third-party AI environment can expose training data, chat histories, backend credentials, and API keys when operational boundaries are not tightly governed.
For implementation patterns, teams often compare these dependencies against the 52 NHI Breaches Analysis and external guidance from OWASP Non-Human Identity Top 10 to decide whether the dependency is merely technical or also a control-plane risk.
Why It Matters in NHI Security
Third-party AI dependencies are security-critical because they extend trust beyond enterprise-managed systems into identities, secrets, and telemetry controlled elsewhere. That creates gaps in evidence, revocation, and monitoring, especially when vendors rotate keys, change model behaviour, or retain prompts in ways the enterprise cannot inspect. The result is often secret sprawl, over-privileged integrations, and weak incident containment.
This is not a theoretical concern. In NHIMG research on secrets and AI abuse, The State of Secrets in AppSec reports that 43% of security professionals are concerned AI systems may learn and reproduce sensitive information patterns from codebases. That concern grows when the AI capability itself is external and opaque. Threat patterns described in LLMjacking: How Attackers Hijack AI Using Compromised NHIs show how exposed credentials can be abused rapidly, making third-party access paths a direct target.
Organisations typically encounter the consequence only after a vendor incident, leaked credential, or unexpected model behaviour reveals that the dependency was 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 AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Third-party AI often depends on unmanaged non-human identities and external trust links. |
| NIST CSF 2.0 | GV.SC | Supplier governance covers external services that affect confidentiality, integrity, and availability. |
| NIST AI RMF | AI RMF addresses third-party model and data dependencies within the risk management function. |
Classify AI suppliers, assess risk, and require contractual control evidence before production use.
Related resources from NHI Mgmt Group
- How should security teams govern third-party AI agents that use OAuth access?
- How can IAM and security teams reduce third-party risk from AI-enabled SaaS tools?
- Should organisations treat AI vendors like third-party suppliers?
- When should organisations re-evaluate third-party controls for AI agents?
