Third-party AI software is externally developed AI code or services that an organisation incorporates into its environment. It may process data, call tools, or make decisions through models, agents, or APIs, so it expands the attack surface and requires review of identity, access, data handling, logging, and vendor risk.
Why third-party AI software changes your security boundary
Third-party AI software is not just another application dependency. Once you embed it, its models, agents, prompts, APIs, plugins, and update path can influence your own data handling and decision-making, so the security boundary shifts from “what we run” to “what we trust and integrate.”
That matters because externally developed AI can ingest sensitive inputs, emit sensitive outputs, and invoke downstream services on your behalf. If the vendor’s controls are weak, your environment inherits exposure through the integration rather than through your own code alone.
For teams assessing that boundary, the practical questions are whether the software is allowed to process organisational data, what it can call, and how its behaviour is governed across identity, access, and vendor oversight.
Core security concerns in third-party AI software
The main concern is not simply that the software is “AI,” but that it can act on information and systems in ways that ordinary software libraries usually cannot. A third-party AI service may retain prompts, train on inputs, surface retrieved content, or trigger tools, and each of those behaviours creates a distinct trust dependency.
That dependency becomes material when the tool is connected to internal repositories, SaaS platforms, tickets, chat systems, or production workflows. Mis-scoped permissions, weak tenant isolation, or unexpected model behaviour can turn a convenience feature into a data exposure or control failure.
Third-party AI also tends to evolve quickly. New model versions, connector changes, and policy shifts can alter the risk profile without a corresponding change in your own application code, which is why inventory and vendor change awareness matter so much.
How organisations assess exposure and trust
Assessment starts with knowing exactly which third-party AI products are present, what data they receive, and which identities they use to connect to other systems. That includes direct user access, service-to-service integrations, OAuth grants, API keys, and any embedded agent or automation features.
From there, organisations should check vendor documentation, security statements, and administrative controls to see how data is stored, whether prompts or outputs are retained, and how logs are accessed. The same review should cover model update behaviour, connector permissions, and whether the vendor supports tenant-level isolation and auditability.
Vendor review is also where contractual and governance questions surface. If the software can process regulated or confidential data, you need clarity on sub-processors, breach notification, deletion, retention, and the vendor’s own dependency chain.
Controls for safer use and ongoing oversight
Safer deployment usually comes from reducing what the third-party AI software can see and do. Limit the data sent to it, narrow its permissions, separate test and production use, and treat any embedded connector or agent as a privileged integration rather than a harmless add-on.
Logging and review are equally important. You want enough telemetry to reconstruct what data was submitted, what actions were taken, and which external systems were touched, while avoiding unnecessary retention of sensitive prompts or outputs.
When the product relies on accounts, tokens, or certificates, rotation and offboarding need to be part of the control model. A revoked vendor contract should not leave behind active credentials, unused webhooks, or dormant connections that can still reach internal systems.
How third-party AI software affects incident response
Because these products often sit between users and business systems, a compromise can spread faster than a single-app incident. If the vendor, an integration token, or a connected agent is abused, the issue may involve data exposure, fraudulent actions, or abuse of trusted automation rather than only malware on an endpoint.
Response plans should therefore include vendor contact paths, token revocation, connector disablement, and a clear way to review what the software accessed before containment. If the tool handled sensitive prompts, credentials, or decision data, post-incident review should also consider whether that information needs to be rotated, re-issued, or re-validated.
For widely connected products, the safest posture is to assume that compromise of the third-party layer can become compromise of the trust relationship itself, even if your own internal systems were not directly breached.
Risk and Threat Considerations
Third-party AI software can widen the attack surface because it often combines data access, external dependencies, and automated action in one place. If the vendor, connector, or embedded agent is compromised, attackers may gain a faster route to internal data or downstream systems than they would through a conventional application path.
Failure mechanism: The failure usually comes from over-scoped access, retained prompts or outputs, weak connector governance, or abuse of tokens and API credentials that the software uses to operate across systems.
Impact: The result can be data leakage, unauthorised actions, trust abuse across SaaS platforms, or persistence through a still-valid integration even after the original issue is discovered.
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 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 — Vulnerable Third-Party NHI | Third-party AI software often arrives through external integrations and vendor-managed access paths. |
| NHI-05 — Overprivileged NHI | AI connectors and agent accounts can hold excessive permissions across SaaS and internal systems. | |
| NHI-02 — Secret Leakage | Third-party AI software commonly uses tokens, keys, or certificates to reach external systems. | |
| Recommendation — Review vendor-connected AI services for inherited trust and third-party dependency risk before deployment. Constrain AI integrations to the minimum permissions needed for each approved use case. Protect and rotate integration secrets used by AI services, and revoke them promptly when exposure is suspected. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Third-party AI agents and connectors can abuse delegated authority if permissions are too broad. |
| Recommendation — Scope delegated AI authority tightly and verify every privileged action path. | ||
| NIST SP 800-53 Rev 5 | SA-9 — External System Services | This subject depends on managing risk from externally provided software and services. |
| AC-6 — Least Privilege | Third-party AI integrations should only hold the access needed to perform approved tasks. | |
| AU-2 — Event Logging | Logging is essential for reconstructing AI actions, data access, and downstream system use. | |
| Recommendation — Assess and monitor the security controls of external AI services before integrating them. Apply least privilege to vendor connections, service accounts, and tool permissions. Log AI service activity, connector actions, and administrative changes for investigation and review. | ||
Practitioner Guidance
Why practitioners should care: Treat third-party AI software as a governed dependency, not just a feature purchase. Its value comes with a trust and access decision, so ownership should sit with both the application team and the security function.
Governance implication: Require a documented approval path for new AI services, including data classification, permitted use cases, integration scope, and explicit review of any token, connector, or agent capability before rollout.
Practitioner takeaway: If you cannot clearly explain what the third-party AI can access, what it can do, and how you would disable it quickly, it is not ready for broad production use.
Related resources from NHI Mgmt Group
- Why do AI-generated code and third-party software increase application security risk in federal environments?
- Why do AI vendors create more third-party risk than traditional software vendors?
- Should organisations govern AI tools like third-party software dependencies?
- How should organisations govern embedded AI in third-party software under the EU AI Act?