They often focus on procurement checks and forget the ongoing governance dependency. Once a vendor AI system influences regulated decisions, the insurer still needs visibility into data use, audit cooperation, access boundaries, and change management. Without that, the organisation inherits regulatory and operational risk it cannot fully observe.
Why This Matters for Security Teams
Third-party AI risk is often treated like a vendor due-diligence exercise, but insurers and insured organisations face a much wider control problem once an external model influences underwriting, claims handling, fraud scoring, or customer communications. Procurement questionnaires can confirm that a provider has policies, yet they rarely prove how training data is governed, how prompts are logged, or whether model changes are communicated before impact. NIST’s NIST AI Risk Management Framework is useful here because it pushes teams toward lifecycle governance, not one-time assurance.
The practical issue is accountability. If a third-party AI system makes or influences a regulated decision, the insurer still owns the customer outcome, the recordkeeping obligation, and the need to explain or challenge the decision path. That means risk teams need visibility into data provenance, output validation, access boundaries, human override points, and incident notification terms. For insurers, the gap is not usually a missing policy statement; it is the absence of operational evidence that the vendor can support control testing after deployment. In practice, many security teams encounter the real failure only after a claim dispute, complaint, or regulatory query has already exposed the lack of auditability.
How It Works in Practice
Managing third-party AI risk starts by treating the vendor model as a living dependency, not a static product. The insurer should define the use case, decision authority, data categories, and acceptable failure modes before integration, then require evidence that the vendor can maintain those controls over time. This is where the security and governance conversation overlaps with identity, secrets, and access control: service accounts, API keys, and model administration rights must be tightly scoped, tracked, and revocable. The OWASP Non-Human Identity Top 10 is relevant when vendors or internal orchestration layers use machine identities to call AI services.
A practical control set usually includes:
- Data processing limits, including which inputs can be sent to the model and whether they may be retained or reused.
- Audit rights covering prompts, outputs, version changes, and incident logs.
- Human review thresholds for high-impact decisions, especially where adverse customer outcomes are possible.
- Change notification for model updates, retraining events, and policy shifts that could alter behaviour.
- Segregated access for admin, support, and engineering functions, with revocation tested regularly.
Security teams should also map the vendor relationship into the broader control framework. The NIST Cybersecurity Framework 2.0 helps anchor governance, protection, detection, and response around the AI service rather than around the procurement file. Where the vendor uses generative capabilities or autonomous workflows, the NIST Cyber AI Profile is especially useful for aligning AI-specific risks such as misuse, output reliability, and attack surface expansion with security operations. These controls tend to break down when the insurer has no contractual right to inspect logs or when the AI service is embedded inside a broader platform and version changes are not surfaced to the customer.
Common Variations and Edge Cases
Tighter vendor oversight often increases contracting, review, and monitoring overhead, requiring organisations to balance assurance against speed to market. That tradeoff becomes sharper in insurance because different AI use cases carry very different risk profiles, and best practice is still evolving for some of them.
For low-impact internal productivity tools, a lighter control set may be reasonable if the system cannot make or influence regulated outcomes. For claims triage, underwriting support, fraud scoring, or customer servicing, the bar should be much higher because the model can shape decisions that affect fairness, explainability, and complaint handling. There is no universal standard for how much model transparency a third-party insurer should provide in every case, but current guidance suggests the contract must at least establish data handling, change notice, audit cooperation, and incident escalation. Where the vendor itself uses sub-processors or another model provider, the insurer should also understand the downstream chain of dependence.
AI management system standards can help structure this conversation. ISO/IEC 42001:2023 AI Management System Standard is useful for asking whether the vendor has a repeatable management system rather than ad hoc controls. That said, certification or policy alignment is not the same as real visibility into production behaviour. The hardest edge case is a packaged platform where the insurer cannot inspect model internals but still bears the regulatory consequence of the output. In those environments, the governance model often fails unless the contract, technical telemetry, and escalation paths are all defined before go-live.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and MITRE ATLAS address the attack and risk surface, while NIST AI RMF, NIST CSF 2.0 and NIST AI 600-1 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | GOVERN | Third-party AI risk hinges on accountability, oversight, and lifecycle governance. |
| NIST CSF 2.0 | GV.RM | Vendor AI exposure is a governance and risk-management issue, not just procurement. |
| OWASP Agentic AI Top 10 | A01 | Autonomous or tool-using AI introduces prompt, output, and control-abuse risks. |
| MITRE ATLAS | AML.T0001 | AI systems face model abuse patterns that procurement checks will not detect. |
| NIST AI 600-1 | GenAI services need operational controls for output validation and traceability. |
Embed third-party AI into risk governance, control testing, and ongoing assurance processes.
