Yes. Organisations should evaluate generative AI tools the same way they assess other third-party services, with attention to data handling, model inputs, and downstream use. The key question is whether sensitive information can be exposed, retained, or misused through the tool. That assessment should shape where AI is permitted, what data it can access, and what controls surround it.
How to think about generative AI tools as third-party services
Generative AI tools should be treated as third-party services because they introduce an external processing boundary, a separate operator, and a distinct set of contractual, technical, and data-handling assumptions. That means the decision is not only about whether the tool is useful, but whether its trust model fits the business context in which it is used.
The practical test is whether the tool can see, store, transform, or infer from information that the organisation would not want a normal external service to handle. If the answer is yes, the AI tool belongs in the same review path as other vendors that may receive business data, credentials, customer content, or regulated information.
This is why the right comparison is usually not “AI tool versus internal software”, but “AI tool versus any other third-party service with access to the same data and workflow.” A tool that only drafts marketing copy creates a different risk profile from one that processes customer records, source code, support tickets, or internal strategy notes.
What changes in the risk review for AI tools
The review should focus on data exposure, retention, and secondary use. Some tools use prompts and uploaded content to improve services, some retain logs longer than users expect, and some route content through subcontractors or model providers. Those behaviours can matter even when the tool is technically convenient and the immediate output looks harmless.
Organisations should also consider where the tool sits in the workflow. A generative AI product that only helps with low-sensitivity drafting may be acceptable in more places than one connected to confidential documents, customer communications, development environments, or operational systems. The more the tool touches high-value or high-trust data, the more it should be controlled like any other third-party dependency.
That review should include the downstream effect of the output as well. If users paste confidential material into a model, or if the model’s output is copied into business decisions, code, or external messages, the AI tool is no longer just a productivity aid. It becomes part of the information supply chain and should be governed accordingly.
Where the control boundary should be drawn
The decision boundary is usually set by sensitivity and allowed purpose. If the organisation would not send the same information to another external processor, it should not send it to a generative AI tool without a specific justification and control set. That means restricting use cases, defining approved data classes, and making sure the business owner understands the tool’s data path.
Good control design also distinguishes between public tools, enterprise tools, and embedded AI features inside other SaaS products. The risk is often not the model itself, but the combination of prompt handling, retention, administrative access, integration points, and vendor terms. Treating all of them as one generic “AI” category hides the real exposure.
When the tool is embedded in a platform already used by the business, the same third-party review logic still applies. Convenience does not remove the need to understand who can access the data, how long it is retained, and what happens when the vendor changes terms or model behaviour.
Risk and Threat Considerations
Generative AI tools can create exposure through data retention, prompt leakage, overbroad access, and unintended reuse of business content. The main risk is that staff will treat a model like a private workspace when it may be an external processing service with broader retention and visibility than expected.
Failure mechanism: Sensitive information is entered into a tool that stores prompts, logs, outputs, or training signals beyond the organisation’s intended boundary, or the tool is allowed access to data classes it does not need.
Impact: Confidential information, regulated data, or business context can be exposed, retained, misused, or propagated into downstream workflows, increasing legal, operational, and reputational risk.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP API Security Top 10 addresses the attack surface, NIST CSF 2.0 sets the technical controls, and ISO/IEC 27001:2022 and SOC 2 (AICPA) define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.SC-01 — Cybersecurity Supply Chain Risk Management | GenAI tools are third-party services with supply-chain and vendor risk. |
| ID.RA-01 — Asset Vulnerabilities Are Identified and Managed | Tool placement depends on sensitivity, retention, and workflow exposure risk. | |
| PR.DS-01 — Data-at-rest is protected | Retention and storage of prompts or outputs can expose business data. | |
| Recommendation — Assess vendor data handling and subprocessors before approving business use. Classify the data each AI tool can touch and gate use by sensitivity. Require retention and deletion terms before sensitive prompts are allowed. | ||
| ISO/IEC 27001:2022 | A.5.19 — Information security in supplier relationships | AI tools are suppliers whose handling of data must be governed. |
| A.5.12 — Classification of information | Permitted AI use depends on the sensitivity of the data entered. | |
| A.8.12 — Data leakage prevention | Prompt submission can leak confidential information outside the business boundary. | |
| Recommendation — Apply supplier controls before authorising enterprise AI use. Classify data so AI usage restrictions follow information sensitivity. Block or monitor high-sensitivity data from entering external AI tools. | ||
| SOC 2 (AICPA) | CC9.2 — Risk Mitigation and Vendor Management | Vendor oversight is central when AI tools process business information. |
| CC6.6 — Logical Access Security Software | AI tools may receive or infer access to sensitive content through workflow integration. | |
| Recommendation — Document third-party AI risks and approve controls before rollout. Restrict AI tool access to the minimum data and systems needed. | ||
| OWASP API Security Top 10 | API8 — Security Misconfiguration | Enterprise AI services often hinge on misconfigured retention, access, or sharing settings. |
| Recommendation — Review AI service settings for retention, sharing, and access defaults. | ||
Practitioner Guidance
What to verify: Confirm the vendor’s data retention, training, subprocessors, admin-access model, and deletion terms before allowing business users to rely on the tool for anything beyond low-risk content. If those terms are unclear, treat the tool as unsuitable for sensitive use.
Decision rule: If the AI tool can receive confidential, customer, regulated, or operationally sensitive information, require the same third-party approval path you would use for any other external service handling that data. If it cannot meet that standard, limit it to approved low-sensitivity use cases.
Practitioner takeaway: The important question is not whether generative AI is “safe enough” in general, but whether a specific tool is acceptable for a specific data class, workflow, and trust boundary. Allow the tool only where you can explain and defend the exposure, retention, and downstream-use model.
Related resources from NHI Mgmt Group
- Why do AI agents and workflow automations increase operational risk when they interact with business data and third-party tools?
- What should organisations do when they adopt third-party AI tools without a clear risk review?
- How should organisations evaluate vendor AI risk when third-party products use generative models on customer data?
- When should organisations treat an NHI as a high-priority risk?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 25, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
