How should security teams evaluate SaaS app risk when GenAI features may expose company data?

Security teams should assess whether a SaaS app uses customer data for model training, sends data to third party GenAI services, and how that changes exposure. Pair that with app legitimacy checks, privacy policy review, and access review. The goal is to understand both direct data handling risk and the broader trust boundary around the application.

Why This Matters for Security Teams

GenAI features change SaaS risk because the application is no longer just storing and processing company data, it may also be reusing that data in prompts, embeddings, support workflows, analytics, or external model calls. That broadens the trust boundary and makes privacy policy review only a starting point. Security teams need to know whether the vendor trains on customer content, which subprocessors receive the data, and whether secrets or regulated data can be surfaced through AI responses. NHIMG research on non-human identity compromise shows how quickly hidden trust paths become real incidents, as highlighted in the The 52 NHI breaches Report.

This is also a governance problem, not just a procurement question. A SaaS app with GenAI features can create new indirect exposures even when the core product looks compliant on paper, which is why current guidance suggests pairing data-flow analysis with legitimacy checks and access review. The NIST Cybersecurity Framework 2.0 remains a useful baseline for identifying assets, managing exposure, and monitoring third-party risk, while NHIMG’s Ultimate Guide to NHIs is a reminder that machine-to-machine trust often expands faster than teams can inventory it. In practice, many security teams discover GenAI data leakage only after a vendor has already ingested sensitive content into a model path that was never reviewed during procurement.

How It Works in Practice

Evaluate SaaS GenAI risk as a data-path and identity question. Start by identifying what data the app can see, what data it can generate, and where both are sent next. If the application uses customer content to improve models, human review, or retrieval systems, that is a material change in exposure even if the vendor claims the feature is “optional.” The question is not only whether the app is legitimate, but whether its AI workflow creates a new downstream recipient for company data.

A practical review should include:

• Whether customer data is used for model training, fine-tuning, or prompt retention.
• Whether the vendor routes content to third-party GenAI services, subcontractors, or plugin ecosystems.
• Whether administrators can disable AI features, limit data classes, or restrict external sharing.
• Whether access logs, audit trails, and retention settings allow later investigation of AI-assisted access.
• Whether company secrets, tokens, or regulated records are excluded from prompts and attachments.

The NIST AI 600-1 GenAI Profile is useful here because it emphasizes mapping generative AI risks to concrete controls rather than treating them as abstract AI ethics issues. For NHI context, NHIMG’s Salesloft OAuth token breach and Snowflake breach show how trusted integrations and token exposure can turn a SaaS platform into a high-impact data conduit. These controls tend to break down when a SaaS product silently introduces AI copilots, support summarisation, or cross-app agents because procurement, security, and business owners assume the original approval still covers the new workflow.

Common Variations and Edge Cases

Tighter SaaS review often increases friction for business users, requiring organisations to balance data minimisation against the operational value of GenAI features. That tradeoff becomes sharper when teams cannot easily distinguish “product functionality” from “model telemetry,” because vendors may package both inside the same subscription.

Best practice is evolving for several edge cases. If a vendor says customer data is not used for training but allows optional human review, the exposure may still be unacceptable for regulated or confidential data. If GenAI is powered by a third-party model provider, the security team should treat that provider as part of the effective trust boundary, not as an invisible backend detail. If the feature only summarises internal documents, the content may still be copied into logs, cached outputs, or support queues, which means retention and deletion controls matter as much as model policy.

For high-risk SaaS categories, current guidance suggests using stronger intake controls, such as limiting which data classes can reach the app, requiring contractual commitments on training and retention, and re-running access reviews after feature changes. Where an app exposes sensitive collaboration data, the review should also ask whether user permissions map cleanly to AI outputs, because a model that can search broadly can surface information that a normal UI would not reveal. NHIMG’s DeepSeek breach is a reminder that data governance gaps often appear first in the AI layer, not the base application.

Related resources from NHI Mgmt Group

• How should security teams control SaaS data sharing risk?
• Why do GenAI chat tools create data leakage risk for IAM and security teams?
• How should security teams evaluate a data security platform against identity risk?
• How should security teams protect sensitive data across SaaS and GenAI workflows?