How should security teams assess AI features in vendor software before buying?

Why This Matters for Security Teams

Vendor AI features are not just product enhancements; they are potential data access paths, integration brokers, and model-reuse channels. That makes pre-purchase review a security decision, not only a procurement one. NIST’s NIST Cybersecurity Framework 2.0 is useful here because it forces teams to examine governance, asset exposure, and third-party risk before deployment.

The hard part is that AI features often arrive with broad defaults: opt-out rather than opt-in activation, unclear retention rules, and vague descriptions of what content may be reused for training. In NHI environments, that creates the same failure pattern seen in account and token abuse. NHIMG’s Ultimate Guide to NHIs — The NHI Market shows how quickly third-party access can become opaque once systems are connected, which is directly relevant when a vendor AI tool can touch files, tickets, chats, or APIs.

Security teams should treat the feature as a trust boundary extension. If the AI can see sensitive data, send prompts to a third-party model, or trigger actions in other systems, the review must cover data flow, log retention, and contractual restrictions on model reuse. In practice, many security teams discover those details only after the feature has already been enabled in production.

How It Works in Practice

A practical assessment starts with four questions: what data the feature can access, what it can do with that data, where the data goes, and who can turn it on. That means reviewing admin controls, user-facing notices, retention settings, and any integration scopes that expand access beyond the base product. The goal is to determine whether the AI feature is a passive assistant or an active data processor with downstream system access.

Security and procurement teams should ask vendors for written answers on:

• Whether prompts, uploads, outputs, and telemetry are retained, and for how long.
• Whether customer content is used to train vendor models or shared with subprocessors.
• Whether the feature can access inboxes, documents, CRM records, code, or ticketing systems.
• Whether administrators can disable the feature, limit it by role, or require explicit opt-in.
• What logs are available for prompt activity, output generation, and external calls.

This review should also map to your identity and access controls. If the AI feature uses service accounts, OAuth grants, API keys, or delegated admin permissions, those are non-human identities and should be governed like any other privileged access path. NHIMG’s DeepSeek breach material is a reminder that exposed secrets and weak visibility can turn an AI-related feature into a fast-moving compromise path, not a theoretical risk. If vendor documentation is vague, current guidance suggests treating the feature as high risk until legal, security, and data protection teams agree on acceptable use and retention terms.

These controls tend to break down when the AI feature is embedded inside a widely used platform and shares the same permissions model as the host application, because the blast radius becomes difficult to isolate.

Common Variations and Edge Cases

Tighter AI feature review often increases procurement friction, requiring organisations to balance faster adoption against stronger data and identity controls. That tradeoff is especially visible when business teams want an assistant feature enabled for everyone, while security needs staged rollout, logging, and explicit limits on sensitive content.

There is no universal standard for this yet, so best practice is evolving. Some vendors provide clear model-use commitments and administrative controls; others expose only coarse toggles or bury important terms in product-specific addenda. In those cases, the safe approach is to require a written risk acceptance decision before any sensitive workflow is enabled.

Common edge cases include:

• Features that are technically “off” but can be enabled by end users through a personal workspace.
• Embedded AI that routes prompts to a third-party model outside the organisation’s direct contract boundary.
• Tools that summarize or index regulated content, creating retention and discovery implications even when outputs look harmless.
• Agentic features that can take actions, not just generate text, which raises the bar for approval.

For purchase decisions, the practical rule is simple: if the AI feature can touch sensitive data or connect to other systems, it needs the same scrutiny as any other privileged integration, with explicit limits on logging, retention, training use, and third-party sharing.

Related resources from NHI Mgmt Group

• How should security teams handle risks from AI browser extensions?
• How should security teams govern API keys used for generative AI access?
• How should security teams assess AI readiness before scaling agents and copilots?
• How should security teams govern AI transformation across identity and access programmes?