Security teams should map every AI touchpoint across the product, then tie each one to a specific function, data flow, and decision boundary. The practical goal is to separate cosmetic AI features from systems that influence user actions, sensitive data handling, or automated decisions. A defensible answer depends on inventory, telemetry, and clear ownership of each AI component.
Why This Matters for Security Teams
Answering where AI is used in a product or service is not a branding exercise. It determines whether the organisation can explain data handling, review decision-making, and assign ownership when the AI changes output, behaviour, or risk. Security teams need a defensible inventory because AI can sit in obvious user-facing features, but also in ranking, routing, summarisation, fraud screening, support automation, and internal decision support.
That matters because the security question is often not “is there AI?” but “what does it touch, and what can it influence?” If the answer is vague, teams usually miss hidden dependencies such as third-party model calls, embedded copilots, retrieval layers, or agent workflows that act on behalf of users. Current guidance suggests treating these as part of the control scope, not as optional product embellishments. The NIST Cybersecurity Framework 2.0 is useful here because it pushes teams toward asset awareness, governance, and risk communication rather than informal assumptions.
In practice, many security teams encounter the real AI footprint only after a customer complaint, a privacy review, or an incident reveals a previously unrecorded model path.
How It Works in Practice
The strongest approach is to build an AI service map that sits alongside the application architecture and data flow diagrams. Each AI touchpoint should be recorded with four basics: what the system does, what data it consumes, what decisions or recommendations it produces, and whether a human can override it. That makes it easier to distinguish a decorative feature from a component that changes security, privacy, fraud, or operational outcomes.
Security teams should ask product owners for evidence, not labels. A response such as “AI-powered” is not enough. Teams should look for model APIs, retrieval-augmented generation layers, automated classifiers, recommendation engines, agent tooling, and vendor services embedded through SDKs or managed endpoints. Where the product uses generative AI, the map should also show prompt handling, content filtering, logging, and any path where outputs can trigger further actions.
- Identify each AI component and its business purpose.
- Record all inputs, especially personal, confidential, and regulated data.
- Note whether the model only informs a user or can trigger downstream action.
- Assign an accountable owner for the model, prompt layer, and operational controls.
- Document third-party dependencies, hosting location, and retention settings.
For governance, the inventory should connect to change management so new models, prompts, or agent tools cannot appear without review. It should also connect to detection and logging so teams can tell when an AI path is invoked, what data it accessed, and whether it produced a high-impact response. In many environments, the most useful control is not a blanket ban but clear classification: customer-facing AI, internal decision support, or autonomous action with tool access. That classification helps security, legal, privacy, and operations speak the same language.
These controls tend to break down when AI functionality is delivered through unmanaged third-party plugins or shadow-IT automation because the product owner may not see every model call or data exchange.
Common Variations and Edge Cases
Tighter AI disclosure often increases review overhead, requiring organisations to balance transparency against the speed of product delivery. That tradeoff is especially real where AI is embedded in many microservices or where teams reuse shared model gateways across multiple products.
Not every AI capability needs the same level of disclosure. A spelling assistant, a search ranking model, and an agent that can execute transactions present very different risk profiles. Current guidance suggests focusing first on functions that affect user trust, sensitive data, regulated decisions, or external commitments. There is no universal standard for exactly how much detail must appear in a public answer, but the internal answer should always be specific enough for audit, incident response, and accountability.
Edge cases also matter. If the product uses vendor-hosted AI, the security team still needs to know whether prompts, uploads, or telemetry leave the environment. If the service uses an AI feature only in a limited region or for a subset of users, that scope should be stated clearly. If an autonomous agent can act on behalf of staff or customers, the answer should describe the action boundary, not just the model name. Where AI is used only as a back-office aid with no customer impact, say so plainly, but still record the supporting workflow because that is where governance failures usually begin.
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 CSF 2.0, NIST AI RMF and NIST AI 600-1 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | ID.AM-1 | Asset inventories must include AI components to answer where AI is used. |
| NIST AI RMF | GOVERN | AI governance requires clear ownership and accountability for AI touchpoints. |
| OWASP Agentic AI Top 10 | A01 | Agentic systems can create hidden action paths that must be disclosed. |
| MITRE ATLAS | AML.TA0001 | Threat modeling helps identify AI-specific attack surfaces and misuse paths. |
| NIST AI 600-1 | GenAI profiles emphasize system description, data flows, and output controls. |
Document where GenAI appears, what it processes, and how outputs are governed.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 1, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
