They should keep it out of elicitation entirely and route it through a separate secure flow. Runtime context requests are appropriate for non-sensitive values such as locale or timezone, but not for credentials, personal data, or anything that would create a disclosure or abuse risk if exposed in-session.
Keep Sensitive Inputs Out of Runtime Elicitation
When a live AI workflow asks for extra context, the safest default is to treat the request as a data-minimisation problem, not a convenience problem. If the workflow does not need the data to complete the immediate task, do not let it ask for it in-session. That boundary matters because once sensitive values enter the conversational surface, they are much easier to log, replay, expose to the wrong tool, or retain longer than intended.
Runtime collection is reasonable for low-risk context, such as locale, timezone, preferred units, or other non-sensitive preferences. It is not a good place for credentials, personal data, payment details, or any value that would create disclosure or abuse risk if it were surfaced to the model or operator path.
A separate secure flow is the right pattern because it keeps sensitive handling in a controlled channel with stronger access rules, purpose limitation, and auditability. EU General Data Protection Regulation (GDPR) and NIST Privacy Framework both reinforce the same practical idea: collect only what is necessary, in the right place, for the right purpose.
What Counts as Sensitive in a Live Workflow
The key judgment is whether the requested value would materially increase exposure if the AI session, downstream tooling, or supporting logs were compromised. Credentials and tokens are obvious examples, but the category is broader: personal identifiers, account recovery data, private business records, and any attribute that could enable impersonation, targeted abuse, or unauthorized access should stay out of elicitation.
Teams sometimes over-focus on whether the model is “trusted” and under-focus on the surrounding workflow. Even if the model itself is well behaved, the conversation may traverse connectors, observability systems, analytics, human review queues, or vendor-hosted components. Sensitive data becomes risky when it crosses those boundaries unnecessarily.
That is why separating sensitive capture from conversational context is not just a privacy preference, it is a control design choice. The secure flow should be the only place where the sensitive value is entered, validated, and stored, while the AI workflow receives only the minimum non-sensitive result it needs to continue.
Build the Hand-off So the Model Never Sees the Secret
Good handling means the AI workflow can request an outcome, but not the raw sensitive input itself. For example, a user can authenticate in a dedicated flow and return a simple status or tokenized result to the AI experience, rather than pasting secrets into the prompt. That pattern reduces disclosure risk and limits how much the workflow can accidentally retain.
When teams design the boundary well, they preserve usefulness without expanding trust. The AI can still continue the task, but it works from a safe response object, a confirmed state, or a limited assertion rather than the original sensitive content. In practice, this is the difference between “collect data in chat” and “collect data through a controlled control plane.”
For workflow designers, the strongest test is simple: if the information would be painful to see in a helpdesk transcript, browser history, ticket export, or model trace, it should not be gathered through elicitation. The secure path should absorb that burden instead.
Risk and Threat Considerations
Sensitive data requested in-session can be exposed through logs, prompt history, connector misrouting, retention systems, or later reuse by people and tools that never needed access. Once that data enters the conversational path, the blast radius is usually wider than teams assume.
Failure mechanism: The workflow captures a value that was unnecessary for runtime reasoning, then propagates it into traces, handoffs, or connected systems where access controls and retention are weaker than the original business need.
Impact: Disclosure, replay, unauthorized access, account takeover, or downstream misuse can follow, especially if the captured value can authenticate, identify, or profile a person or system.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF and NIST SP 800-53 Rev 5 set the technical controls, while GDPR and ISO/IEC 27001:2022 define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| GDPR | Art.5 — Principles relating to processing of personal data | Sensitive in-session collection must follow data minimisation and purpose limitation. |
| Art.25 — Data protection by design and by default | The answer is about designing the workflow so sensitive data never enters elicitation. | |
| Art.32 — Security of processing | Secure handling and reduced exposure are central to the recommended control boundary. | |
| Recommendation — Minimise collection and route sensitive data through a purpose-limited secure flow. Design the workflow so sensitive data is excluded from chat-based collection by default. Apply appropriate technical and organisational controls to protect sensitive data in transit and storage. | ||
| NIST AI RMF | GOV — Govern | The question is about governing AI data handling decisions and safe workflow boundaries. |
| Recommendation — Define policy for what sensitive data the AI workflow may request and where it must be routed. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Sensitive data should only flow where needed, limiting who and what can access it. |
| IA-5 — Authenticator Management | Credentials and similar secrets must not be collected through conversational elicitation. | |
| Recommendation — Limit access paths so the AI workflow only receives the minimum data required. Keep authenticators and other secrets out of the AI chat path and manage them in a secure channel. | ||
| ISO/IEC 27001:2022 | A.5.12 — Classification of information | The answer depends on distinguishing sensitive values from harmless runtime context. |
| A.8.12 — Data leakage prevention | The control goal is to prevent sensitive values from leaking through the live workflow. | |
| Recommendation — Classify inputs so sensitive data is excluded from AI elicitation and handled separately. Apply leakage prevention so sensitive inputs cannot be exposed through prompts, logs, or traces. | ||
Practitioner Guidance
Decision rule: If the value could be used to impersonate someone, unlock access, or reveal protected personal or business information, do not let the live AI workflow request it directly. Route the user to a separate secure capture step and pass back only the minimum non-sensitive result the workflow needs.
What to verify: Check that the AI path receives no raw sensitive payloads, that logs and transcripts cannot reconstruct them, and that any secure hand-off is limited to the smallest possible outcome object. The control is working only when the model can complete its job without ever seeing the sensitive source data.
Practitioner takeaway: The safest AI workflow is one that can ask for context without ever becoming a collector of sensitive inputs; if the data matters enough to protect, it matters enough to move out of elicitation.
Related resources from NHI Mgmt Group
- How should security teams handle risks from AI browser extensions?
- How should security teams handle AI interactions that can expose sensitive data in real time?
- How should security teams handle sensitive data in enterprise AI chats?
- What should IAM teams ask about AI products that handle sensitive data?
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Reviewed and updated by the NHIMG editorial team on October 8, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org