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Do not send raw primary account numbers (PANs), card verification codes, PIN data, or other sensitive authentication data (SAD) to an AI model, and do not let those values enter prompts, traces, application logs, error reports, recordings, analytics, or support exports. Remove payment data before prompt construction and log emission. When an AI workflow needs a payment reference, pass the least revealing alternative—usually a provider token, single-use PAN, last-four display value, or non-sensitive transaction ID—while keeping the mapping and de-tokenization controls outside the model. Preserve who did what, where, and when for auditability, but never use raw card data as your audit trail.
What must stay out of AI systems
Payment data has different protection rules depending on the field. Treating every value as interchangeable is a common design error.
Primary account number (PAN)
The PAN is the card number used to identify the payment account. It is cardholder data (CHD). A system may have a legitimate reason to process it during payment capture, but an AI assistant normally does not need to see it. A support bot can identify a transaction by an internal payment ID; a fraud workflow can use a provider-generated token; a receipt assistant can use a masked value such as the last four digits.
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Sensitive authentication data (SAD)
PCI Security Standards Council (PCI SSC) identifies card verification codes and PIN or PIN-block data as SAD. SAD must not be stored after authorization, even if it is encrypted and even if the environment contains no PAN. A prompt, model context window, trace, recording, exception report, or backup is still a copy if it persists the value. Design the flow so SAD is never captured by those systems.
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Other payment-related fields
Expiration dates, cardholder names, billing addresses, tokens, transaction identifiers, and masked PANs have different risk and scope characteristics. Minimize each field according to the task. A field being less sensitive than PAN does not make it automatically appropriate for a prompt or an indefinitely retained log.
Can you send a card number to an AI model?
Do not send a live PAN or SAD to a general-purpose model, an AI provider API, an agent tool, a retrieval index, or a debugging assistant. PCI SSC’s “AI Principles: Securing the Use of AI in Payment Environments” says to limit sensitive data provided to AI systems and to secure production data trusted to them. The safest value is one that never enters the AI boundary.
Start by asking what the model actually needs:
- Answering a customer question: use a case ID, order ID, or payment-provider transaction reference.
- Displaying a payment method: use a masked representation such as “card ending in 1234,” generated before the prompt is assembled.
- Retrying or charging a payment: invoke a narrowly scoped payment tool that accepts a provider token or one-time reference; do not expose the underlying PAN to the model.
- Fraud or reconciliation analysis: use a stable internal identifier and approved, minimized attributes rather than the account number.
- Data that is not needed: omit it entirely. Redaction is safer than asking a model to ignore a secret that it has already received.
Never assume a provider’s statement that data is “not used for training” means the value is absent from request logs, safety traces, abuse monitoring, backups, support systems, or subprocessors. Your architecture must prevent the value from being transmitted and persisted in the first place.
Map every route before changing the system
Make a data-flow inventory from card entry through authorization and every downstream copy. Include paths that developers often overlook:
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- Hosted payment pages, mobile SDKs, checkout forms, and webhook payloads.
- Request bodies, headers, cookies, and model-context assembly code.
- Agent tools, function arguments, retrieved records, and tool results.
- Application logs, reverse-proxy logs, database audit logs, traces, metrics labels, and profiling output.
- Exception handlers, crash dumps, ticketing integrations, screen recordings, call recordings, and chat transcripts.
- AI-provider traces, evaluation datasets, prompt-management systems, analytics exports, data lakes, and backups.
- Developer terminals, staging environments, test fixtures, vendor support bundles, and exported reports.
For each hop, record which component can see PAN, SAD, a token, or only a non-sensitive reference; whether the value is transmitted; where it is stored; who can retrieve it; and when it is deleted. A value that never reaches a route cannot leak through that route.
Design the least-revealing payment flow
Isolate card capture in a payment-provider interface or service boundary, then pass downstream components only the reference required for the business operation.
| Design choice | What the AI path receives | Main exposure to control | When it fits |
|---|---|---|---|
| Application or agent handles raw PAN | PAN, and possibly SAD | Prompts, tools, logs, traces, errors, recordings, and every downstream vendor can copy the values | Generally avoid for AI-assisted workflows |
| Provider-managed capture with payment token | Token or transaction reference | Token vault access, de-tokenization permissions, request logs, and correlation of the token to a customer | When a workflow needs a reusable payment reference |
| Single-use payment reference | One-time token or single-use PAN, where supported | Replay, expiry, authorization boundaries, and accidental logging | One operation such as a specific authorization or payment update |
| Masked display value | Last four digits or another precomputed display string | Over-sharing identity or account context; accidental joining with other records | Receipts, support explanations, and user-facing confirmation |
| Non-payment identifier | Order ID, case ID, or internal transaction ID | Authorization to look up payment details and access-control failures | Most conversational, reporting, and troubleshooting tasks |
PCI SSC’s AI principles identify payment tokens and single-use PANs as potential protection approaches where an AI payment task genuinely requires payment information. That does not mean every tokenization product removes PCI obligations. Scope depends on the vault, the application, the AI provider, logging, de-tokenization controls, and the actual data flow.
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Redact before a prompt or log exists
Downstream masking is not enough. If an unmasked request was written to a file, trace collector, or provider endpoint before a dashboard hid it, the sensitive copy already exists. Suppression must occur at the collection or emission boundary.
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Prompt construction controls
- Define an allow-list of fields. Build prompts from named, approved fields rather than serializing an entire request, user profile, database row, or tool result.
- Normalize and validate input. Treat user text, uploaded documents, retrieved records, and exception messages as possible carriers of card data. Parse and remove PAN-like and SAD fields before templating.
- Use structured objects. Pass a payment reference with an explicit type, such as
transaction_idorsingle_use_token, instead of a free-form blob that might contain hidden fields. - Reject unsafe requests. If a caller attempts to provide a full card number or verification code, stop processing, return a safe error, and record only the event metadata needed for investigation.
- Inspect tool boundaries. Prevent the model from reading raw payment objects through function schemas, retrieval permissions, browser sessions, or shell tools.
Logging and telemetry controls
Configure request and response logging to exclude bodies and sensitive fields by default. Add only the event fields required for operations and audit, such as actor, action, service, location, outcome, correlation ID, and timestamp. Do not rely on a mask applied later in an observability dashboard.
Apply the same policy to:
- HTTP access logs and reverse proxies.
- Database query logging, ORM debugging, and message queues.
- Distributed traces, span attributes, baggage, and metric labels.
- Exception payloads, stack traces, retries, dead-letter queues, and crash dumps.
- AI prompt, response, tool-call, and evaluation logs.
- Customer-support tickets, screenshots, call recordings, and screen-sharing captures.
Use suppression or field allow-lists at the earliest collector. Hashing a PAN is not a universal solution: a repeatable hash can become a durable identifier, may be reversible through guessing, and can still violate a policy that prohibits retaining the value or a usable derivative.
Keep auditability without copying secrets
PCI SSC describes logging as a way to record who did what, where, and when so an event can be investigated. The evidence trail should describe the action, not reproduce the payment credential.
A safe authorization event might contain:
- Human or service actor and the authorization source.
- Action, such as “attempted refund” or “payment token created.”
- Order, case, or transaction identifier.
- Component and environment that performed the action.
- Time, location or network context, result, and reason code.
- Approval or policy decision identifier.
It should not contain PAN, card verification code, PIN data, full magnetic-stripe or chip data, or a prompt transcript that includes them. PCI SSC’s AI guidance says that, where possible, logging should support an audit of the prompt inputs and reasoning process that led to an AI output. Andrew Jamieson, writing for PCI SSC on September 11, 2025, put it this way: “Where possible, logging should be sufficient to audit the prompt inputs and reasoning process used by the AI system that led to the output provided.” Implement that principle with a sanitized prompt record, approved field names, policy decisions, tool-call metadata, and a link to the relevant transaction—not with a raw secret.
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PCI SSC’s FAQ 1081 (December 2022) says an appropriate combination of operating-system, database, and application logging can meet logging needs; it does not require redundant copies when another configured source already records the required access events. Choose one trustworthy source for each event and protect it from alteration.
Handle sensitive authentication data as a special case
SAD has a stricter rule than ordinary CHD: it cannot be stored after authorization, even in encrypted form and even where no PAN is present. Preventing persistence is therefore more important than adding another encryption layer.
Voice and call recordings
If a payment interaction can be recorded, disable recording during the portion where a customer might state a verification code or PIN. PCI SSC FAQ 1210 (June 2025) says every effort should be made to prevent recording SAD and that suppression or redaction technology should be enabled where available. If prohibited data was recorded, restrict access and securely delete it promptly after authorization where feasible. Check transcription, speech analytics, agent-assist prompts, and backup copies as well as the original audio.
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Prompts and traces
Do not include SAD in a prompt “temporarily” and depend on a short retention period. The model provider, gateway, trace collector, error handler, or retry queue may create a copy before your deletion job runs. Reject the field before transmission and ensure the rejection path itself does not log the rejected value.
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Retention and secure deletion
PCI DSS does not set one universal minimum or maximum period for retaining cardholder data. PCI SSC FAQ 1318 (updated July 2025) directs organizations to document legitimate legal, regulatory, and business needs, retain data only as long as necessary, and securely delete it or render it unrecoverable when it is no longer needed.
Apply that decision to every copy:
- Prompt stores, conversation histories, and model-evaluation datasets.
- Application and platform logs, traces, queues, and error databases.
- Support exports, analytics tables, data warehouses, and developer downloads.
- Snapshots, replicated storage, offline media, and vendor-held backups.
Define retention by data class and purpose. SAD should have no post-authorization retention. For other CHD, document the owner, legal or operational reason, maximum period, deletion method, and evidence that the deletion reached downstream systems. “Expired” or “hidden” is not the same as securely deleted if the raw value remains recoverable in an index, snapshot, or backup.
A practical implementation sequence
- Inventory the flow. Trace card entry, authorization, prompt assembly, model calls, tools, logs, observability, exports, backups, and support access.
- Classify fields. Mark PAN, SAD, other CHD, tokens, masked values, and non-sensitive identifiers. Record whether each field is needed at every step.
- Choose the smallest reference. Prefer a transaction or case ID; use a masked value, token, or single-use reference only when the operation requires it.
- Isolate capture. Keep raw PAN and SAD inside the narrowly defined payment boundary. Deny model, analytics, and support components access to that boundary by default.
- Sanitize before serialization. Validate and filter user input, records, tool output, and exceptions before constructing a prompt or emitting an event.
- Configure fail-closed logging. Omit request bodies and sensitive fields by default, then allow-list the metadata required for operations and audit.
- Control tools and permissions. Give the model only the payment operation it needs, with constrained parameters, short-lived credentials, and explicit human approval for consequential actions.
- Prevent recordings. Pause audio or screen capture during payment entry and enable suppression or redaction technology where available.
- Set retention rules. Assign deletion deadlines to prompts, traces, logs, exports, and backups. Prohibit post-authorization SAD storage.
- Test the real path. Use synthetic card-like values, invoke normal and error flows, and inspect the actual stored prompts, logs, traces, exceptions, exports, and deletion results. This is implementation practice, not a claim that PCI SSC prescribes one exact test script.
- Monitor continuously. Alert on PAN-like or SAD-like patterns at ingestion boundaries, investigate policy violations, and review changes to model providers, tools, and logging configurations.
What to do if card data has already leaked into a prompt or log
- Stop further propagation. Disable the affected prompt, tool, logger, export, or recording path and prevent retries from copying the value.
- Preserve safe evidence. Record actor, action, systems, timestamps, and policy configuration without duplicating the card data in a new ticket or chat.
- Determine the data class. Identify whether PAN, SAD, or another field was exposed, whether authorization occurred, and which providers or backups received it.
- Restrict access. Apply the narrowest emergency permissions to affected logs, traces, conversations, and support artifacts.
- Delete or render unrecoverable. Remove prohibited SAD promptly after authorization where feasible and follow the documented deletion process for other CHD, including downstream copies.
- Escalate appropriately. Consult your acquirer, payment brands, incident-response team, and qualified assessor about notification, investigation, and scope obligations.
- Fix the boundary. Add a regression test using synthetic values, verify raw storage is absent, and require review before re-enabling the workflow.
PCI DSS scope and accountability
PCI DSS is relevant to entities that store, process, or transmit CHD or SAD, and to entities able to affect the security of the cardholder-data environment. A tokenized design can reduce exposure, but it does not automatically remove every obligation. The token vault, de-tokenization service, application, AI provider, logs, support access, and network controls determine the actual scope.
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Quick Recap
Deployment checklist
- Raw PAN and SAD are blocked from prompts, model context, tool arguments, traces, logs, errors, recordings, analytics, and support exports.
- Prompt inputs and log fields are allow-listed; full request bodies are not captured by default.
- Payment capture is isolated, and downstream services receive only the least revealing reference needed.
- Token and de-tokenization requests are logged without PAN while preserving an accurate activity record.
- Human ownership, approval, and the AI decision context are auditable without retaining secrets.
- Voice and screen capture are paused or redacted during payment entry.
- SAD has no post-authorization storage, including in backups and derived datasets.
- Retention, deletion, access, monitoring, and incident-response responsibilities are documented and tested.
- Architecture-specific scope questions have been reviewed with the acquirer or a qualified assessor.
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