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Payment Processing AI: PCI DSS Compliance Through PII Redaction

Payment-processing AI creates risk in predictable places: customer chats that include full card numbers, refund workflows with order metadata, fraud-review notes that mix personal data with case history, and internal operations assistants that quietly become a second copy of cardholder-data handling. Keeptrusts helps by moving the control point to the gateway. Instead of hoping users redact inputs or trusting the model not to echo sensitive values back, you can require identity with rbac, run pii-detector with pci_mode, block additional secrets with dlp-filter, and restrict provider eligibility with Data Routing Policy.

That matters because PCI DSS problems usually do not begin with a deliberate breach. They begin with convenience. A support agent pastes a PAN into a chat. A merchant-ops analyst asks the assistant to summarize a chargeback thread with unredacted customer details. A dispute bot forwards a payment transcript to an upstream provider that was never approved for cardholder data. The fix is not a longer system prompt. The fix is enforcing the policy chain before the model sees the request.

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Primary audience​

  • Primary: Technical Leaders
  • Secondary: Technical Engineers, AI Agents

The problem​

Payments teams often start with a narrow use case that looks harmless. A support assistant drafts replies about failed authorizations. A fraud analyst uses AI to summarize suspicious checkout behavior. A merchant-success tool explains dispute codes. None of those sound like full card-processing functions, but in practice they attract cardholder data almost immediately because the surrounding workflow already contains it.

The first issue is raw PCI exposure. The current PII Detector supports pci_mode, which means PANs, CVV or CVC values, and expiration dates can be detected before the provider call. That gives operators a real request-boundary control. It is stronger than telling staff to "never paste card numbers" because it assumes they eventually will.

The second issue is everything that is sensitive but not covered by basic PCI matching. Merchant IDs, dispute-case identifiers, live API keys, and internal settlement references are usually organization-specific. They belong in detect_patterns or blocked_terms under DLP Filter, not in a vague internal policy memo.

The third issue is provider selection. Even when content is redacted, some payment teams still need to require zero-data-retention and training opt-out guarantees on the upstream target. That is where Data Routing Policy matters. It does not inspect prompt text; it filters provider targets based on declared data_policy metadata. In other words, it gives you a way to express that payments traffic cannot fall back to a target with a broader retention profile.

It is also useful to be precise about scope. Keeptrusts can help keep card data out of model requests, mark audit logging as active in the chain, and expose decisions through events and exports. It is not itself a card vault, tokenization processor, or complete PCI program. The operational win is reducing how much payment-sensitive text reaches the model layer in the first place.

The solution​

For payment-processing assistants, the safest baseline is a four-part control pattern.

Start with explicit caller identity. rbac makes it harder for a generic shared bot token or anonymous service account to become the entire trust boundary. Require headers such as X-User-ID, X-User-Role, and a workflow identifier so merchant support, chargeback operations, and fraud review do not all collapse into one undifferentiated route.

Next, turn on pii-detector with pci_mode: true. That is the main payment-specific control because it catches PANs, CVV or CVC values, and expiration dates at the request boundary. In action: redact mode, the assistant can still function, but the provider sees sanitized text rather than raw card data.

Then add dlp-filter for operator-specific payment secrets. This is the right place to block live API key formats, merchant identifiers, or internal dispute references that standard PII matching does not know about.

Finally, apply data-routing-policy so only provider targets with the required data_policy declarations remain eligible. This is what turns provider governance into an enforceable runtime condition instead of a procurement note.

Implementation​

This configuration keeps the scope narrow: payment support and dispute workflows that need strong prompt sanitation and no compliant-provider fallback.

pack:
  name: payment-operations-guardrail
  version: 1.0.0
  enabled: true

providers:
  targets:
    - id: payments-zdr
      provider: openai
      model: gpt-5.4-mini-mini
      secret_key_ref:
        env: OPENAI_API_KEY
      data_policy:
        zero_data_retention: true
        training_opt_out: true
        retention_days: 0
        in_memory_only: true
        sanitized: true
        accepts_tokenized_input: true
        allow_internet_egress: false
        local_only_processing: true

  routing:
    strategy: ordered

policies:
  chain:
    - prompt-injection
    - rbac
    - pii-detector
    - dlp-filter
    - data-routing-policy
    - audit-logger

policy:
  prompt-injection:
    use_embedding: false
    detection:
      attack_patterns:
        - "ignore.*previous.*instructions"
        - "reveal.*system.*prompt"
    encoding:
      decode_base64: true
      normalize_unicode: true
      detect_homoglyphs: true
    boundaries:
      enforce_delimiters: true
      reject_fake_boundaries: true

  rbac:
    deny_if_missing:
      - X-User-ID
      - X-User-Role
      - X-Workflow-ID
    require_auth: true

  pii-detector:
    action: redact
    pci_mode: true
    detect_patterns:
      - 'MERCHANT-[0-9]{6}'
      - 'CASE-[0-9]{8}'
    redaction:
      marker_format: label
      include_metadata: true

  dlp-filter:
    detect_patterns:
      - 'sk_live_[A-Za-z0-9]{24,}'
      - 'pk_live_[A-Za-z0-9]{24,}'
    blocked_terms:
      - magnetic stripe data
      - raw track data
    action: block

  data-routing-policy:
    require_zero_data_retention: true
    require_no_training: true
    max_retention_days: 0
    require_in_memory_only: true
    sanitize_before_provider: true
    tokenize_sensitive_fields: true
    allow_internet_egress: false
    local_only_processing: true
    on_no_compliant_provider: block
    log_provider_selection: true

  audit-logger: {}

There are two practical operator notes behind this example. First, pii-detector is the main payment control, but it is not a substitute for your existing card-data architecture. You should still keep vaulting, tokenization, and settlement systems outside the assistant path. Second, data-routing-policy only works when every provider target declares data_policy clearly. Missing metadata is treated as non-compliant, which is usually the right default for payments.

The basic validation loop is short:

kt policy lint --file ./payment-operations-guardrail.yaml
kt gateway run --policy-config ./payment-operations-guardrail.yaml --port 41002
kt events tail --policy pii-detector
kt export create --format json --filter "policy=pii-detector,data-routing-policy,audit-logger"

That gives you a quick test of config validity, live redaction evidence, and an export path you can review with operations and compliance stakeholders.

Results and impact​

The biggest improvement is containment. Payment assistants stop acting like a side door around the card-data handling rules that already exist elsewhere in the organization.

  • Raw PAN and CVV values are less likely to reach the provider because pii-detector handles PCI-like inputs before the model call.
  • Merchant-specific secrets are governed explicitly through dlp-filter instead of depending on staff memory.
  • Provider routing becomes enforceable because Data Routing Policy removes targets that do not meet retention and processing requirements.
  • Event and export workflows give operators a reviewable evidence trail when they need to prove the assistant route was governed.

This also creates a cleaner boundary for future rollout. Once support and dispute assistants are controlled, teams can decide whether to expand into merchant analytics or fraud-review drafting without reopening the basic PCI question every time.

Key takeaways​

  • For payments, start with pii-detector and pci_mode before you optimize prompts or model choice.
  • Use dlp-filter for merchant-specific identifiers and secrets that the built-in detector does not know about.
  • Treat Data Routing Policy as the provider-side companion to content redaction.
  • Use the event and export path to prove the controls are active, not just configured.
  • Keep payment AI scoped to workflows where redaction and routing controls can be validated end to end.

Next steps​