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Contact Centers & Customer Support

PII Redaction for Contact Centers

Speech-to-text transcripts, chat logs, ticket bodies, and agent-assist features all swim in PII. Philter scrubs cardholder data, account numbers, and customer identifiers at the ingestion point — before they land in QA platforms, analytics warehouses, or hosted LLMs.

Or deploy Philter yourself →

The contact-center PII problem

Every contact center has the same architectural problem: the channel where customers say their card number aloud feeds directly into systems where that card number must not land. QA platforms, analytics warehouses, agent-coaching tools, sentiment dashboards, and the new generation of AI-summarization tools all hit recordings or transcripts as a matter of routine workflow.

The result is PCI DSS scope sprawl — ten or twelve systems all touching cardholder data because no one redacted it at the front of the pipeline. The same story repeats with GLBA NPPI, with PHI in healthcare-adjacent call centers, and now with PII landing in LLM prompts for agent assist and call summarization.

How Philterd handles contact center

Pre-ingest transcript redaction

Drop Philter at the speech-to-text or chat-logging step. PANs, CVVs, SSNs, account numbers, and other PII get redacted before transcripts reach QA, CRM, analytics, or coaching systems — collapsing PCI / GLBA scope to a single small system.

Spoken-aloud PAN variants

“Four eight nine three… uh, four eight nine three, two one zero zero…” The call-recording policy handles the messy reality of spoken card numbers, restarted digits, and operator dictation patterns — not just clean 16-digit strings.

LLM-safe agent assist

Live agent-assist features increasingly call hosted LLMs for summarization, sentiment, and next-best-action. Philter AI Proxy redacts PII from the prompt and retrieval context before it hits OpenAI, Anthropic, or Bedrock — no customer-data egress to the model provider.

Works with Genesys, NICE, Verint, Five9, Twilio

Vendor-agnostic by design. The transcript flow uses standard HTTP / Kafka / S3 patterns; Philter sits in the path regardless of the contact-platform vendor.

Luhn-validated PAN detection

Philter validates 13-19 digit sequences against the Luhn checksum before treating them as PANs. Order IDs, tracking numbers, ticket references, and case numbers don’t get falsely redacted — the QA team still sees the operational identifiers they need.

Stays in your VPC

Runs entirely inside your AWS, Azure, or GCP account. No transcripts or recordings sent to a third-party redaction API. The data path never leaves your audit boundary.

Ready-to-use policies

Apache 2.0 policies from the open source policy library — download and load into your Philter instance.

Contact Center v1.0.0

Contact Center Call Recording Transcripts

Strip cardholder data and PII from contact-center call transcripts — primarily PAN, CVV, SSN, account numbers — to reduce PCI DSS scope and meet QA privacy requirements.

PCI DSScontact centercall recordingtranscripts
Finance v1.0.0

PCI DSS Scope Reduction

Strip cardholder data (PAN, CVV, expiration) from logs, transcripts, and tickets to reduce PCI DSS scope per Requirement 3.4.

PCI DSScardholder dataPANscope reduction

Browse all redaction policies →

Recent writing on contact center

Why API-Based Redaction is a Security Antipattern

Sending sensitive data to a third-party redaction API creates the security holes you're trying to close. Here's why true data sovereignty requires a self-hosted engine — and how Philter delivers it.

Using an LLM or Pattern-based Rules for PII/PHI Redaction

In our data-driven world, being able to protect Personally Identifiable Information (PII) and Protected Health Information (PHI) is imperative. Whether you’re securing customer data, complying with regulations like GDPR or HIPAA, or simply aiming for responsible data handling, the need to effectively redact sensitive information is crucial. Today, there are two primary approaches: leveraging the…

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Where contact-center teams start

  1. Inventory the systems touching transcripts. QA platforms, CRMs, analytics warehouses, agent-coaching tools, AI summarization — the list is usually longer than the PCI assessor was told about.
  2. Deploy Philter at the transcript-emission point. Right after the speech-to-text step, right before the bus that fans out to all the downstream systems.
  3. Apply the call-recording policy tuned for your speech-to-text vendor’s output quirks (Whisper, AWS Transcribe, Deepgram, Google STT).
  4. Measure precision and recall against a labeled sample of your real transcripts. Spoken card numbers are messy; the policy needs tuning against your actual data.
  5. De-scope the downstream systems. Document the data flow showing PAN cannot re-enter; bring the QSA in to confirm scope reduction; pocket the audit savings.

Common deployments

1. PCI scope reduction at the QA platform. Call-recording transcripts go from the speech-to-text engine straight into a QA platform for agent evaluation. Spoken PANs land there as plain text. Inserting Philter between STT and QA collapses the QA platform’s PCI scope and usually two or three downstream systems with it — a six-figure annual savings in audit and remediation cost for most mid-sized contact centers.

2. Agent-assist LLM guardrails. Modern agent-assist features call hosted LLMs in real time to summarize active calls, suggest next responses, or score sentiment. Without a redaction step, the customer’s spoken PII flows into the model provider. Philter AI Proxy sits between the agent-assist application and the LLM and strips PII from each prompt before forwarding. The agent experience is unchanged; the data path is now defensible.

3. Analytics-warehouse customer-data scrubbing. Conversation data feeds into the analytics warehouse for trend analysis, churn modeling, and product-feedback aggregation. Redact before the warehouse load so the warehouse stays out of GLBA / NPPI scope.

What teams need to be careful about

  • Recording vs transcript. Recordings are usually retained under separate retention rules and may already be encrypted. Transcripts are the redaction target — that’s where downstream systems consume the spoken content as text. Redact at the transcript step; the underlying recording stays under its own retention policy.
  • DTMF capture for cards. Many contact centers capture card numbers via DTMF tones (the customer types digits on the keypad) precisely to keep PANs out of the recording. Philter doesn’t help with that capture path — it’s already PCI-clean by design. Philter is for the cases where PAN does leak into the spoken/typed channel.
  • Recall on accented and noisy transcripts. Speech-to-text quality varies enormously by accent, line quality, and STT vendor. The same Philter policy will hit different precision and recall depending on the upstream STT. Measure against your specific transcript corpus before declaring scope reduction.

Build PII redaction into your contact center pipeline

PCI scope reduction is one of the few compliance moves that pays for itself in audit fees the same year. Talk to engineers who’ve seen the before-and-after numbers.

Or deploy Philter yourself →