Sluiceport

Support tickets can arrive with documents, screenshots and voice clips containing health, financial, identity, account and legal-matter information before an AI agent ever sees them. The supplied research confirms production-grade encrypted processing infrastructure, a mature open sensitive-data recognizer and an adjacent document-processing vendor, while finding no reviewed product positioned specifically at inbound support-ticket redaction.

Sluiceport would preserve ticket and attachment identities, channel, collection authority, media digest, extraction or transcription version, detection candidate, category, character or region span, confidence, reviewer decision, transformation, replacement token, sanitized payload version, provenance manifest, quarantine state, release approval and deletion. Raw and sanitized content would live under separate access boundaries.

Automated detection can miss uncommon identifiers, context-dependent health information, secrets embedded in images or voice and combinations that reidentify a person. It can also over-redact useful context. Attorney-client privilege is a legal determination, not a pattern label. A sanitized payload is a reviewer-approved transformation with residual risk, not proof that privacy, confidentiality or regulatory obligations are satisfied.

The initial mode should fail closed: uncertain attachments stay quarantined and never reach the agent. Authorized reviewers need side-by-side access, correction and reason codes. Raw content requires short retention, encryption, tenant isolation, access logging and deletion propagation. The buyer hypothesis is a privacy, security, support-platform or compliance leader at a regulated organization using AI support; data types, channels, volume, false-negative tolerance, review staffing, budget and existing gateway need validation.

A privacy, security, support-platform or compliance leader controlling sensitive inbound media before it reaches AI support systems.