Privacy that protects accounts.

Clarip's Automated Data Mapping scans on-prem databases, mainframes, data lakes, and cloud vendors continuously, classifying nonpublic personal information, payment data, and other regulated elements. The live inventory feeds retention, minimization, and risk-tiering programs without relying on stale survey-based mapping.

Clarip's Universal Consent Management Platform unifies marketing consent, communications preferences, and opt-outs across retail banking, wealth, cards, and insurance lines into one customer record. Branch, contact-center, and digital channels share the same source of truth, eliminating the cross-line consent leakage that drives complaints and enforcement.

Banks increasingly expose customer data to fintechs, screen-scrapers, and API aggregators — each one a potential breach and regulatory exposure point. Clarip inventories every third party touching consumer financial data, maps exactly which data elements flow to whom under what legal basis, and enforces consent and purpose limitations at the integration layer. When an aggregator's authorization is revoked or a vendor relationship ends, Clarip triggers downstream deletion and produces the evidence state privacy regulators expect during exams.

Relationship managers, marketers, and operations teams are pasting customer information into ChatGPT, Copilot, and internal LLMs faster than risk teams can write policy. Clarip discovers shadow AI usage across the bank, classifies which prompts and outputs contain NPI or confidential customer data, and enforces guardrails that block, redact, or route sensitive interactions to approved enterprise models. Every employee–AI interaction involving customer data is logged with user, purpose, and data category — giving CISOs and compliance the audit trail needed for GLBA Safeguards, FFIEC, and emerging state AI disclosure laws.

Clarip enforces purpose-limitation and consent checks on the data feeding RAG corpora and prompts, logs every interaction for audit, and flags generative outputs that touch regulated data classes. Banks can deploy copilots and customer assistants without losing visibility into what data the models see, what they say, and why.