UPI ID Detection and Validation for DPDP Compliance

UPI ID Format and Variants

UPI Virtual Payment Address (VPA) format: localpart@upihandle. The local part can be: mobile number (9876543210@paytm), name-based (rahulkumar@upi), bank account number, custom vanity address. UPI handles are registered by banks and payment apps with NPCI.

Major UPI handles as of 2026: @paytm, @upi (NPCI), @okicici, @okhdfcbank, @oksbi, @okaxis, @ybl (PhonePe), @ibl (PhonePe), @apl (Amazon Pay), @abfspay (Aditya Birla), @axisbank, @citi, @kotak, @indus, @sib, @timecosmos, @freecharge, @airtelpaymentsbank.

Detection Strategy

Two-pass detection: (1) Extract all @-containing tokens from text, filter by known UPI handle list for high-precision matches. (2) Apply heuristic validation: local part matches phone number pattern OR contains only alphanumeric/dot/hyphen characters, AND handle matches known UPI provider pattern.

Edge cases: @ symbols in email addresses vs. UPI IDs — an email ends with a TLD (gmail.com, company.in) while a UPI handle is a single word (paytm, upi, ybl). Emails never start with a 10-digit mobile number, while UPI IDs often do.

DPDP Compliance for Payment Data

UPI IDs are payment enablers — knowing someone's UPI ID allows sending unsolicited payment requests. Under DPDP, UPI IDs are personal financial data requiring the same protection as bank account numbers. In LLM contexts: always redact before transmission. In support tickets: mask the UPI handle while preserving the last 3 characters for reference (9876XXXXX@paytm → showing the provider helps support without exposing the identifier).

Transaction IDs (UPI Ref No.) are not personal data by themselves but can be used to retrieve personal data from payment systems — treat them with care in shared contexts.

Data Types operational checklist

UPI ID Detection and Validation for DPDP Compliance should be reviewed as an operating control, not only as a reference article. The minimum checklist is a data inventory, a stated processing purpose, owner approval, PII detection at the AI boundary, redaction or tokenisation where possible, retention limits, vendor transfer records, and a tested user-rights workflow. This checklist gives engineering and compliance teams a shared language for deciding what must be blocked, what can be allowed in shadow mode, and what needs human review before production release.

For AI systems, the review should include prompts, retrieved context, tool call arguments, model responses, logs, traces, analytics events, exports, and support attachments. Many incidents happen because teams scan only the visible form field while sensitive data moves through background context or observability tooling. CrewCheck's recommended pattern is to place the scanner at the request boundary, record the policy version, and keep audit evidence that shows which identifiers were detected and what action was taken.

A practical rollout starts with representative samples from production-like traffic. Run a DPDP scan, sort findings by identifier sensitivity and blast radius, fix Aadhaar, PAN, financial, health, children's, and precise-location exposure first, then move to consent wording, retention, deletion, and vendor review. Use shadow mode when false positives could disrupt users, and promote to enforcement only after the exceptions have owners and expiry dates.

This page is educational and should be paired with legal review for final policy interpretation. The operational proof should still come from repeatable evidence: scanner results, audit exports, pull-request checks, policy configuration, and a documented owner for the workflow. That combination is what makes the content useful during buyer diligence, board review, regulatory questions, or an incident investigation.