Financial Data Under DPDP: What's Covered and How to Protect It

Financial Data Categories Under DPDP

DPDP covers financial personal data broadly: bank account numbers, credit/debit card numbers, investment portfolio details, loan amounts and status, salary and income information, tax payment records, and transaction histories.

Note: financial data is also covered by sector-specific regulations. RBI guidelines for banks, SEBI guidelines for securities, IRDAI for insurance — DPDP operates in addition to, not instead of, these sector regulations. The more restrictive requirement applies.

Credit/Debit Card Numbers

Format: 13-19 digits validated by the Luhn algorithm. Major Indian issuers: Visa (starts with 4), Mastercard (starts with 51-55 or 2221-2720), RuPay (starts with 60, 6521, 6522, 65), American Express (starts with 34, 37).

Detection: extract 13-19 digit sequences, apply Luhn validation. Context filtering: card numbers in support tickets usually appear near keywords 'card', 'payment', 'transaction'. PCI-DSS requirements apply in addition to DPDP — card data must be tokenised before storage and never logged in plaintext.

Investment and Demat Data

Demat account numbers (DP ID + Client ID, 16 digits), folio numbers for mutual funds, trading account numbers. These are financial identifiers that, combined with PAN, allow full financial profile reconstruction.

In AI contexts: financial advisors using LLMs to analyse portfolios may inadvertently send demat account details to third-party APIs. Gateway-level redaction must catch these identifiers. Detection heuristics: 16-digit sequences adjacent to 'DP ID', 'demat', 'CDSL', 'NSDL' keywords.

Data Types operational checklist

Financial Data Under DPDP: What's Covered and How to Protect It 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.