Detecting and Protecting Children's Personal Data Under DPDP Section 9

Identifying Minor Users

DPDP Section 9 applies to users under 18. Detection approaches: explicit age declaration (date of birth field during registration — validate and flag if under 18), guardian account linking (parent email/mobile required for child accounts), school/educational institution email patterns (student.schoolname.edu.in, schoolname.ac.in).

Indirect signals: users who browse EdTech content, select 'student' as occupation, or provide a school name in profile. These are soft signals — treat as potential minor until age verification confirms otherwise.

Technical Controls for Minor Accounts

Once an account is identified as a minor: (1) Block AI features that involve personal data processing without parental consent, (2) Disable behavioural tracking and personalised advertising, (3) Apply stricter data minimisation — only collect what's essential for the core service, (4) Tag the user in your system as 'minor' so downstream services apply appropriate restrictions.

For AI chatbots accessible to minors: add age-appropriate content filters, restrict data retention for conversations to 30 days, and never use minor users' conversation data for model training.

Data Types operational checklist

Detecting and Protecting Children's Personal Data Under DPDP Section 9 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.