Automated DPDP Data Posture Management

Know your
DPDP exposure
before they do.

ztrust is an Automated DPDP Data Posture Management engine for Indian organizations. It connects to your actual systems — databases, cloud storage, SaaS — scans for personal data automatically, scores your DPDP exposure, and generates auditable proof. No consulting project. No questionnaires.

Audience

Owners and operators in Indian teams with no dedicated privacy function

Detects

Exposure, consent gaps, DPR readiness, access risk, breach blast radius

Boundary

Detection and scoring — not consent management or request workflows

🔍 Automated personal data discovery🛡 DPDP Act 2025 mapped findings📊 Proof Pack — no setup required⚡ Scans in minutes, not weeks🗄 PostgreSQL · MySQL · Google Drive · S3👤 Access actor detection🔒 Metadata only — never row-level data📋 Evidence timeline with timestamps🏦 Aadhaar, PAN, bank details detection⏱ May 13, 2027 enforcement deadline🔍 Automated personal data discovery🛡 DPDP Act 2025 mapped findings📊 Proof Pack — no setup required⚡ Scans in minutes, not weeks🗄 PostgreSQL · MySQL · Google Drive · S3👤 Access actor detection🔒 Metadata only — never row-level data📋 Evidence timeline with timestamps🏦 Aadhaar, PAN, bank details detection⏱ May 13, 2027 enforcement deadline

Platform

One automated loop:
detect, score, prove.

01

Hybrid discovery across real sources

Connect databases, cloud stores, and manual uploads to inventory where personal data lives — without starting with a consulting project.

02

Risk findings tied to DPDP questions

Surface exposure, consent gaps, DPR-readiness signals, and breach blast-radius diagnostics — each with severity and confidence signals.

03

Topology, actions, and proof in one loop

Map assets to employees, customers, and vendors. Turn findings into action items. Export a Proof Pack with timestamps — always current.

Operating flow

Answer the five questions DPDP demands.

What personal data do you hold? Where is it exposed? Is processing lawful? Are you ready to be challenged? What proof do you have?

01

Find what personal data you actually hold

Scan connected systems, identify field types such as Aadhaar number, PAN, bank details, email address, and children’s data, and build a current inventory.

02

Score whether that processing is exposed or weak

Evaluate storage, sharing, retention, legal-basis signals, and challenge readiness across the five core risk categories.

03

Turn findings into a defensible artifact

Give owners the posture summary and give operators the action queue, evidence timeline, and Proof Pack needed for customer review, board conversations, or enterprise procurement.

Proof Pack

The export is the diagnostic story, compressed.

Owners need a binary answer in under thirty seconds. Operators need the finding register, evidence trail, and action queue behind that answer. Share it when a customer questions your data practices, a board asks about DPDP exposure, or an enterprise prospect sends a security questionnaire.

Proof Pack contents

Posture status: Defensible, At Risk, or Improving

Top risk findings in plain English

Connected sources and personal data inventory

Evidence timeline with verification timestamps

Priority actions linked back to the originating findings

Dark Sources

The personal data you haven't inventoried is still your liability.

The average Indian startup holds personal data in 6–8 systems it has never formally inventoried. Keka holds Aadhaar numbers. Razorpay holds bank accounts. Freshdesk support tickets contain health data shared during customer calls. DPDP applies to all of it — whether or not you've connected it to a compliance tool.

ztrust detects which of your connected sources hold personal data and flags the known SaaS sources in your stack that you haven't connected yet. So your inventory reflects reality, not just what's convenient to scan.

Keka HR

Aadhaar, PAN, bank details, biometrics

critical

Razorpay

Bank accounts, UPI IDs, PAN, transaction history

critical

Zoho CRM

Customer contacts, deal data, financial info

high

Freshdesk

Support tickets with health, KYC, financial data

high

Google Drive

Employee contracts, offer letters, Aadhaar copies

high

WhatsApp / Email

Salary slips, identity docs shared informally

medium

These are not hypothetical risks. These are the actual SaaS products most Indian startups are running right now — each holding personal data that DPDP covers.

Compounding evidence

Your posture gets stronger every week.

A Proof Pack generated at month six is categorically more valuable than one generated the week before an investor asks. Evidence accumulates automatically — you just have to start.

1Day 1

First inventory scan

Connect your database or cloud storage. ztrust scans schema metadata and detects personal data fields — Aadhaar, PAN, bank details, email — in under 4 minutes.

4 min avg scan
2Week 4

30 days of continuous evidence

Every scan, every finding, every resolved action is timestamped automatically. You now have a 30-day evidence history — not assembled on request, just there.

30-day trail
3Month 3

Proof Pack with verifiable history

Export a Proof Pack that answers investor and enterprise procurement questions. The evidence timeline shows controls were running for 90 days — not set up the week before someone asked.

Procurement-ready
4Month 6

Enterprise deal unblocked

180 days of continuous signals. Security questionnaire answered in 10 minutes. The posture that took a competitor 6 weeks to assemble takes you one button press.

10 min response

The evidence timeline matters. Enterprise security questionnaires ask for proof that controls were operational for at least 6 months. A company that starts its evidence programme the week before a deal closes will fail that question — regardless of how good its actual controls are. Start now.

Stop guessing where your DPDP exposure is.

Connect the first systems, surface the risk findings, and generate a Proof Pack before the next customer review, board conversation, or remediation sprint.