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Personal data discovery software · DPDP Act

Personal data discovery that finds every place it sits, without copying any of it

Read-only scans of your databases, file shares and Microsoft 365 that keep counts and column names, never values. Indian identifiers checked by check digit. A record of processing that stays current, and the gaps on the record, not off it.

The challenge

You cannot protect, retain or erase what you have not found

Data in places nobody listed

Exports on file shares, PAN in an invoice column, Aadhaar quoted in a notes field.

Tools built for another country

Scanners trained on foreign identifiers miss Indian ones, or flag every ten-digit number.

Discovery that copies the data

A scan that ships samples to a vendor's cloud creates the exposure it was meant to find.

A register that goes stale

A one-time data mapping exercise is out of date by the next quarter.

Solution highlights

Data discovery, from every system to a record you can date

Systems register of 24 systems: 17 scanned, 4 declared by their owners and 3 not yet covered, including a legacy CCTV recorder registered as cannot be scanned, with the reason recorded
Step 1 · Register

Every system, and how it is known

Scanned, declared by its owner in plain questions, cannot be scanned with a reason, or not yet covered. Before a scan, the credential's grants are read and any write privilege is reported.

Metadata-only findings for an employee table: PAN and GSTIN columns with valid checksums, Aadhaar found in a free-text remarks field by Verhoeff check, names found by a local name model, and a health column identified by its name
Step 2 · Classify

Indian identifiers, checked before they are counted

Aadhaar by Verhoeff check, PAN, GSTIN, voter ID, passport, IFSC, UPI, ABHA, UAN and cards. Names by a model trained on Indian names that runs inside the platform. Found in free text too. Evidence is a sentence and a count, not a score.

Record of processing as at a chosen date, listing each dataset with whose data it is, purpose and basis, retention and who it is shared with; the published version carries a SHA-256 hash and can be viewed as at any past date
Step 3 · Record

A record of processing you can date

Whose data, purpose and basis, retention and sharing for every dataset. Versions carry a content hash and can be viewed as at any past date. Exports to Excel and Word.

Removal instructions comparing what each owner says with what the source says: an old marketing export marked done by its owner is still present at the source with 18,402 rows, and is reopened with the owner
Step 4 · Remove

Erasure verified at the source

Retention counted in rows past the period. Removal instructed to the owner, then checked by reading the source again; the owner's claim and the source's answer are recorded apart.

Processor register tiering vendors by the personal data they can reach, with a payroll provider rated high and its contract missing deletion-on-exit and breach-notice clauses
Step 5 · Processors

Vendors tiered by what they can reach

Every vendor, the personal data it can reach and its contract reviewed clause by clause. Assessments go as a link and are answered without an account.

Locating one person by a hashed mobile number: matching row counts in the customer database, a spreadsheet on the finance share and the HRMS
Rights

Where one person's data is, in minutes

For a rights request or a breach, matching rows are counted in each connected source. The identifier is used in the query and stored only as a keyed hash.

Incident record with clocks from awareness: the CERT-In report done inside six hours, intimation to the Board and affected persons drafted, the detailed Board report due within 72 hours, and 2,140 people counted from the exposed copy
Incidents

The clocks start at awareness

CERT-In's six-hour report and the Board's clocks run from one timestamp. The scope comes from scanning the exposed copy, so the number of people affected is counted, not estimated.

Sources

What discovery reads

SourceHow it is read
PostgreSQL, MySQL, SQL Server, Oracle, SAP HANARead-only session; catalogue, samples and counts, paced to the source's latency
Snowflake, Databricks, BigQuery, MongoDBRead-only service account
Windows and NFS file sharesCSV, TSV, Excel, PDF, Word and scanned images
Microsoft 365 and Google WorkspaceSharePoint, OneDrive, Google Drive and mailboxes, read-only
Cloud storage and SaaS applicationsS3, Azure Blob, Google Cloud Storage; HRMS, CRM and ticketing APIs
Anything elseDeclared by its owner, or registered as cannot be scanned, with a reason

Read-only

Discovery never writes to a source. Write privileges on the credential are flagged before a scan starts.

Metadata only

Samples are read in memory. Counts, column names and categories are kept. No value is stored.

Inside your infrastructure

No outbound calls except to endpoints you configure; keys from your key store.

FAQ

Data discovery, answered

Does the scanner copy our data?

No. Samples are read in memory inside your infrastructure. Only counts, column names and categories are kept.

Do we need discovery if we only have a few systems?

Often not. If personal data sits in a handful of systems you already know, a structured register may be enough. Discovery earns its place in larger and older estates.

How does it avoid false positives on Indian identifiers?

Identifiers are checked by format and, where the scheme has one, by check digit, such as Verhoeff for Aadhaar, so an invoice reference that looks like a PAN or an Aadhaar number is not counted as one.

What happens to systems it cannot scan?

They are registered with an owner, the reason, and the owner's answers to plain questions, so the record shows where its edges are.

Readiness check

Twenty questions. Ten minutes. Your exposure, by obligation.

No sign-up. Your answers stay in your browser. You see where liability sits and what would reduce it, including where software is not the answer.

See your own estate, read-only, in a day.

Wekalp is software for data fiduciaries and is not a Consent Manager registered with the Data Protection Board under section 6(9) of the Act. Nothing on this site is legal advice.