Personal Data Discovery & Inventory
for Digital Privacy Compliance

Get a clear, real time view of where personal data exists

Where Things Break

Personal data is everywhere… but visibility isn’t.

Personal data sprawls across CRMs, data lakes, logs, analytics tools, and third party integrations. Static audits and manual spreadsheets can’t keep pace  creating blind spots that regulators increasingly penalise.

  • Shadow IT and legacy systems hold undocumented personal data
  • Point in time inventories become stale immediately after completion
  • Incomplete records delay Data Subject Request (DSR) responses
  • GDPR enforcement actions consistently cite poor data inventories

Sigmify GRC Approach

AI driven discovery that keeps pace with your data

1. Comprehensive data discovery

Identifies personal data using pattern recognition and contextual analysis

2. Validation of trusted systems

Confirms data presence in approved environments

3. Risk detection in real time

Flags unexpected or unauthorized data locations

4. Always current inventory

Maintains a continuously updated, audit ready data inventory

5. Built for change

Automatically adapts to new systems, tools, and data sources

Value Delivered

1. Stronger compliance accountability

Aligned with evolving data privacy requirements

2. Faster, more accurate responses

Efficient handling of access and deletion requests

3. Reduced risk exposure

Eliminates unknown or unnecessary data retention

4. Audit confidence

Be fully prepared with clear, evidence backed data insights

Proof

Large telecom and financial services organizations globally have used similar AI based discovery approaches to uncover personal data locations that were missed during years of manual assessments.

Reduction in discovery effort

Significant

Improvement in response timelines

Call to Action

Move from assumptions to evidence

Proof

Proven outcomes across industries

Uncovered data missed during years of manual tracking

Reduced discovery effort by
50%+

Improved response timelines significantly