OPERATIONS

Enterprise PII Auditing & Automated Governance

Find sensitive data hiding in free text before it reaches your analytics.

Enterprises handling millions of customer interactions face massive compliance penalties under GDPR, CCPA, and HIPAA if personally identifiable information is improperly stored in raw analytics fields. Data governance teams can upload un-scrubbed database dumps, customer support tickets, and raw feedback logs into the platform. Sensitive personal information can hide inside customer records, spreadsheets, surveys, support tickets, documents, transcripts, and free-text fields long after teams assume their data is safe.

Steeped AI's data preparation suite uses machine learning to identify likely PII across structured and unstructured data before it enters downstream analysis, while AI topic mapping categorizes the underlying text concepts without exposing sensitive identity data. Teams can review, flag, standardize, or remove risky records while preserving clean, analysis-ready data. Governance officers receive verifiable audit reports proving data hygiene, creating a practical privacy layer between raw enterprise data and analytics.

PII Audit Citations

¹ 9% of "support tickets" held "unredacted PII"

² 34% of "free-text fields" carried "contact data"

³ 27% of "legacy dumps" missed a "retention tag"

⁴ 22% of "survey open-ends" named a "real person"

⁵ 18% of "transcripts" included an "account number"

⁶ 12% of "exports" left a "region" unrestricted

⁷ 6% of "records" were "flagged for deletion"

Data Preparation & ML PII Detection

Use Steeped AI's data preparation to deploy specialized machine learning models that automatically detect, redact, and clean sensitive PII across millions of text rows. Every flagged record surfaces for review before removal, so governance teams stay in control of what leaves the dataset.

Flagged 8,900 rows with likely PII for your review

Emails and phone numbers in notes were redacted

Account numbers were masked but kept joinable

Removed column "raw_notes_bak" because 79% were blank

Retention tags were applied to every source table

generating clean dataset

SAFE DATA

Long Text or ID Examples

Trace any governance finding down to the raw records behind it with long text or ID examples, with the exact sentences that triggered the flag highlighted for you. An auditor sees the actual row and the actual words, not a number they have to take on faith.

Examples

See Examples For

9% of "ticket_body" records contained unredacted PII

"Customer called about a billing error on the March invoice and asked us to reverse the duplicate charge. I confirmed it and issued the refund while they were on the line. Card on file ends 4419, DOB 03/12/1984, so the account was updated during the call."

flag for redaction

Column: ticket_bodyforpii_detected: Contact + DOB

AI Topic Mapping

Use Steeped AI's AI topic mapping to extract overarching business concepts and user sentiment from sanitized text, so analytical value is retained without exposing private individual details. The insight survives the redaction, which is the whole point of a privacy layer.

Org-wide Insight Hunting

With org-wide insight hunting, every metric and page reads in plain language, so legal, security, and analytics explore the same sanitized dataset while each person keeps a private space for their own findings and reports.

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.