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PR.DS-1 / A.8.12 / CIS-3.12 NIST Privacy Framework

Discovery tool scans structured + unstructured data

Demonstrate that automated discovery tools comprehensively scan and accurately classify sensitive data across all structured and unstructured data repositories within the organization's defined scope.

Description

What this control does

Data discovery tools are deployed to automatically scan both structured data repositories (databases, data warehouses) and unstructured data stores (file shares, cloud storage buckets, email archives, collaboration platforms) to identify, classify, and inventory sensitive information such as PII, PHI, PCI, and intellectual property. These tools use pattern matching, machine learning classifiers, and content inspection to locate regulated or confidential data across the enterprise, enabling data governance, access control refinement, and breach risk reduction. Discovery scans typically run on schedules or continuously, producing inventories that feed into data loss prevention (DLP), encryption, and retention policies.

Control objective

What auditing this proves

Demonstrate that automated discovery tools comprehensively scan and accurately classify sensitive data across all structured and unstructured data repositories within the organization's defined scope.

Associated risks

Risks this control addresses

  • Unidentified sensitive data in unstructured repositories remains unprotected and exposed to unauthorized access or exfiltration
  • Shadow IT or undocumented data stores containing regulated information evade security controls and compliance monitoring
  • Incomplete discovery results in inaccurate data flow mapping, undermining privacy impact assessments and breach response readiness
  • Sensitive data proliferates into non-production environments or unauthorized cloud storage without detection
  • Failure to discover and classify intellectual property enables insider threats and corporate espionage
  • Non-compliance with data residency and sovereignty requirements due to undetected cross-border data transfers
  • Discovery tool blind spots allow attackers to locate and exfiltrate high-value data assets during breach reconnaissance

Testing procedure

How an auditor verifies this control

  1. Obtain and review the data discovery tool's configuration documentation, including scan scope definitions, exclusions, data source connectors, and classification taxonomies.
  2. Inventory all structured data sources (databases, data warehouses, SaaS applications) and unstructured data repositories (file shares, SharePoint, OneDrive, S3 buckets, email systems) within organizational scope.
  3. Compare the discovery tool's configured scan targets against the complete inventory to identify any data repositories excluded from scanning.
  4. Select a representative sample of at least 5 structured and 5 unstructured data sources spanning different platforms and business units.
  5. Review the most recent discovery scan reports for the sampled data sources, verifying scan completion timestamps, coverage statistics, and classification results.
  6. Conduct spot-checks by manually inspecting a subset of files and database records flagged by the tool to validate classification accuracy and false-positive rates.
  7. Test discovery tool effectiveness by placing synthetic sensitive data samples (test PII, mock credit card numbers) in monitored repositories and verifying detection within the next scheduled scan cycle.
  8. Review discovery tool alerting and reporting workflows to confirm findings are routed to appropriate data owners, security teams, and compliance stakeholders for remediation.
Evidence required Configuration exports from the data discovery platform showing scan schedules, data source connections, classification rules, and sensitivity labels. Discovery scan reports from the most recent cycle including repository coverage maps, data classification summaries, sensitivity heatmaps, and exception logs. Screenshots or audit logs demonstrating successful detection of synthetic test data samples, along with remediation tickets or workflow records showing how discovery findings are escalated and addressed.
Pass criteria All in-scope structured and unstructured data repositories are configured for automated discovery scanning, scans execute successfully on defined schedules, classification accuracy meets or exceeds 90% based on spot-check validation, and synthetic test data is detected within one scan cycle.

Where this control is tested

Audit programs including this control