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PII

PII (Personally Identifiable Information) is any data that can be used to identify an individual either directly or indirectly. Examples of PII include:

  • Full name
  • Social Security number
  • Email address
  • Phone number
  • Date of birth
  • Passport number
  • Physical address
  • Financial account numbers
  • IP address (in some contexts)

If you’re handling PII, it’s important to follow applicable privacy laws and regulations (e.g., GDPR, CCPA) to protect it and ensure it is used responsibly.

PII Detection in the Enterprise

Once the domain of data security and data privacy teams, increasingly PII detection and mitigation capabilities are being offered as part of a broader unstructured data management solution. Detecting PII in an enterprise is a critical step in maintaining data security, compliance, and data governance. Here’s are some of the ways enterprises handle PII detection today.

Identify PII Sources

  • Data Repositories: Databases, file servers, email systems, and cloud storage.
  • Data Flows: API interactions, third-party integrations, and data pipelines.
  • Endpoints: User devices, internal systems, and web applications.

Use Automated Tools

A variety of tools and technologies can detect PII across the enterprise:

  • Data Discovery Tools: Tools like Microsoft Purview, Varonis, and Spirion scan structured and unstructured data repositories to find PII. These tools have historically been used to discover PII and other sensitive data but they have often not been part of a broader data management and data mobility strategy.
  • DLP Solutions: Data Loss Prevention (DLP) tools monitor and prevent unauthorized transfer of PII outside the organization.
  • Machine Learning: Advanced systems use natural language processing (NLP) and pattern recognition to identify PII in complex datasets.
  • Cloud-Native Services: AWS Macie, Google DLP, and Azure Information Protection provide PII detection for cloud environments.

What are some common techniques for PII detection?

  • Pattern Matching: Regular expressions to detect common PII formats (e.g., email regex, SSN patterns).
  • Keyword Matching: Identifying sensitive terms associated with PII (e.g., “social security,” “passport number”).
  • Metadata Analysis: Analyzing file names, tags, or attributes.
  • Contextual Analysis: Understanding context to distinguish between sensitive data and non-sensitive similar patterns.
  • AI/ML-Based Detection: Identifying nuanced PII (e.g., names in free text).

Implementing PII detection policies

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As part of a broader data management strategy, it’s important to be able to define policies to classify and handle detected PII:

  • Data Classification: Tagging data as sensitive, restricted, or public.
  • Access Control: Restrict access to PII based on roles and need-to-know principles.
  • Retention Policies: Delete PII when it is no longer needed to minimize exposure risk.

PII Compliance and Data Governance

Ensure PII detection aligns with legal and regulatory requirements:

  • GDPR (EU): Requires organizations to identify and protect personal data.
  • CCPA (California): Mandates disclosures about collected personal data.
  • HIPAA (USA): Enforces protections for health-related PII.

Common PII Data Detection Challenges

Historically PII detection has been narrowly defined and technologies have not been part of a broader unstructured data management strategy. Some of the common PII detection challenges include:

  • Data Volume: Enterprises often have petabytes of data scattered across silos.
  • False Positives: Pattern-based detection can misidentify non-sensitive data as PII.
  • Evolving Data Types: New formats or types of PII may require constant updates to detection mechanisms.
  • Hybrid Environments: Monitoring PII in both on-premises, cloud and edge environments.

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