Discover and Classify Your Data

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Potential consequences of not rolling out a data classification program

  • Inconsistent labeling and categorization can lead to confusion and ineffective enforcement of security measures.
  • Failure to align classification policies with evolving data protection laws (GDPR, CCPA, HIPAA) can expose organizations to legal and financial risks.
  • Without clear data ownership, organizations may struggle to enforce consistent classification and access controls.
  • Without classification, organizations struggle with data sprawl, making it harder to locate and manage critical information efficiently.
  • Lack of classification prevents effective DLP enforcement, increasing the risk of accidental data exposure or leaks.

Benefits of setting up a data classification program:

  • Reduces the risk of fines, legal action, and reputational damage from non-compliance.
  • Creates clear ownership, accountability, and visibility over data assets, improving governance.
  • Enables faster identification and containment of security incidents.
  • Enables better enforcement of encryption, access controls, and data loss prevention (DLP) policies.
  • Demonstrates a commitment to data privacy and security, strengthening customer, investor, and partner confidence.

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Member Rating

9.4/10
Overall Impact

$127,620
Average $ Saved

47
Average Days Saved

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Module 1: Formalize the classification program

The Purpose

  • Establish a structured and governance-driven approach to data classification by defining policies, standards, and responsibilities.

Key Benefits Achieved

  • Gain a formalized data classification program with clear policies, governance, and compliance alignment. Laying the foundation for scalable and effective data classification, to ensure information is properly managed and secured.

Activities: Outputs:
1.1 Develop a steering committee charter to provide oversight and leadership.
  • A structured governance body to guide decision-making and ensure accountability.
1.2 Identify relevant regulations to align classification policies with legal and industry standards.
  • A compliance-driven classification framework that reduces regulatory risks and enhances audit readiness.
1.3 Create a data classification policy to define how data is categorized and managed.
  • Formalized definitions and requirements for operationalizing a data classification program.
1.4 Assign roles and responsibilities to stakeholders involved in classification efforts.
  • Clearly defined RACI chart, ensuring effective enforcement and ongoing management of classification initiatives.
1.5 Develop a classification standard with handling requirements across classification levels.
  • Consistent guidelines for data access, storage, retention, and protection, minimizing risks of misclassification or unauthorized access.

Module 2: Discover the data

The Purpose

  • Develop a structured approach to data discovery by leveraging both technology and stakeholder insights.

Key Benefits Achieved

  • Create a clear methodology for discovering and understanding the organization's data assets. This ensures that future classification efforts are accurate, efficient, and aligned with business needs.

Activities: Outputs:
2.1 Consider data governance and DLP solutions for automated and technology-assisted data discovery.
  • Clear understanding of key attributes to evaluate when selecting a tool, ensuring the right fit for the organization’s needs.
2.2 Conduct interviews with data owners to gather business context and validate technical findings.
  • A systematic approach for conducting data discovery interviews, ensuring alignment between business and IT perspectives.

Module 3: Classify the data

The Purpose

  • Classify data based on sensitivity, regulatory requirements, and business value while ensuring accuracy and consistency in classification levels.

Key Benefits Achieved

  • Obtain insights from classified data to enable better risk mitigation, data management, and policy enforcement across the enterprise.

Activities: Outputs:
3.1 Assign classification labels according to predefined policies and sensitivity levels.
  • A centralized and organized data inventory, ensuring that data is categorized consistently across the organization.
3.2 Use classification insights to streamline storage, retention, and archiving strategies.
  • Improve efficiency by potentially eliminating redundant or obsolete data, while prioritizing protection for high-value assets.

Module 4: Plan to implement the program

The Purpose

  • Operationalize and sustain the data classification program through ongoing measurement, user adoption, and continuous improvement.

Key Benefits Achieved

  • Proactively ensure long-term sustainability, compliance, and effectiveness of the data classification program.

Activities: Outputs:
4.1 Prioritize and track meaningful metrics using the Data Classification Metrics Tracking Tool.
  • A list of key performance indicators (KPIs) to measure classification success, compliance adherence, and security improvements
4.2 Develop a plan for end-user awareness and training to ensure employees understand classification policies and responsibilities
  • Improved user engagement and accountability, reducing misclassification risks and strengthening compliance.
4.3 Communicate program status and updates to leadership and key stakeholders through structured reporting.
  • A customized program communication deck, fostering executive buy-in, cross-functional collaboration, and accountability.

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