Data Protection & Privacy

Data Classification & Tagging Automation

  • May 9, 2025
  • 0

Sherlocked Security – Data Classification & Tagging Automation

Automate the Classification and Tagging of Sensitive Data to Ensure Compliance and Data Protection

1. Statement of Work (SOW)

Service Name: Data Classification & Tagging Automation
Client Type: Enterprises, Healthcare Providers, Financial Institutions, Government Agencies
Service Model: Project-Based Assessment & Automation Deployment
Compliance Alignment: GDPR, HIPAA, PCI-DSS, NIST 800-53, ISO/IEC 27001, SOC 2

Data Classification & Tagging Automation Covers:

  • Assessment of current data classification and handling processes
  • Implementation of automated classification and tagging workflows
  • Integration with existing data security tools and infrastructure
  • Integration of AI/ML-based classification models for accuracy and scalability
  • Metadata tagging for sensitive data across on-premises, cloud, and hybrid environments
  • Policy enforcement and compliance automation
  • Continuous monitoring and auditing of classified data

2. Our Approach

[Assessment & Discovery] → [Classification Schema Design] → [Automation Tool Selection & Integration] → [Deployment & Testing] → [Compliance Mapping] → [Ongoing Monitoring & Optimization]

3. Methodology

  • Assessment & Discovery:

    • Evaluate current data classification methods, tools, and policies.
    • Identify critical data, compliance needs, and classification gaps across systems.
    • Review existing metadata and file structures for tagging opportunities.

  • Classification Schema Design:

    • Develop a tailored data classification schema based on business needs and regulatory requirements.
    • Design metadata tags to categorize data by sensitivity, risk level, and business value (e.g., PII, PHI, financial data).
    • Define classification rules, including automated detection thresholds.

  • Automation Tool Evaluation & Integration:

    • Evaluate and select automation tools to handle data classification (e.g., Varonis, Microsoft Information Protection, Forcepoint).
    • Integrate classification automation with existing data management, storage, and security infrastructure.
    • Implement AI/ML models to continuously improve classification accuracy over time.

  • Tagging Workflow Design & Deployment:

    • Automate tagging of data across endpoints, servers, cloud environments, and storage solutions.
    • Ensure tagging is applied in real-time during data creation, movement, and access.
    • Implement workflows to notify relevant stakeholders when unclassified or improperly tagged data is identified.

  • Policy Enforcement & Compliance:

    • Develop automated policies to enforce proper classification and tagging based on data sensitivity.
    • Ensure alignment with compliance frameworks such as GDPR, HIPAA, and PCI-DSS.
    • Automate data handling rules for sensitive data, including encryption and access restrictions.

  • Ongoing Monitoring & Reporting:

    • Set up monitoring to ensure tags are consistently applied and enforced.
    • Generate real-time alerts for non-compliance or misclassification incidents.
    • Continuously refine classification models based on audit results and evolving business needs.

4. Deliverables to the Client

  1. Data Classification Assessment Report: A thorough evaluation of the current data classification and tagging processes, including any gaps and inefficiencies.
  2. Classification Schema: A tailored schema outlining the classification categories and metadata tags for sensitive data.
  3. Automation Tool Integration Plan: A detailed plan for selecting, implementing, and integrating automation tools into your infrastructure.
  4. Tagging Workflow Documentation: Step-by-step documentation of the tagging process, including automated workflows for data movement and access.
  5. Compliance Mapping Report: Identification of how the new classification schema aligns with regulatory and industry requirements, with specific focus on compliance gaps.
  6. Automated Reporting Dashboard: A customizable dashboard for real-time monitoring, incident reporting, and auditing of classified data.

5. What We Need from You (Client Requirements)

  • Current Data Classification Policies: Any existing data classification policies or frameworks in use.
  • Access to Data Repositories: Access to key data repositories, including databases, file servers, cloud storage, and email systems.
  • Compliance Documentation: Regulatory and compliance requirements specific to your industry (e.g., GDPR, HIPAA, PCI-DSS).
  • Data Flow Diagrams: Diagrams showing how data moves across your organization’s network, including cloud and hybrid environments.
  • Security Infrastructure Overview: Information about existing data security solutions (e.g., encryption, DLP, access control) and how they interact with classified data.
  • Stakeholder Interviews: Access to data owners, compliance officers, and security teams for clarification on classification needs.

6. Tools & Technology Stack

  • Data Classification & Tagging:
    • Varonis, Microsoft Information Protection, Forcepoint, Digital Guardian
  • Cloud Data Protection:
    • AWS Macie, Google Cloud Data Loss Prevention (DLP), Azure Information Protection
  • Machine Learning & AI-based Classification:
    • Microsoft Cognitive Services, Google Cloud AI, IBM Watson
  • Data Security Integration:
    • Vormetric, McAfee MVISION, Symantec Data Loss Prevention
  • Compliance & Reporting:
    • Vera, Tanium, Splunk, Elastic Stack

7. Engagement Lifecycle

  1. Kickoff & Scoping: Initial assessment, collection of documentation, and setting expectations for the project scope.
  2. Data Discovery & Assessment: Identify sensitive data types and classify data flow across systems.
  3. Classification Schema Design: Tailor a data classification schema based on business needs and regulatory requirements.
  4. Tool Selection & Integration: Evaluate, select, and implement classification and tagging automation tools.
  5. Workflow Automation & Tagging: Deploy automation workflows to tag and classify data in real-time.
  6. Compliance Mapping: Ensure the classification and tagging solution meets compliance requirements (e.g., GDPR, HIPAA).
  7. Ongoing Monitoring & Reporting: Set up real-time monitoring and generate reports for auditing and compliance purposes.
  8. Final Handover & Training: Provide documentation and training to ensure continued success and compliance.

8. Why Sherlocked Security?

Feature Sherlocked Advantage
Tailored Classification Schema Custom classification rules designed to meet business needs and compliance requirements
Automated Tagging Real-time automated tagging workflows for data classification across all systems
AI & ML Integration Incorporating AI/ML for continuous improvement and accuracy of classification
Seamless Integration Full integration with existing security and compliance tools
Compliance-Focused Designed to align with global compliance frameworks such as GDPR, HIPAA, and PCI-DSS

9. Real-World Case Studies

Financial Institution – Automating PCI-DSS Compliance

Client: A global bank required automated data classification to meet PCI-DSS standards.
Findings: Manual classification processes were error-prone, resulting in non-compliant data handling.
Outcome: Deployed automated classification and tagging, ensuring 100% compliance with PCI-DSS and reducing manual effort by 70%.

Healthcare Provider – Securing PHI with Automated Tagging

Client: A regional healthcare provider needed to protect PHI (Protected Health Information) under HIPAA.
Findings: PHI was not adequately classified across cloud and on-premises environments, exposing sensitive patient data.
Outcome: Implemented AI-based classification models and automated tagging across all storage and access points, ensuring HIPAA compliance.

10. SOP – Standard Operating Procedure

  1. Initial Assessment: Review current data classification policies and frameworks.
  2. Data Discovery: Identify and classify sensitive data types across the organization.
  3. Schema Design: Develop a classification schema and metadata tags for data across systems.
  4. Tool Evaluation & Integration: Select automation tools for classification and tagging, ensuring integration with existing infrastructure.
  5. Automated Tagging: Deploy tagging automation and workflows to ensure compliance in real-time.
  6. Compliance Mapping: Ensure the classification aligns with relevant regulations and internal policies.
  7. Ongoing Monitoring: Implement continuous monitoring and reporting to track classification effectiveness.
  8. Documentation & Training: Provide documentation and training for internal stakeholders to ensure continued compliance.

11. Data Classification & Tagging Readiness Checklist

1. Pre-Assessment Preparation

  • [ ] Current data classification policies and frameworks
  • [ ] Access to key data repositories (on-premises, cloud, hybrid)
  • [ ] Regulatory and compliance documentation
  • [ ] Data flow diagrams and architecture overviews
  • [ ] Inventory of data security tools in use

2. During Engagement

  • [ ] Review and assess current data classification practices
  • [ ] Tailor classification schema based on business needs and regulatory requirements
  • [ ] Deploy tagging automation across all systems
  • [ ] Integrate AI/ML for enhanced classification accuracy
  • [ ] Ensure real-time policy enforcement and compliance alignment

3. Post-Engagement Actions

  • [ ] Deliver final reports on classification schema, tagging workflows, and compliance mapping
  • [ ] Conduct training on new data classification processes and tools
  • [ ] Set up continuous monitoring and reporting
  • [ ] Regularly review and update data classification policies as new regulatory standards emerge
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