Empowering Precision: Implementing Manual Data Tagging in Zero Trust (Activity 4.3.2 Part 1)

We’ve invested heavily in establishing the framework for data analysis (Activity 4.1.1) and defining the enterprise-wide standards for data tagging and classification (Activity 4.2.1). We even have sophisticated tools to manage automated classification rules (Activity 4.3.1). However, not all data can be perfectly categorized by algorithms alone, especially at the outset of a Zero Trust implementation. For certain types of sensitive, nuanced, or newly created data, human precision remains important. This brings us to Zero Trust Activity 4.3.2: Manual Data Tagging Pt1.

This activity focuses on integrating human intelligence into the data classification process. It mandates that DoD Components map DoD Enterprise Zero Trust (ZT) tags to local labeling to meet minimum essential metadata criteria for compliance. This means Components are actively applying the enterprise-defined data classification standards (from 4.2.1) using manual processes, ensuring that even data classified by humans adheres to the common vocabulary and structure. This initial phase focuses on establishing these manual processes and ensuring their adherence to compliance requirements.

This activity is vital because accurate data tagging is the foundation for data-centric Zero Trust policies. For data where automation is not yet mature or sufficient, precise manual labeling by data owners or subject matter experts ensures that critical context is captured, enabling granular access controls and robust data protection.

The outcome for Activity 4.3.2 Part 1 highlights the establishment of this human-driven classification:

1. Data tagging is conducted at the Component-level with basic attributes.

The ultimate end state underscores the power of this standardized approach: A standardized data tagging and labeling solution is in place, ensuring all Components comply with ZT principles. Metadata criteria are consistently applied, enhancing data security and access control across the Enterprise.

Solutions for Achieving Manual Data Tagging Pt1

Implementing Activity 4.3.2 is focused on defining clear workflows for human classification and providing user-friendly tools that support the enterprise’s tagging standards.

1. Developing Manual Data Tagging Processes and Guidelines:
   1. Process: Components establish clear, step-by-step procedures for how users, data owners, or dedicated data stewards will manually tag and label data. This includes:
      1. Mapping Enterprise Tags to Local Labels: Defining how the high-level enterprise ZT tags (e.g., “Confidential – PII”) translate to specific labels or attributes that users apply in their daily tools (e.g., a “Sensitivity: Confidential” field in a document, or a specific label in a cloud storage service).
      2. Metadata Criteria: Ensuring the manual tagging process captures “minimum essential metadata criteria” for compliance (e.g., owner, creation date, retention period, specific regulatory applicability).
      3. Workflow Integration: Integrating manual tagging steps into existing data creation or handling workflows (e.g., as part of saving a document, sharing a file).
   2. Guidelines: Provide clear, concise, and accessible guidelines, often with examples, to ensure consistency in manual tagging across different users and departments.
2. User Training and Awareness:
   1. Process: Implement comprehensive training programs for all relevant users, especially data owners and creators, on the new data tagging standards (from Activity 4.2.1) and the manual tagging processes.
   2. Awareness: Emphasize the importance of accurate tagging for data security, compliance, and how it directly enables Zero Trust policies. Regular awareness campaigns reinforce these practices.
3. Implementing Tools to Facilitate Manual Tagging:
   1. Client-Side Labeling Tools: Deploy tools that provide user-friendly interfaces for applying labels directly within common applications (e.g., Microsoft Office, email clients, collaboration platforms). These tools should present the enterprise’s standardized control vocabulary (from 4.2.1).
   2. Integration with Data Catalogs: Ensure that manually applied tags and metadata are integrated with Component data catalogs (and ultimately enterprise CMDBs) to maintain a centralized, consistent view of classified data.
   3. Version Control: Implement version control for tagging guidelines and templates to ensure consistency as standards evolve.

How Trellix Can Achieve the Desired Outcomes and End State:

Trellix, particularly through its Data Loss Prevention (DLP) Endpoint suite, plays a significant role in facilitating and enforcing manual data tagging while ensuring compliance with enterprise standards and contributing to the overall Zero Trust data security posture.

• Supporting Manual Data Tagging: Trellix DLP Endpoint can provide users with interfaces directly within their applications (e.g., Microsoft Office, email clients) to apply predefined classification labels (the Enterprise ZT tags from Activity 4.2.1) to documents and emails. This allows Components to “conduct data tagging at the Component-level with basic attributes” using a tool that aligns with their existing endpoint security.
• Enforcing Metadata Criteria: Trellix DLP can be configured to enforce the “minimum essential metadata criteria for compliance.” For instance, it can prompt users to apply a tag before saving a sensitive document, or it can ensure that specific metadata fields are populated before a file can be shared. This helps ensure that manual tagging captures all required attributes.
• Mapping Enterprise Tags to Local Labeling: Trellix DLP allows organizations to import and configure their enterprise-defined ZT tags and classification policies within the DLP system. This provides the mechanism for “Components to map DoD Enterprise ZT tags to local labeling” (within the DLP’s enforcement scope).
• Ensuring Compliance and Standardization: By acting as the enforcement point for manual tagging, Trellix DLP helps ensure that users “comply with ZT principles” and that “metadata criteria are consistently applied” for manually tagged data. It can identify and flag data that is sensitive but untagged, or tagged incorrectly.
• Enhancing Data Security and Access Control: Once data is tagged (manually or automatically), Trellix DLP policies can then leverage these tags to enforce access controls and data protection rules (e.g., block sensitive data from being uploaded to unapproved cloud storage, or encrypt it before it leaves the endpoint). This directly contributes to “enhancing data security and access control across the Enterprise” as part of the end state.

For the Technical Buyer

Activity 4.3.2 is about making the human element of data classification a reliable and compliant part of your Zero Trust strategy. It focuses on Components meticulously mapping enterprise ZT tags to local labeling processes, ensuring data meets minimum metadata criteria. For technical buyers, success here means implementing clear manual tagging processes and deploying user-friendly tools that align with your enterprise standards. Trellix DLP is a strong solution for this, providing both the interface for users to apply labels and the enforcement mechanisms to ensure those labels meet compliance criteria. This activity is vital for ensuring that all data, even that requiring human insight, is accurately tagged, thereby enhancing data security and access control across your enterprise.

Pillar: Data

Capability: 4.3 Data Labeling and Tagging

Activity: 4.3.2 Manual Data Tagging Pt. 1

Phase: Target Level

Predecessor(s): 4.1.1 Data Analysis; 4.2.1 Define Data Tagging Standards

Successor(s): 4.3.3 Manual Data Tagging Pt2; 4.5.3 DRM Enforcement via Data Tags and Analytics Pt1; 4.6.2 DLP Enforcement via Data Tags and Analytics Pt1