Data Capabilities

Gather requirements:

• Leverage documentation of previously established algorithms and processes developed by the Enterprise to provide governing body oversight and awareness that it is Visible, Accessible, Understandable, Linked, Trusted, Interoperable, Secure (VAULTIS) compliant.

Define objectives and scope:

• Collaborate with the Enterprise to establish clear objectives, guidelines, and scope definition for creating the algorithm registry.
  • Include improving transparency.
  • Include enhancing management.
  • Include compliance requirements.

Identify approved authentication sources:

• Create and maintain a comprehensive list of all approved authentication systems and codes used for task automation analysis within the Component.
• Assign a responsible system owner to each category based on resource criticality.

Select solutions and technologies:

• Choose solutions for documenting algorithms.
  • Include markdown editors.
  • Include documentation generators.
  • Include version control systems.

Develop the Component Master Data Catalog plan:

• Define criteria for documenting algorithms.
  • Include algorithm type.
  • Include algorithm's purpose.
  • Include algorithm inputs.
  • Include algorithm outputs.
  • Include performance metrics.

Create a data governance body:

• Create and implement a governance structure that enables the collection and sustainment of the Component Master Data Catalog to facilitate search and retrieval in a distributed environment for baseline and change management during component growth for mission-critical tasks.
• Assign a responsible system owner to each category based on resource criticality.

Develop data catalog standards:

• Identify and standardize metadata.
  • Metadata: Information about the data, including data sources, formats, owners, and descriptions.
  • Search and Discovery: Features that allow users to search and browse the catalog for data assets and store that information in the catalog.
  • Data Lineage: Store data lineage information to track the flow of data from source to destination.
  • Data Profile: Features that allow data to be profiled, document data quality rules, and track data quality issues.
• Metadata should be determined by the Component in alignment with Enterprise requirements.
  • Descriptive Metadata Standard: Information about the data, including data sources, formats, owners, and descriptions.
  • Structural Metadata Standard: Features that allow users to search and browse the catalog for data assets and store that information in the catalog.
  • Administrative Metadata Standard: Store data lineage information to track the flow of data from source to destination.
• Examples include:
  • Categorize Data based on types.
    • Include data sorting.
    • Include data searching.
    • Include data encryption.
    • Include data Machine Learning (ML).
  • Categorize data based on the application domain and the field or industry it relates to.
    • Include data processing.
    • Include cryptography.
    • Include Artificial Intelligence (AI).
  • Categorize based on data-identified complexity levels.
    • Mark Level – low (simple data)
    • Mark Level – medium (moderate data)
    • Mark Level – high (complex data)
• As per the need of the Component, adopt one or more of the four (4) Master Data Management architecture styles to implement master data: (a) Registry Style, (b) Consolidation Style, (c) Coexistence Style, and (d) Transaction/Centralized Style.

Develop Master Data Inventory and standards:

• Leverage data owners to compile a comprehensive list of data and associated metadata.
• Consolidate information collected into the Component Master Data Inventory.
• Choose Master Data Management solutions for creating Master Data.
  • Include markdown editors.
  • Include documentation generators.
  • Include version control systems.
  • Ability to read various data formats.
  • Ability to handle Application Programming Interface (API) calls (e.g., Representational State Transfer (REST), Simple Object Access Protocol (SOAP), etc
• Using data governance processes, create data update rules to create the master data “golden” record, also known as “Single Version of Truth”. (Data update rules can include data cleansing, data transformation, data standardization, data merge, etc.)
• Identify and document the goals and key outcomes of their Master Data Management process. For example, a Component can create a Master Data Management process to improve their data quality, improve access, and/or usage.

Verify and validate the accuracy/integrity of the Master Data Inventory:

• To ensure the integrity of the Master Data Inventory, it is necessary to review it for accuracy and completeness periodically.
• The frequency for periodic review of data governance will need to be determined by the Component in alignment with Enterprise requirements.
  • It is strongly recommended that the frequency is no longer than annually.
• Continuously monitor the Master Data Management implementation to add new data sources and update new business rules that govern the “golden” record.

Enterprise-Component collaboration framework:

• Establish cross-functional working groups with representation from Enterprise data governance teams, Component data stewards, security specialists, and end users to ensure comprehensive input.
• Document formal communication channels between the Enterprise and Components to facilitate ongoing alignment and updates to standards.
• Create a standardized feedback loop to provide insights on implementation challenges and improvement opportunities back to Enterprise governance bodies.

Data taxonomy development:

• Conduct comprehensive data inventory to identify all data types, sources, and usage patterns across the Component.
• Map existing classification schemes to the Enterprise standard and document translation requirements.
• Define taxonomy hierarchy levels that align with Enterprise-defined data sensitivity classifications while supporting Component-specific needs.
• Create visual reference materials (e.g., decision trees, flowcharts, etc.) to help data owners determine appropriate tags.

Control vocabulary management:

• Implement version control systems for tag libraries to track changes and maintain historical records.
• Establish authorization workflows for proposing, reviewing, and approving new tags or modifying existing tags.
• Define metadata schemas that include mandatory and optional fields aligned with Enterprise standards.
• Establish tag conflict resolution procedures when multiple classification schemes apply to the same data.
• Create tag validation processes to ensure consistency and compliance with standards.

Data tagging policy implementation:

• Develop comprehensive documentation that clearly articulates tagging requirements, procedures, and responsibilities.
• Establish compliance monitoring mechanisms to track adherence to tagging standards.
• Implement periodic audit procedures to validate tag accuracy and completeness.
• Create remediation processes for addressing non-compliant or incorrectly tagged data.

Cross-Component interoperability:

• Implement a federated tag library architecture that supports both Enterprise-wide and Component-specific tags.
• Establish metadata exchange protocols between Components to maintain consistency.
• Define cross-boundary data handling procedures to preserve tags when data moves between Components.
• Create integration testing frameworks to validate tag interoperability prior to production implementation.

System integration requirements:

• Define Application Programming Interface (API) specifications for integrating tagging capabilities into existing systems.
• Establish data exchange formats that preserve tag metadata during transfers.
• Implement tag propagation mechanisms to maintain classification through data transformations.
• Create technical validation procedures to ensure systems correctly interpret and apply tag restrictions.

Security implementation requirements:

• Define tag-based access control mechanisms that enforce ZT principles.
• Where necessary, implement tag encryption requirements for sensitive classification metadata.
• Establish tag verification procedures to verify and validate authenticity and prevent tampering.
• Create security monitoring capabilities that leverage tag information for anomaly detection.

Migration planning:

• Develop transition strategies from legacy classification systems to new tagging standards.
• Establish data remediation procedures for previously untagged or improperly tagged data.
• Create rollback capabilities to address potential implementation issues.
• Define contingency operations to maintain security during transition periods.

Implementation risk assessment and mitigation:

• Identify critical implementation risks such as operational impacts, resource constraints, and technical limitations.
• Develop targeted mitigation strategies for each identified risk.
• Establish risk monitoring mechanisms to detect emerging implementation challenges.
• Create escalation procedures for addressing critical implementation blockers.

Select data tagging solution:

• Identify a data tagging solution that supports both Component needs and Enterprise interoperability requirements.
• Where possible, select a solution that can implement tag inheritance mechanisms to efficiently apply tags to data hierarchies.

Test data tagging solution:

• Within a controlled environment, ensure the selected solution(s):
  • Meet previously identified requirements.
  • Integrate with the Component environment(s).
  • Successfully provide the advertised functionality within the Component environment(s).

Manage exceptions:

• Data/data types on systems that are incompatible with the data tagging solution are:
  • Identified
  • Documented
  • Approved or Rejected
• Approval is granted when justification for the exception outweighs the risks to the Enterprise/Component.
  • If approved, they are documented for manual tagging.
• The Enterprise and/or Component determines risks.
• Approval is periodically reassessed.

Manual tagging processes:

• Develop role-based tagging responsibilities, clearly defining who is authorized to assign types of tags.
• Establish quality control checkpoints to verify and validate manually applied tags.

Enterprise alignment:

• Inventory existing Enterprise interoperability standards by cataloging all approved data exchange formats, protocols, and frameworks already in use across the DoW.
• Establish an interoperability assessment framework to evaluate current capabilities against ZT requirements.
• From cross-functional working groups with Enterprise and Component representatives to ensure a comprehensive understanding of technical and operational requirements.
• Identify regulatory and compliance mandates that impact data interoperability, particularly those related to classification levels and handling requirements.

Component-specific requirements:

• Leverage the Component Data Catalog, from Activity 4.1.1 (Discovery) – Data Analysis, to identify critical data assets requiring interoperability solutions.
• Document existing data-sharing agreements with other Components, partners, and third parties.
• Map data flows across systems to identify integration points and interoperability requirements.
• Conduct stakeholder interviews to identify operational needs and challenges related to data sharing.

DRM integration:

• Leverage the Component-level access control policies, from Activity 4.4.2 (Discovery) – Data Rights Management (DRM) Enforcement Point Logging and Analysis.
• Assess current DRM implementations to determine compatibility with Enterprise standards.
• Identify interoperability standards for DRM and protection to enforce comprehensively across the Component environment(s), utilizing a common language.

Define and document data exchange patterns considering ZT principles:

• Authentication and authorization requirements for all data exchanges.
• Encryption standards for data in transit and at rest.
• Audit and logging requirements for accountability.
• Mechanisms for enforcing data exchange policies across participating systems.

Establish and document standardized data models for each identified data category, considering interoperability and enforcement needs:

• Schema definitions with mandatory and optional fields.
• Data type specifications and validation rules.
• Standard identifier formats for cross-system references.

Develop and document machine-to-machine communication protocols aligned with approved interoperability and security standards:

• Real-time data exchange with minimal latency.
• Asynchronous communication for non-critical exchanges.
• Error handling and resiliency mechanisms.
• Standard authentication, authorization, and enforcement mechanisms to ensure consistent access control.

Establish SDS standards and policy:

• Identify stakeholders to establish a unified SDS framework.
• Identify existing SDS and/or the potential need for SDS based on Component operational demands.
• Review industry best practices, technology trends, third-party recommendations, and vendor accreditations to tailor a Component SDS strategy.
• Collaborate with the Enterprise to define the overarching SDS standards and policy.)

Define and establish SDS standards:

• Define clear goals, objectives, and scope for the Component SDS policy based on mission-operational objectives and requirements.
• Align the developed SDS strategy and policy with existing Enterprise SDS policy, mandates, and standards for compliance.
• Define multiple storage tiers based on performance, cost, data sensitivity, and compliance requirements. Establish storage criteria that are aligned with the overall Enterprise data governance.
• Establish data residency rules as applicable and mandated by federal, Enterprise, and local laws and regulations. Determine relevant factors such as application requirements, data categorization, geographic location, and operational constraints.

Develop applicable use cases for SDS:

• Engage with various stakeholders across all Components to develop the Component SDS solution. Prioritize and characterize sensitive applications and workloads to gain security insights.
• Analyze all relevant workloads and applications to better understand performance requirements, capacity demands, and data access traffic patterns.
• Establish SDS policy for data replication, snapshots, and backups to ensure data high availability and disaster recovery compliance. Review and enforce recovery point and time objectives as a baseline to meet business continuity requirements.
• Integrate Access Control security policies with the SDS strategy to restrict access to storage resources based on data categorization, User/Person Entity (PE)/Non-Person Entity (NPE) roles and attributes, and environment conditions. Key features to consider:
  • Data encryption
  • Scalability and performance
  • Capacity management
  • Quality of Service
  • Hard Disk Drive (HDD) and Solid State Drive (SSD) storage types

Review data storage strategies:

• Leverage the Component SDS policy and standards.
• Identify all data storage solutions within the Component environment. Leverage the:
  • Component Master Data Inventory, from Activity 4.1.1 (Discovery) – Data Analysis
  • Component Master Application Inventory, from Activity 3.1.1 (Discovery) – Application and Code Identification
• Review benchmarks and key performance metrics to verify and validate the expected outcomes associated with the data storage policy.
• Review, verify, and validate the effectiveness of data backup strategy, data availability and reliability, disaster recovery capabilities, data retention requirements, privacy compliance, and overall data access-control security.

Publish a comprehensive SDS strategy:

• Engage stakeholders for SDS policy adoption. Monitor and enable feedback for business leaders on operational impact.
• Incorporate the developed SDS policy with a broader data governance strategy and data security. Key features to consider:
  • Service Level Agreements (SLAs)
  • Data growth forecast
  • Key performance indicators
  • Data Access Control Lists (ACLs)

Identify data storage/management solutions, constraints, and data collection points, and establish a standardized data collection architecture:

• Identify existing data storage and management solutions, including local, cloud, and/or Hyperconverged Infrastructure (HCI). Examples include:
  • Data Lakes
  • Data Marts
  • Operational databases (e.g., Online Transaction Processing (OLTP) Database, Metadata repositories, etc.)
• Identify data storage and management constraints:
  • Capacity and Performance:
    • Storage limits, processing capacity, query performance, Service Level Agreements (SLAs), and concurrent User/Person Entity (PE) support boundaries that impact architecture decisions.
  • Security and Compliance:
    • Data protection requirements, regulatory constraints, Access Controls, and audit trails needed for sensitive information.
  • Integration and Compatibility:
    • Limitations in connecting with existing systems, tools, and Application Programming Interfaces (APIs), and the need to support various data formats and schemas.
  • Governance and Data Quality:
    • Requirements for metadata management, lineage tracking, data verification and validation, and alignment with Enterprise standards.
  • Operational Boundaries:
    • Maintenance windows, deployment restrictions, disaster recovery requirements, and lifecycle management constraints.
• Identify existing data collection points, such as:
  • Data Collection Node (DCN)
  • System Log (Syslog) data collection
  • Forwarders
  • Hypertext Transfer Protocol (HTTP) Event Collector (HEC)
• Establish standardized data tagging and classification models. Implement solutions to automatically tag data at the point of creation or production and provide mechanisms for data stewards to tag and classify existing data assets.

Select data tagging solution(s):

• Leverage data tagging and criticality standards, documented in the federated tag library, from Activity 4.2.1 (Phase One) – Define Data Tagging Standards.
• Identify data tagging tools/solutions that meet Enterprise/Component-defined requirements and align with the standards in the federated tag library. At a minimum, the data tagging solution(s) should include the following functionalities:
  • Data tagging at creation
  • Tagging for data discovery
  • Tagging for imported data
  • Quarantine untagged data
• Identify a global key access store solution to act as a centralized tag repository/single source of truth for all tags:
  • Ensure the key access store solution can align with the federated tag library standards.
• Design federation processes.
• Define data quality requirements and create a data model:
  • Develop data metrics.
  • Determine acceptable data duplication and collision ratio.
  • Alert on inconsistent data events.
• Define data quality requirements and data duplication/standardization.
• Define rule-based tagging algorithms aligned with established classification criteria.
• Implement Machine Learning (ML) models for content-based classification that can be iteratively improved.
• Establish confidence thresholds for automated tagging with human review requirements for borderline cases.
• Create exception handling processes for data that cannot be automatically classified with sufficient confidence.

Hybrid tagging approaches:

• Define workflow integration points between manual and automated processes.
• Establish escalation procedures for resolving tagging discrepancies.
• Implement feedback mechanisms to continuously improve automation accuracy based on manual corrections.
• Create automated quality sampling to validate both manual and automated tagging processes.
• Define a data tagging process for testing, verification, and validation:
  • Enable monitoring and auditing to verify and validate compliance with expected outcomes.
  • Create and review audit trails from data access and tagging activities.

Verify and validate data tagging solutions:

• Ensure the selected solutions provide the necessary capabilities by testing their ability to implement Enterprise/Component requirements.

Technical Infrastructure:

• Implement federation processes.
• Implement Access Controls and security layers.
• Develop monitoring and reporting solutions.

Workflow Implementation:

• Develop data tagging workflows (e.g., creation, discovery, import, etc.).
• Create quarantine mechanisms for untagged data.
• Build search and discovery interfaces.

Deployment:

• Pilot implementation with limited scope.
• Progressive rollout across data domains or business units.
• Integration with existing systems.

Metrics and monitoring:

• Define Key Performance Indicators (KPIs) for data tagging effectiveness (e.g., accuracy, completeness, timeliness, etc.).
• Implement tagging coverage monitoring to identify gaps in tagged data stores.
• Establish periodic compliance reviews to validate alignment with Enterprise standards.
• Create automated dashboards to visualize tagging implementation progress and effectiveness.

Continuous improvement process:

• Implement feedback collection mechanisms from data users and stakeholders.
• Establish regular review cycles to evaluate tagging effectiveness and identify improvement opportunities.
• Create pilot programs for testing enhancements before full implementation.
• Develop knowledge sharing platforms to exchange best practices between Components.

Create comprehensive mapping relationships:

• Leverage the Component-defined data tagging solution(s), from Activity 4.3.1 (Phase One) – Implement Data Tagging and Classification Tools.
• Develop a formal tag mapping document that explicitly correlates each Enterprise ZT tag to corresponding Component-level tags, documenting rationale for each mapping decision.
• Identify and document gap areas where Enterprise tags have no Component equivalent, with clear remediation plans, including timeframes, for developing missing tags.
• Establish a tiered prioritization schema for implementing tag mappings, focusing first on data that directly supports security decisions in the ZT environment.
• Create visual mapping matrices for different data categories showing Enterprise-to-Component tag relationships that can be referenced by both technical teams and data owners.
• Develop tag coverage metrics that measure both the breadth (percentage of data tagged) and depth (completeness of applied tags) across Component data assets.

Develop a tagging implementation plan:

• Develop a Component-level tagging implementation roadmap with clear phases tied to data sensitivity and criticality, ensuring the most sensitive data receives tags first.
• Create tag application templates for common data types that streamline consistent tag application and reduce manual decision-making.
• Implement tag inheritance rules for derivative data to maintain proper classification as data is transformed within the workflows.
• Document tag override procedures for exceptional cases where standard tag mappings may not apply, with appropriate approval chains.

Streamline the data tagging process:

• Leverage tagging conventions, defined in the key access store, from Activity 4.3.1 (Phase One) – Implement Data Tagging and Classification Tools.
• Apply data tagging to data objects through a phased approach, prioritizing data in accordance with data tagging standards, from Activity 4.2.1 (Phase One) – Define Data Tagging Standards.

Enable practical tag integration across boundaries:

• Develop automated tag translation services that can convert between Enterprise and Component tag schemas when data crosses organizational boundaries.
• Implement tag persistence policies, ensuring that original Enterprise tags remain associated with data even when Component-specific tags are applied.
• Create tag validation checkpoints at key data exchange points to verify that mapped tags maintain semantic equivalence and security properties.
• Establish data lineage tracking to maintain the history of tag translations as data moves between Enterprise and Component environments.

Integrate tagging with the environment:

• Configure Data Loss Prevention (DLP) and Data Rights Management (DRM) solutions to recognize and enforce both Enterprise and Component-level tag schemas.
• Develop security policy mapping documents showing precisely how different tags trigger specific security controls and restrictions.
• Implement real-time tag verification capabilities at security enforcement points to prevent tag manipulation or removal.
• Create integration reference architectures demonstrating how tags flow between tagging systems, Security Information and Event Management (SIEM) solutions, and security control mechanisms.
• Develop operational use cases that demonstrate how mapped tags enhance access decisions in alignment with ZT principles. Ensure data tags can be ingested by SIEM, Security Orchestration, Automation, and Response (SOAR), and other tools/solutions supporting the Visibility and Analytics and/or Automation and Orchestration pillars.

Review, verify, and validate expected outcomes:

• Verify and validate data tags/metadata criteria are consistently applied to data objects in accordance with Component policy and standards.
• Routinely conduct audits to ensure data tagging remains effective and compliant with applicable laws and regulations. Enable exception handling to drive future lessons learned sharing.
• Review and report all the data tagging inconsistencies to help improve processes and procedures. Enable feedback loop mechanisms to verify and validate tag accuracy and consistency over time.

Identify key stakeholders:

• Establish a cyber collaboration center to assess cyber incidents and securely share findings with approved partners. Review Enterprise initiatives and compliance requirements on Indicators of Compromise (IoC), data breaches, and Incident Responses (IRs).
  • Create dedicated internal teams.
  • Identify external components.
  • Establish broader national partners.

Define objectives and scope:

• Leverage the data sensitivity/classification levels documented in the Component Data Catalog, from Activity 4.1.1 (Discovery) – Data Analysis.
• Collaborate with stakeholders to establish a DLP policy. The Component DLP plan should include:
  • Scope:
    • What systems will fall under DLP enforcement?
      • Environments, devices, endpoints, etc.
  • Integration:
    • Determine how DLP responses will integrate with existing Component IR actions, which, at a minimum, should include:
      • Preparation and planning
      • Detection and analysis
      • Data recovery
  • Enforcement:
    • Establish the business rules/enforcement strategy, such as when to block vs. alert on policy violations.
  • Exceptions:
    • Create an exception process and standards.
  • Visibility:
    • Audit requirements, such as frequency, alert criteria, integration into Security Information and Event Management (SIEM), Security Orchestration, Automation and Response (SOAR), and other Capabilities established in the Visibility and Analytics and/or Automation and Orchestration Pillars.
  • Reporting:
    • Determine how alerts are handled, including stakeholders, data owners, and IR responders.

Identify DLP enforcement points:

• Identify DLP enforcement points within the Component environment:
  • Endpoint DLP:
    • Install DLP agents on endpoint devices to monitor and safeguard sensitive data where possible.
  • Web DLP:
    • Deploy web gateways to scan web traffic for sensitive data
  • Network DLP:
    • Deploy network appliances with DLP built-in capability.
  • Email DLP:
    • Protect email servers with DLP monitoring.
  • Cloud DLP:
    • Adopt cloud-based DLP solutions.

Select a DLP solution that meets the needs of the Component:

• Leverage the previously created Component DLP policy.
• Leverage the Component Data Catalog from Activity 4.1.1 (Discovery) – Data Analysis.
• Leverage the Component Master Device Inventory from Activity 2.1.1 (Discovery) – Device Health Tool Gap Analysis.
• Leverage the Component Master Application Inventory from Activity 3.1.1 (Discovery) – Application and Code Identification.

Verify and validate the DLP solution:

• Ensure the selected solutions provide the necessary capabilities by testing their ability to implement Enterprise/Component requirements.

Review Enterprise/Component logging requirements:

• Leverage industry standards, such as National Institute of Standards and Technology (NIST) Special Publication (SP) 800-92r1 log management implementation planning, policies, and processes.

Review DLP logging events.

• Explore data enrichment with contextual information for in-depth log analysis to help create a complete story and timeline of application and event logs.
• Leverage log information to trigger the appropriate DLP action (e.g., block, alert, allow, quarantine, etc.).

Review DLP logging formats:

• Adopt a standardized format for all application and event logs consistent across all platforms for searching, filtering, and collection from various sources for unified analysis.
• Consolidate all DLP enforcement points generated logs into a centralized logging repository for advanced threat detection, malicious activity, and correlation.
• Integrate logging with the Component solutions supporting the Visibility and Analytics and/or Automation and Orchestration Pillars.

Analyze DLP logs:

• Leverage the DLP-generated logs to assess and track data security incidents. Capture and analyze standard fields in DLP logging format to identify threats and manage compliance.

Monitor DLP events:

• Implement system capability to identify, monitor, and protect data in use, data in motion, and data at rest through activity monitoring, encryption, deep packet content inspection, and contextual security analysis of transactions within a centralized management framework.
• In coordination with all relevant stakeholders (e.g., System Administrators (SAs), Subject Matter Experts (SMEs), owners, operators, managers, etc.), define each distinct DLP Policy Enforcement Point (PEP) access control boundary.
• Use a collaborative approach, Technology Expense Management, and Information Processes and Technology systems engineering processes, managing Users/Person Entities (PEs)/Non-Person Entities (NPEs) in approved legacy Enterprise-wide DLP or equivalent solutions.

Develop data retention for testing DLP policies:

• Establish and obtain identified and defined rules for DLP enforcement to create a comprehensive catalog of all task activities that collect and process all DLP evaluation logs and make them available to the Computer Network Defense Service Provider (CNDSP), Cyber Operations (CyberOps), or Security Operations Center (SOC/Security Operations (SecOps)).
• Establish data retention policies to make DLP-generated logs available for IR and forensic analysis.

Review, verify, validate, and audit DLP logs:

• Create a comprehensive catalog of all identified DLP logs with PEP for access control boundaries to establish a baseline list that facilitates communication between Software-Defined Networking (SDN) components.
• Review regulatory changes in data governance, such as the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and Payment Card Industry Data Security Standard (PCI DSS), to drive continuous compliance and update DLP policies.

Review Enterprise data standards:

• Adopt the Enterprise open data architecture to enable data and information secure utilization and sharing. Review and align with the data “fit for purpose” principle for protected quality data, readily discoverable and understood within the context of its intended use.
• Leverage the Enterprise metadata guidance to exploit metadata as a key element to effectively provide protection and context and enforce DRM control access policies.

Define Component-level access control policies:

• Leverage existing Enterprise standards to establish Component-level access control and identity authentication for information systems.
• Leverage Multi-Factor Authentication (MFA), from Activity 1.3.1 (Phase One) – Organizational Multi-Factor Authentication (MFA) and Identity Provider (IdP).
• Review, verify, and validate at the Component-level that the access requirements align with Least Privilege and deny-by-default policy, from Activity 1.7.1 (Phase One) – Deny User by Default Policy.

Define Component-level data guidelines:

• Leverage Component Data Catalog, from Activity 4.1.1 (Discovery) – Data Analysis to understand data sensitivity/classification levels.
• Leverage global key access store as the single source of truth regarding data attributes, from Activity 4.3.1 (Phase One) – Implement Data Tagging and Classification Tools.

Manage data assets Acceptable Use Policy (AUP):

• Define roles and responsibilities for data stewardship, custodian to enforce the ethical use of data created, collected, and shared across all infrastructure platforms in accordance with AUP. Integrate DRM with Data Loss Prevention (DLP) tools to apply protective countermeasures and encryption to sensitive and regulated data types.

Define DRM key enforcement points:

• Adopt a strong encryption algorithm(s) for content protection at either disk or file level. Enable robust encryption for data at rest, in motion, and in use.
  • Where possible, leverage Enterprise and industry standards, such as National Institute of Standards and Technology (NIST) Federal Information Processing Standards (FIPS) 140-3 Security Requirements for Cryptographic Modules.
• Establish dynamic permission management and licensing control for sensitive and copyrighted digital content. Enable file tampering detection and protection.

Identify DRM enforcement points:

• Identify DRM enforcement points within the Component environment:
  • Endpoint DRM: Install DRM agents on endpoint devices to monitor and safeguard sensitive data where possible.
  • Web DRM: Deploy web gateways to scan web traffic for sensitive data.
  • Network DRM: Deploy network appliances with DRM built-in capability.
  • Email DRM: Protect email servers with DRM monitoring.
  • Cloud DRM: Adopt cloud-based DRM solutions.

Identify key stakeholders:

• Establish a cyber collaboration center to assess cyber incidents and securely share findings with approved partners. Review Enterprise initiatives and compliance requirements on Indicators of Compromise (IoC), data breaches, and IRs.
  • Create dedicated internal teams.
  • Identify Enterprise-approved Communities of Interest (COI).
  • Establish broader national partners.

Define cyber defense and IR strategy:

• Collaborate with mission partners, various agencies, and other Components to adopt a proactive cyber defense plan to protect critical assets against cyber threats through deterrence, prevention, detection, containment, and response.

Integrate DRM solutions with data security tools:

• Select complementary tools, compatible DRM solutions, and data security tools that offer Application Programming Interfaces (APIs) and integration capabilities to enable unified Access Control and policy enforcement.
• Integrate an Identity and Access Management (IAM) policy with a DRM solution to enable and restrict data Access Control based on User/Person Entity (PE)/Non-Person Entity (NPE) roles, permissions, and contextual attributes.

Review DRM compliance requirements:

• Establish a centralized DRM protection solution to monitor and improve data compliance across various infrastructure platforms, file repositories, network segments, and host-based and cloud-based storage.
• Import extracted DRM log schema-compliant metrics for tracking into a consolidated, central repository or designated ticket tracking system(s).
• Normalize the data to ensure consistency across different authenticated and approved sources.

Select a DRM solution that meets the needs of the Component:

• Leverage the previously created Component DRM policy.
• Leverage the Component Master Device Inventory, from Activity 2.1.1 (Discovery) – Device Health Tool Gap Analysis.
• Leverage the Component Master Application Inventory, from Activity 3.1.1 (Discovery) – Application and Code Identification.
• Leverage the Component Data Catalog, from Activity 4.1.1 (Discovery) – Data Analysis.

Review data interoperability requirements:

• Ensure the selected solutions provide the necessary capabilities by testing their ability to implement Enterprise/Component requirements.
• Enable the adoption of diverse DRM content formats with offline access capabilities and support for legacy systems. Review and evaluate the interoperability requirements for DRM implementation.
• Prioritize DRM solutions that support multiple open DRM standards and APIs and enable format conversions to ensure compatibility across different platforms and devices without compromising security.
• Select and leverage technology and technological systems to protect copyrighted digital content and limit illegal sharing and unapproved spread.

Review Enterprise/Component logging requirements:

• Leverage industry standards, such as National Institute of Standards and Technology (NIST) Special Publication (SP) 800-92r1 log management implementation planning, policies, and processes.
• Verify and validate that standardized logging procedures are sufficient to support DRM toolset configuration.

Review DRM logging events:

• Explore data enrichment with contextual information for in-depth log analysis to help create a complete story and timeline of application and event logs.
• Leverage log information to trigger the appropriate DRM action (e.g., block, alert, allow, quarantine, etc.).

Review DRM logging formats:

• Adopt a standardized format for all application and event logs consistent across all platforms for searching, filtering, and collection from various sources for unified analysis.
• Consolidate all DRM enforcement point-generated logs into a centralized logging repository for advanced threat detection, malicious activity, and correlation.
• Integrate logging with the Component solutions supporting the Visibility and Analytics and/or Automation and Orchestration Pillars.

Develop data activity monitoring for testing of DRM policies:

• Establish and obtain identified and defined rules for DRM enforcement to create a comprehensive catalog of all task activities that collect and process all DRM evaluation logs and make them available to the Computer Network Defense Service Provider (CNDSP), Cyber Operations (CyberOps), or Security Operations Center (SOC/SecOps).
• Establish data retention policies to make DRM-generated logs available for IR and forensic analysis.

Review, verify, validate, and audit DRM logs:

• Create a comprehensive catalog of all identified DRM logs with Policy Enforcement Points (PEPs) for Access Control boundaries to establish a baseline list that facilitates communication between Software-Defined Networking (SDN) components.
• Review regulatory changes on data governance, such as the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), and mission-critical data assets to drive continuous compliance and update DRM policies.
• Review DRM PEP with approved, authoritative, and authenticated sources from across and within the Enterprise and extended to the Communities of Interest (COI).
• Normalize the data to ensure consistency across different approved authentication sources.

Collaborate with stakeholders:

• Identify stakeholders to establish FAM policy/processes.
• Collaborate with the Enterprise to gather overarching requirements.

Leverage data tagging solutions/standards:

• Leverage data tagging standards, from Activity 4.1.1 (Discovery) – Data Analysis.
• Leverage the Component key access store, from Activity 4.2.1 (Phase One) – Define Data Tagging Standards, as the single source of truth on tagging standards.
• Leverage the Component Data Loss Prevention (DLP) policy, from Activity 4.6.1 (Phase One) – Implement Enforcement Points.
• Create a detailed mapping document connecting data tagging standards, from Activity 4.1.1 (Discovery) – Data Analysis, to specific FAM monitoring requirements, ensuring semantic alignment between tags and monitoring rules.
• Develop technical integration specifications for how the FAM solution will interface with the Component key access store to obtain authoritative tag information in real-time.
• Define specific DLP policy extensions focused on monitoring critical data with clear documentation of how DLP and FAM solutions will complement each other rather than duplicate functionality.

Select a FAM solution:

• Identify a potential FAM solution that will integrate with the existing:
  • Data tagging standards
  • Data, Applications, Assets, and Services (DAAS)
  • Visibility and Analytics Pillar solutions (e.g., Security Information and Event Management (SIEM), etc.)
  • Automation and Orchestration Pillar solutions (e.g., Security Orchestration, Automation, and Response (SOAR), Extended Detection and Response (XDR), etc.)
• Collaborate with Incident Response (IR) teams to ensure potential FAM solutions meet Enterprise/Component alerting and monitoring requirements, as established in the Component DLP policy.
• Create a FAM solution requirements matrix that evaluates capabilities across key dimensions including:
  • Support for monitoring structured and unstructured data across diverse repository types.
  • Ability to detect and alert on unusual access patterns to critical data.
  • Capabilities to identify data misclassification based on content analysis.
  • Forensic capabilities to provide detailed activity history for investigations.
  • Performance impact assessment under various monitoring configurations.
• Develop an integration validation checklist to verify FAM solution compatibility with:
  • Both Enterprise and Component-specific data tag formats.
  • Existing data repositories and file systems requiring monitoring.
  • Specific SIEM/SOAR/XDR solutions currently deployed in the environment.
  • Legacy systems containing critical data that may require specialized monitoring approaches.
• Establish FAM testing scenarios that simulate critical threat patterns to validate detection capabilities, including:
  • Mass file access or downloads of critical data.
  • Off-hours access to sensitive repositories.
  • Unusual data access patterns indicating potential exfiltration.
  • Modification of critical files by unauthorized users.

Develop a FAM solution implementation plan:

• Develop a detailed phased deployment roadmap based on critical data prioritization, with clear milestones, dependencies, and success criteria for each Phase.
• Create implementation templates for common repository types to accelerate consistent deployment across similar environments.
• Establish a deployment verification process that confirms comprehensive monitoring coverage for each critical data repository before proceeding to the next deployment Phase.
• Document FAM monitoring exclusions and exceptions with appropriate justifications and compensating controls where direct monitoring isn't feasible.
• Develop detailed integration specifications for each security platform, including:
  • Data field mappings between systems.
  • Event normalization rules to ensure consistent interpretation.
  • Bi-directional integration capabilities for coordinated response actions.
  • Correlation rules leveraging FAM data to enhance other security detections.
• Create integration test scenarios that validate end-to-end workflows from detection through alerting to response across connected systems.

Enhanced Business Rules Implementation:

• Develop a comprehensive rule library mapping specific critical data classifications to corresponding detection rules, with clear documentation of:
  • Event thresholds triggering alerts.
  • Correlation requirements with other security events.
  • Response workflows for different alert severities.
  • Exceptions handling procedures for authorized deviations.
• Create custom rule templates for Component-specific critical data types that may not align with standard Enterprise classifications
• Implement progressive rule deployment starting with monitoring-only mode before enabling alerting and enforcement actions.

Test FAM capabilities:

• Develop a comprehensive test plan with specific validation scenarios for each critical data designation, including:
  • Tests for detection accuracy across different repository types.
  • Performance impact testing under peak load conditions.
  • False positive/negative analysis for alert configurations.
  • Verification of proper tag interpretation and policy application.
• Ensure the selected FAM solution meets the Enterprise/Component-defined requirements.

Manage exceptions:

• Files that cannot be monitored or systems incompatible with the FAM are:
  • Identified
  • Documented
  • Approved or Rejected
• Approval is granted when justification for the exception outweighs the risks to the Enterprise/Component.
• The Enterprise and/or Component determine risks.
• Approval is periodically reassessed.

Implement FAM:

• Deploy FAM solution(s) through a phased approach in accordance with Enterprise/Component-defined priorities, risk determination, and operational impacts.
  • This Activity is scoped to critical data.
• Implement business rules to consume critical data designations and manage outcomes in accordance with the Component DLP policy.
• Integrate the FAM solution(s) with Visibility and Analytics and Automation and Orchestration Pillar solutions, to include:
  • SIEM
  • SOAR
  • Endpoint Detection and Response (EDR)
  • User and Entity Behavior Analytics (UEBA)

Verify and validate:

• Ensure the FAM solution(s) meets the needs of the Component and align with Enterprise requirements after implementation.
• Confirm that the operational impact of the FAM solution is acceptable to the Component.
• Periodically reassess the functionality of the FAM solution(s) to ensure comprehensive coverage and compliance with Enterprise/Component requirements.
• Create processes for periodic rule tuning based on false positive/negative analysis and evolving threat patterns.
• Where possible, implement continuous monitoring of FAM solution efficiency with metrics tracking:
  • Alert-to-investigation ratio measuring detection quality.
  • Mean time to detect critical data incidents.
  • Coverage percentage across critical data repositories
  • Performance impact trending on monitored systems.

Leverage the existing Component FAM solution:

• Extend the existing Component FAM solution, from Activity 4.4.3 (Phase One) – File Activity Monitoring Part 1, to include regulatory protected data (e.g., Controlled Unclassified Information (CUI), Personally Identifiable Information (PII), Protected Health Information (PHI), etc.).
• Create a prioritized implementation schedule for adding monitoring coverage to different categories of regulated data, considering factors such as:
  • Data volume and distribution across repositories.
  • Regulatory compliance deadlines.
  • Technical complexity of detection requirements.
  • Integration dependencies with other security systems.
• Develop specialized detection patterns for each regulatory data type (CUI, PII, PHI) that weren't covered in the critical data monitoring implemented in Activity 4.4.3 (Phase One) – File Activity Monitoring Part 1, such as:
  • Content-based patterns specific to regulatory frameworks.
  • Contextual access patterns unique to regulated data.
  • Organizational usage patterns requiring special monitoring attention.
• Create configuration templates for extending existing FAM deployments to include regulated data types, documenting:
  • Detection rule modifications needed for regulatory compliance.
  • Monitoring depth adjustments required for different data types.
  • Alert thresholds specific to regulatory requirements.
  • Reporting parameters necessary for compliance documentation.
• Establish supplemental monitoring policies, specifically addressing regulatory requirements not covered in critical data monitoring, with detailed specifications for:
  • Minimum monitoring coverage requirements by regulation.
  • Evidence collection standards for regulatory audits.
  • Integration points with compliance management systems.
  • Regulatory-specific retention policies for monitoring data.

Extend FAM solution:

• Implement the extended FAM coverage/capabilities in a phased approach, prioritizing data based on:
  • Risk-based implementation tiers: Create a multi-tier implementation framework categorizing regulated data by risk level, with highest-risk data categories (e.g., classified CUI, PHI with large volume, etc.) implemented first.
  • Regulatory deadline alignment: Synchronize the implementation schedule with compliance deadlines and audit cycles to ensure timely coverage of regulated information subject to upcoming reviews.
  • Data exposure surface: Prioritize monitoring for regulated data with the broadest access patterns or largest user base to maximize initial security impact.
  • Technical complexity considerations: Develop a complexity assessment matrix to identify which regulatory data types require specialized detection mechanisms beyond standard pattern matching.

Verify and validate:

• Ensure the FAM solution continues to meet the needs of the Component.
• Confirm that the operational impact of the FAM solution is acceptable to the Component.
• Continuously reassess the functionality of the FAM tool to ensure comprehensive coverage and compliance with Enterprise/Component requirements.
  • The Enterprise/Component must define frequency, but the application of digital policy requires consistent oversight.
• Conduct regular gap analysis against regulatory requirements, integration effectiveness, and coverage.

Leverage existing DRM policies:

• Review Enterprise/Component guidelines on DRM policies and data taxonomy and ensure compliance adherence. Assess mission-critical data assets and categorize them based on predefined approved rules.
  • Review Enterprise data classification.
  • Leverage critical asset mapping.

Review data protection mechanisms:

• Develop and enforce data asset protection to help safeguard sensitive data across the entire Component environment. Leverage Policy Decision Points (PDPs), Policy Enforcement Points (PEPs), and Access Control policies to perform critical asset mapping, such as:
  • PDP
  • PEP
  • Time-based restrictions

Review DRM technical requirements:

• Refer to the Enterprise technical requirements and industry best practices while selecting a DRM solution. The following is a list of features to consider supporting growth, facilitate integration, enforce compliance, and enhance security.
  • Encryption capability
  • Application Programming Interface (API) calls compatibility
  • Seamless integration
  • Automated policy enforcement

Leverage the Component DRM solution:

• Leverage the Component selected DRM solution, from Activity 4.4.2 (Discovery) – Data Rights Management (DRM) Enforcement Point Logging and Analysis.
• Ensure the DRM solution meets the requirements established in this activity.
• Depending on the regulatory bodies, rules, and requirements, the compliance capability should be built into all DRM solutions to help maintain adherence to legal and regulatory compliance.
  • Compliance support
  • Violation reporting

Asset alignment and license management:

• Maintain audit trails of all data assets activities based on predefined rules and actions. Implement and secure system logs to enable forensic analysis. Deploy a centralized licensing server to manage, verify, and validate licenses.
  • Audit logs
  • Real-time monitoring
  • License expiration and management
  • Distribution of decryption keys

Implement DRM solution:

• Deploy the DRM solution on high-risk data, as defined in the Global key access store and Component Data Catalog, and test extensively to verify and validate that the expected outcomes were achieved.
  • Adhere to Enterprise/Component DRM policies.
  • Leverage vendor recommendations.
  • Test system integration and compatibility.
• Develop automation playbooks for policy enforcement.

Encrypt sensitive data:

• Implement and deploy a strong and vetted Key Management System (KMS) to restrict access to encryption keys only to approved identities.
• Enable encryption of sensitive data located on servers, databases, cloud storage, data repositories, and endpoint devices; leverage updated security protocols (e.g., Hypertext Transfer Protocol Secure (HTTPS), Transport Layer Security (TLS), etc.) to protect data either at rest or in transit.
  • Key management
  • Encryption keys
  • End-to-End Encryption
  • Watermarking

Implement protection mechanisms:

• Apply fine-grained permissions to high-risk data assets and enable DRM protection-based access control to only allow approved Users and identities.
  • Multi-Factor Authentication (MFA)
  • Attribute-Based Access Control (ABAC)
  • Data Loss Prevention (DLP)

Review data privacy protection under DRM policies:

• Review all applicable data privacy guidelines to maintain established Personal Identity Verification (PIV) requirements. Leverage industry best practices, such as Federal Information Processing Standards (FIPS) 201, to verify and validate compliance.

Enhance existing Identity and Access Management (IAM) policies to verify, validate, and automate DRM enforcement:

• Review IAM access control policies to verify and validate the capability to automatically grant or revoke license rights based on User/Person Entity (PE)/Non-Person Entity (NPE) attributes, roles, permissions, behavior, and data sensitivity levels.

Ensure data is encrypted:

• Verify and validate that high-risk data objects are encrypted in a manner that meets Enterprise/Component data steward requirements.

Test operational impacts of DRM implementation:

• Test to ensure Component operations are acceptable/sustainable under DRM implementation on high-risk data objects.
• Establish a performance baseline after the DRM solution is implemented.
• Verify and validate that activity/events are ingested and actioned by Component Visibility and Analytics and/or Automation and Orchestration solutions.

Track and monitor data usage:

• Enable tracking of illegal, unapproved distribution of proprietary and sensitive data assets.
• Leverage geo-location to enforce data access restrictions to safeguard critical data assets.
• Enable access logs monitoring to track content and approved device management.
• Review device binding and offline access authorization in compliance with mission requirements.
• Verify and validate that activity/events are ingested and actioned by Component Visibility and Analytics and/or Automation and Orchestration solutions.

Leverage existing DRM policies:

• Review Enterprise and Component guidelines on DRM policies and data taxonomy and ensure compliance adherence.

Review data protection mechanisms:

• Develop and enforce data asset protection to help safeguard sensitive data across the entire Component environment. Leverage Policy Decision Points (PDPs), Policy Enforcement Points (PEPs), and Access Control policies to perform critical asset mapping.

Extend the Component DRM solution:

• Leverage the Component implemented DRM solution, from Activity 4.5.1 (Phase One) – Implement Data Rights Management (DRM) and Protection Tools Part 1.

Asset alignment and license management:

• Maintain audit trails of all data assets activities based on predefined rules and actions. Implement and secure system logs to enable forensic analysis. Deploy a centralized licensing server to manage, verify, and validate licenses.
• DRM Policy Enforcement: Implement mechanisms to enforce DRM policies, restricting access, usage, and distribution based on license terms.
• Secure Key Management: Implement a robust system for generating, storing, and distributing decryption keys, ensuring only authorized users and devices can access protected content.
• License Validation: Deploy a centralized licensing server to manage, verify, and validate licenses, preventing unauthorized access and usage.
• Audit Trails (DRM-Specific): Maintain audit trails of all DRM-related activities, including license requests, key access, and policy violations.
• Real-time Monitoring (DRM-Specific): Monitor DRM system activity for suspicious behavior and potential policy breaches.
  • License Expiration & Revocation: Implement automated license expiration and revocation mechanisms.

Implement the DRM solution:

• Deploy the DRM solution on all data and test extensively to verify and validate that the expected outcomes were achieved.
  • Adhere to Enterprise/Component DRM policies.
  • Leverage to vendor recommendations.
  • Test system integration and compatibility.
• Develop automation playbooks for policy enforcement.

Encrypt data:

• Implement and deploy a strong and vetted Key Management System (KMS) to restrict access to encryption keys only to approved identities.
• Enable encryption on all data located on servers, databases, cloud storage, data repositories, and endpoint devices; leverage updated security protocols (e.g., Hypertext Transfer Protocol Secure (HTTPS), Transport Layer Security (TLS), etc.) to protect data either at rest or in transit.
  • Key management
  • Encryption keys
  • End-to-end encryption
  • Watermarking

Implement protection mechanisms:

• Apply fine-grained permissions on all data assets and enable DRM protection-based Access Control to only allow approved Users/Person Entities (PEs)/Non-Person Entities (NPEs). Ensure the following solutions are implemented:
  • Multi-Factor Authentication (MFA)
  • ABAC
  • Data Loss Prevention (DLP)

Ensure data is encrypted:

• Verify and validate all data objects are encrypted in a manner that meets Enterprise/Component data steward requirements.

Test operational impacts of DRM implementation:

• Test to ensure Component operations are acceptable/sustainable under DRM implementation on high-risk data objects.
• Establish a performance baseline after the DRM solution is implemented.
• Verify and validate activity/events are ingested and actioned by Component Visibility and Analytics and/or Automation and Orchestration solutions.

Enforce User and Entity Behavior Analytics (UEBA) and continuous monitoring:

• Implement DRM solutions compatible with continuous monitoring, leveraging UEBA to automatically enforce DRM policies, trigger alerts, and DLP for suspicious activity.

Automate content encryption:

• Leverage Application Programming Interfaces (APIs) and plugins for seamless application integration between DRM solutions and Content Management Systems (CMSs) to enable automatic encryption and packaging of content at creation and upon collection

Automate audit logging and alerting:

• Build and implement workflows and playbooks to automatically alert and trigger data security, mitigating countermeasures such as DLP, license monitoring, and API-driven data rights access management.

Track and monitor data usage:

• Continuously verify and validate access log monitoring to track content and approved device management.
• Verify and validate activity/events are ingested and actioned by Component Visibility and Analytics and/or Automation and Orchestration solutions.

Review Enterprise standards for data Access Control and protection:

• Leverage the Global Key Access Store as the centralized tag repository/single source of truth for all tags, from Activity 4.3.1 (Phase One) – Implement Data Tagging and Classification Tools.
• Leverage the Component data access policy, from Activity 4.4.2 (Discovery) – Data Rights Management (DRM) Enforcement Point Logging and Analysis.
• Review and update the Component data access policy to align with existing Enterprise data Access Control standards and industry best practices, as applicable.
• Review, verify, and validate legal and regulatory compliance requirements as well as mission-specific security data protection mechanisms.
• Review, verify, and validate broader alignment with Enterprise data governance, Attribute-Based Access Control (ABAC) policies, and digital modernization strategy.
• Ensure that the existing DRM solution complies with relevant data protection regulations (e.g., General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), etc.).

Leverage existing data tagging standards, from Activity 4.2.1 (Phase One) – Define Data Tagging Standards and Activity 4.3.2 (Phase Two) – Manual Data Tagging Part 1, to enhance DRM policy enforcement:

• Configure DRM solutions to automatically enforce policies based on data tags.
• Ensure DRM protects the data at rest, in transit, and during usage based on the data tag’s restrictions and ABAC policies.
• Collaborate with data owners to enforce standardized data tagging schemes (e.g., classification, license rights, metadata, etc.).

Review and refine ABAC's existing policies:

• Enforce granular Access Control and time-based access restrictions tied to data asset tags (e.g., view, edit, copy, save, print, etc.).
• Leverage existing ABAC policies to effectively tailor DRM enforcement and compliance while improving the User/Person Entity (PE) experience.

Optimize contextual DRM policy enforcement through metadata:

• Configure DRM solutions and tools to align with contextual enforcement based on User/PE attributes and data asset characteristics.
• Enforce data tagging integration into data protection mechanisms for DRM compliance and loss prevention.

Develop playbooks to automate tag-based DRM policies:

• Leverage the existing digital asset tags with relevant metadata information to create a policy engine with a predefined set of rules capable of translating data tag information into DRM actions.
• Review and enforce compliance requirements and acceptance criteria for the protection of copyrighted data and sensitive material.

Enforce data encryption at rest, from Activity 4.5.2 (Phase Two) – Implement Data Rights Management (DRM) and Protection Tools Part 2:

• Leverage existing Enterprise standards to enforce encryption across entire repositories or specific files, as appropriate.
• Use hardware-based encryption for physical assets (e.g., disk encryption, secure storage hardware, etc.).

Enforce secure key management:

• Use a centralized Key Management System (KMS) to generate, store, and rotate encryption keys.
• Enforce policies for key lifecycle management (e.g., expiration, revocation, etc.).
• Protect keys with Hardware Security Modules (HSMs) or secure cloud KMS solutions.

Leverage analytics to detect DRM policy violations through data usage tracking:

• Combine User and Entity Behavior Analytics (UEBA) and Security Information and Event Management (SIEM) solutions to monitor digital assets, User/PE behavior, and access patterns to identify potential violations of copyrighted data usage.
• Enforce IR orchestration into the data security protection scheme to proactively act on anomaly detection, such as sudden spikes in downloads, restricted geolocation, or compromised identities.
• Enforce Representational State Transfer Application Programming Interface (REST API) integration for DRM enforcement of cloud-based data assets and repositories.

Enforce continuous monitoring and auditing:

• Enforce real-time alerting for critical DRM violations on sensitive data assets.
• Enforce regular monitoring and auditing of adherence to DRM-approved policies.

Enforce data-driven DRM compliance:

• Enforce a comprehensive log collection of the DRM system, including access requests, license usage, and policy enforcement points.
• Centralize and aggregate all tags and metadata information relevant to digital data assets to develop a system baseline for an approved and acceptable use policy.

Enforce logging and real-time alerting:

• Enable logging for repository access and data operations.
• Use real-time monitoring tools to detect unapproved or suspicious activity.
• Regularly review access logs and audit reports for anomalies.

Loss activity coordination:

• Review the Component DLP policy, established in Activity 4.4.1 (Discovery) – Data Loss Prevention (DLP) Enforcement Point Logging and Analysis, and enhance it with specific Data Privacy and Protection (DPP) alignment, ensuring coordination with Incident Response (IR)/Cybersecurity service providers.
  • DPP alignment includes policies, practices, and technologies implemented by Components to safeguard personal data from unauthorized access, use, or disclosure.
• Extend the existing policy actions for handling loss activity (e.g., detection, coordination, response) by adding privacy-specific considerations, such as:
  • Privacy impact assessment requirements.
  • Data subject notification procedures.
  • Privacy-enhancing technologies implementation criteria.
  • Cross-border data transfer restrictions.
• Develop attribute injection frameworks that identify, enable, and address:
  • Data provenance tracking across ZT control boundaries.
  • Privacy classification metadata preservation during data movement.
  • Contextual privacy requirements based on data location and use.
• Leverage enforcement points and analyze additional DPP-specific requirements, established in Activity 4.4.1 (Discovery) – Data Loss Prevention (DLP) Enforcement Point Logging and Analysis, such as:
  • Privacy-specific data filtering requirements.
  • Encryption requirements for different data classifications.
  • Obfuscation needs for sensitive personal information.
  • Consent verification mechanisms at enforcement boundaries.
• Develop attribute-based enforcement specifications that define:
  • How data origin attributes affect protection requirements.
  • How movement across boundaries triggers specific protections.
  • When encryption or obfuscation measures should be invoked
  • What privacy metadata must be preserved during transfers.

Deploy decision points:

• Evaluate and enforce access requests against predefined access control policies using Identity Access Management (IAM) policies, device posture, and Data, Applications, Assets, and Services (DAAS) context to authorize system resource access. Enable effective policy enforcement by:
  • Automating policy orchestration.
  • Leveraging a centralized policy repository.
  • Continuously evaluating access policies for accuracy and effectiveness.
• Extend the evaluation capabilities, from Activity 4.4.1 (Discovery) – Data Loss Prevention (DLP) Enforcement Point Logging and Analysis, to incorporate privacy-specific decision factors by:
  • Using IAM policies from existing systems.
  • Incorporating device health assessments from existing monitoring.
  • Adding DAAS information.
• Enhance policy orchestration by:
  • Integrating privacy requirements into the decision framework.
  • Adding attribute-based privacy controls to the decision logic.
  • Implementing privacy impact assessment triggers at boundaries.
  • Creating privacy-specific enforcement actions.
• Leverage the centralized policy repository, from Activity 4.4.1 (Discovery) – Data Loss Prevention (DLP) Enforcement Point Logging and Analysis, to add the following, as applicable:
  • Privacy regulation compliance requirements
  • Data subject rights enforcement rules
  • Purpose limitation constraints
  • Jurisdictional privacy requirements

Establish enforcement points:

• Build upon the enforcement point identification, from Activity 4.4.1 (Discovery) – Data Loss Prevention (DLP) Enforcement Point Logging and Analysis, by implementing enhanced capabilities through:
  • Configuring enforcement points to recognize privacy-related attributes.
  • Enabling attribute-based privacy protections at boundaries.
  • Implementing encryption and obfuscation capabilities.
  • Deploying advanced DLP monitoring with privacy awareness.
• Enhance the rule-based engine with specific privacy protection capabilities:
  • Content-aware privacy rule evaluation
  • Context-sensitive privacy enforcement
  • Attribute-based privacy decisions
  • Regulatory compliance validation
• Implement enhanced enforcement policies that:
  • Apply appropriate protection measures based on privacy attributes.
  • Enforce consistent privacy controls across similar data types.
  • Adapt privacy protections based on context and use.
  • Address specific regulatory requirements for different jurisdictions.
• Evaluate decision access policies and enforce policy-based decisions. Communicate with Policy Decision Points (PDPs) to consistently enforce policies, such as:
  • Enable a rule-based engine.
  • Implement enforcement policies.
  • Establish real-time communication.

Implement DLP solutions:

• Leverage the in-scope enforcement points defined earlier and implement DLP in "Monitor Only" mode to:
  • Gather baseline data on privacy impacts before enforcement.
  • Document potential privacy issues without disrupting operations.
  • Test privacy attribute handling across boundaries.
  • Ensure alignment with organizational Componential security policies and compliance requirements.
• Ensure alignment with Component security and privacy policies by:
  • Monitoring privacy impact indicators.
  • Validating privacy protection effectiveness.
  • Identifying potential privacy compliance gaps.
• Configure the DLP solution to specifically monitor key aspects, such as:
  • Protection measure application based on data attributes.
  • Privacy metadata preservation during transfers.
  • Encryption and obfuscation effectiveness.
  • Privacy boundary crossing events.

Enhanced cyber collaboration:

• Building on the collaboration foundations establish specific processes for privacy incidents, for example:
  • Specialized privacy breach reporting workflows.
  • Privacy-focused incident classification criteria.
  • Privacy Subject Matter Expert (SME) engagement protocols.
  • Privacy regulatory notification requirements.
• Create privacy-enhanced collaborative analysis procedures, such as:
  • Privacy impact assessment integration
  • Data subject impact analysis methods
  • Regulatory compliance evaluation

Establish baselines:

• Conduct an assessment and develop baseline profiles for acceptable use policy, approved security posture, and vulnerability management.
• Leverage the assessment approaches, from Activity 4.4.1 (Discovery) – Data Loss Prevention (DLP) Enforcement Point Logging and Analysis, to develop privacy-specific baseline profiles, such as:
  • Privacy-focused acceptable use patterns.
  • Normal privacy attribute handling behaviors.
  • Typical privacy boundary crossing patterns.
  • Expected privacy protection measure application.
• Develop privacy-enhanced behavior profiles accounting for:
  • Privacy-compliant access patterns.
  • Proper handling of privacy-sensitive data.
  • Appropriate application of privacy controls.
• Implement privacy-aware anomaly detection that identifies:
  • Unusual privacy attribute modifications.
  • Unexpected privacy boundary crossings.
  • Abnormal privacy protection bypass attempts.
  • Potential privacy compliance violations.
• Leverage historical system events and logs to define what is an approved normal system and User/Person Entity (PE)/Non-Person Entity (NPE) behavior.
  • Develop approved behavior profiles.
  • Implement anomaly detection.
  • Develop data as a service expected baselines.

Continuously assess DLP solutions:

• Implement comprehensive validation measures that focus specifically on privacy enhancements, such as:
  • Privacy attribute preservation testing.
  • Privacy protection effectiveness verification.
  • Privacy boundary control validation.
  • Privacy regulatory compliance assessment.
• Conduct regular assessments to fine-tune enforcement rules.
• Ensure minimal operational impact while maintaining data security.
• Establish real-time communication between enforcement points and policy management systems.

Enterprise cyber enforcement indicators:

• Coordinate with the Enterprise to identify the minimum indicator standards supporting DLP.
  • Review Enterprise security directives and privacy requirements.
  • Map indicator standards to Component's data environment.
  • Identify any gaps between Component capabilities and Enterprise standards.
  • Develop indicator implementation roadmap aligned with Enterprise requirements.
• Extend the Component DLP policy to include the Enterprise requirements that:
  • Incorporate Enterprise-defined indicator standards.
  • Define specific enforcement triggers based on indicators.
  • Establish thresholds for different enforcement actions.
  • Create data tag-to-enforcement action mappings.
• Develop testing criteria to verify and validate the enforcement functionality within the Component environment.
  • Develop test scenarios for each enforcement action and data type.
  • Create validation criteria for successful enforcement.
  • Establish performance impact assessment methodologies.
  • Define acceptable operational thresholds for enforcement actions.

Test DLP enforcement:

• Where possible, test DLP enforcement policies in a controlled/development environment to limit potential negative operational impacts. If a testing environment cannot be utilized, consider a limited rollout of the capability to a small subset of test Users/Person Entities (PEs) and Data, Applications, Assets, and Services (DAAS).
• Implement a phased testing approach that evaluates:
  • Tag recognition accuracy across enforcement points.
  • Enforcement action is appropriate for different scenarios.
  • System performance under various enforcement loads.
  • User/PE experience impact across different enforcement types.
• Verify and validate that the DLP enforcement actions align with the Enterprise standards and Component DLP policy.
• Ensure activity/events are captured in logging in accordance with the logging standards, from Activity 7.1.2 (Phase One) – Log Parsing.
• Verify and validate that activity/events are ingested and actioned by Component Visibility and Analytics and/or Automation and Orchestration solutions.

Implement DLP enforcement:

• Leveraging a phased approach, develop a strategic enforcement transition plan that:
  • Prioritizes data categories based on criticality and sensitivity.
  • Establishes a phased implementation schedule.
  • Defines success criteria for each implementation phase.
  • Creates rollback procedures for enforcement issues.
  • Includes communication plans for affected stakeholders.
• Prioritize data with a higher level of criticality/sensitivity, as defined in the Data Catalog from Activity 4.1.1 (Discovery) – Data Analysis.
  • Begin with highest-risk data categories.
  • Apply enforcement to clearly defined, high-value assets first.
  • Expand to broader data categories in measured phases.
  • Add complexity to enforcement rules incrementally.

Manage exceptions:

• Systems/data incompatible with DLP enforcement are:
  • Identified
  • Documented
  • Approved/Rejected
• The Enterprise and/or Component determines risks.
• Approval is granted when justification for the exception outweighs the risks to the Enterprise/Component.
• Approval is periodically reassessed.

Verify and validate DLP enforcement:

• Expand the verification and validation approach, from Activity 4.6.1 (Phase One) – Implement Enforcement Points, to ensure compliance with Enterprise standards:
  • Conduct comprehensive enforcement coverage assessments.
  • Verify and validate alignment with Enterprise indicator requirements.
  • Verify and validate enforcement consistency across all DAAS components.
  • Test edge cases and boundary conditions.
• Verify and validate DLP enforcement is established across all DAAS. Leverage:
  • Component Master Device Inventory, from Activity 2.1.1 (Discovery) – Device Health Tool Gap Analysis
  • Component Master Application Inventory, from Activity 3.1.1 (Discovery) – Application and Code Identification
  • Component Data Catalog, from Activity 4.1.1 (Discovery) – Data Analysis
• Verify and validate DLP enforcement meets the requirements established by the Component.
• Verify and validate that the DLP enforcement actions align with the Enterprise standards and Component DLP policy.
• Ensure activity/events are captured in accordance with the logging standards, from Activity 7.1.2 (Phase One) – Log Parsing.
• Verify and validate activity/events are ingested and actioned by Component Visibility and Analytics and/or Automation and Orchestration solutions.

Conduct stakeholder engagement:

• Establish a governance structure of clear roles and responsibilities for ensuring DAAS compliance with Software-Defined Storage (SDS) requirements.
• Identify stakeholders and assign accountability for policy implementation, compliance monitoring, and enforcement.

Define objectives and scope:

• Identify Enterprise-defined Access Control requirements for DAAS management.
• Identify any existing Component policies to align with or build upon.
• Define the scope of the Component DAAS policy.

Develop a policy framework and governance model:

• Define governance structures, roles, and responsibilities for managing DAAS policy.
• Establish policy controls for data security, asset management, Access Control, and compliance monitoring.

Identify Component DAAS to collect requirements:

• Component Master User Inventory, from Activity 1.1.1 (Discovery) – Inventory User.
• Component Master Device Inventory, from Activity 2.1.1 (Discovery) – Device Health Tool Gap Analysis.
• Component Master Application Inventory, from Activity 3.1.1 (Discovery) – Application and Code Identification.
• Component Data Catalog, from Activity 4.1.1 (Discovery) – Data Analysis.

Draft policy document:

• Create a formal Component DAAS policy document detailing objectives, scope, roles, standards, and processes.
• Ensure the policy addresses all relevant aspects:
  • Data management
  • Asset protection
  • Application security
  • Service continuity
• Ensure the policy aligns with the Component SDS policy from Activity 4.2.3 (Phase Two) – Develop Software-Defined Storage (SDS) Policy, particularly regarding storage management, data security, and compliance standards.

Conduct risk assessment and impact analysis:

• Perform a risk assessment to identify potential vulnerabilities and impacts of DAAS components.
• Update the policy based on identified risks to mitigate key security and operational concerns.

Identify existing access control mechanisms:

• Leverage the approval gateways, from Activity 3.4.1 (Phase One) – Resource Authorization Part 1.
• Leverage the SDN Application Programming Interfaces (APIs), from Activity 5.2.2 (Phase One) – Implement Software-Defined Networking (SDN) Programmable Infrastructure.
• Leverage authentication decision points and implement segmentation gateways, from Activity 5.2.2 (Phase One) – Implement Software-Defined Networking (SDN) Programmable Infrastructure.
• Determine if the existing access control mechanisms meet the PEP/PDP needs of the Enterprise/Component DAAS policy.
• Define specific control mechanisms to enforce compliance, such as Access Controls, encryption, and data monitoring within both DAAS and SDS frameworks.
• Ensure these mechanisms address SDS requirements for data security, privacy, and storage management.

Pilot and test the policy enforcement:

• Deploy PEPs/PDPs.
• Conduct a pilot implementation to evaluate the effectiveness of the DAAS policy.
• Gather feedback from stakeholders and adjust policy details as needed.

Implement DAAS Access Control:

• Officially publish the DAAS policy across relevant entities and ensure consistent enforcement.

Implement PEPs:

• Enable PEPs to monitor and enforce DAAS policies in line with SDS requirements.
• Enable PDPs to interpret and apply rules specified in DAAS policies.
• Configure PEPs and PDPs to automatically detect, log, and respond to non-compliance with DAAS and SDS policies

Verify and validate DAAS policy enforcement:

• Test, verify, and validate that DAAS is accessible and operational requirements have been maintained.
• Test, verify, and validate that DAAS has the minimum necessary access in accordance with Component DAAS policy.

Monitor, review, and update policy:

• Continuously monitor the policy’s effectiveness and alignment with Enterprise goals.
• Review and update the DAAS policy periodically based on emerging threats, technology advancements, and Enterprise requirements.

Develop compliance monitoring and reporting processes:

• Define continuous monitoring processes for tracking compliance with DAAS and SDS requirements.
• Establish reporting mechanisms to provide visibility into compliance statuses, such as Security Information and Event Management (SIEM) with dashboards, alerts, and periodic reports.

Automate compliance checks and audits:

• Implement automated compliance tools to assess DAAS policy adherence to SDS requirements regularly.
• Schedule periodic audits to verify and validate compliance and identify gaps requiring remdiation

Implement incident management and remediation processes:

• Establish Incident Response (IR) and remediation processes for non-compliance instances with DAAS and SDS policies.
• Define escalation paths and corrective actions to address policy violations, ensuring swift alignment with SDS standards.

Review, update, and refine governance mechanisms:

• Periodically review governance mechanisms to ensure ongoing compliance with evolving DAAS and SDS policy requirements.
• Update governance practices as needed to address new storage technologies, threats, or regulatory changes.

Report and review compliance status with key stakeholders:

• Regularly report compliance status to governance bodies and stakeholders, providing insights into DAAS alignment with SDS.
• Use stakeholder feedback to enhance and strengthen compliance mechanisms over time.

Conduct stakeholder engagement:

• Leverage Component DAAS policy governance stakeholders, from Activity 4.7.1 (Phase Two) – Integrate Data, Applications, Assets, and Services (DAAS) Access with Software-Defined Storage (SDS) Policy Part 1.

Understand Enterprise requirements:

• Reassess Enterprise-defined control requirements.
• Reassess Component DAAS policy requirements.
• Leverage Component IdP, integrated with the Enterprise, from Activity 1.9.1 (Phase Two) – Enterprise Public Key Infrastructure (PKI) and Identity Provider (IdP) Part 1, which manages Users/Person Entities (PEs)/Non-Person Entities (NPEs), after Activity 2.1.3 (Phase Two) – Enterprise Identity Provider (IdP) Part 1.
• Leverage Component-defined interoperability standards, from Activity 4.2.2 (Phase One) – Interoperability Standards.

Develop DAAS and User/PE/NPE attribute integration plan:

• Ensure the IdP can access an attribute repository where User/PE/NPE data and access attributes are stored.
• Map the required attributes (e.g., User/PE/NPE role, location, access level, etc.) from the attribute repository to the IdP. This allows the IdP to leverage these attributes during authentication and approval.
• Define the metadata configuration for each attribute, specifying how attributes are structured and the values allowed.

Enable logging and monitoring for governance:

• Enable logging in the IdP to track when and how Access Control and location-based attributes are used. This can include:
  • Monitoring User/PE/NPE access logs to identify who is accessing specific data, from where, and using which attributes.
  • Tracking policy enforcement logs of access control policies, including access denials or Multi-Factor Authentication (MFA) triggers based on User/PE/NPE attributes. Location-based access control will be implemented as a component of the overall access control policy, leveraging Attribute-Based Access Control (ABAC) principles.
• Monitor and document anomalies. Incorporate identified anomalies when implementing Security Information and Event Management (SIEM) solutions, to detect unusual access patterns such as attempts to access sensitive data from unapproved locations.

Implement continuous monitoring and updates:

• Review and update Access Control and data location policies regularly based on changes in regulatory requirements, business needs, and threat landscapes.
• Ensure the attributes in the IdP are continuously synchronized with the authoritative data source to reflect any changes in roles, clearance levels, or locations.
• Perform periodic compliance audits to ensure Access Control mechanisms align with regulatory requirements for data location.

Pilot and test interoperability and policy enforcement:

• Test integration and interoperability between Data Loss Prevention (DLP), Data Rights Management (DRM), storage infrastructure, and the IdP, simulating different scenarios to ensure smooth communication, identity verification and validation, and policy enforcement.
  • Test different roles, locations, and access levels to consistently verify and validate that appropriate Access Control and data protection measures are applied.
• Simulate data leakage and/or data exfiltration scenarios to ensure that the DLP system is effectively preventing unapproved data sharing or transfer based on User/PE/NPE attributes.
• Simulate and test various access scenarios to ensure Access Control policies function as intended.
• Verify and validate access logs and monitoring to ensure audit trails capture all relevant access details, including which attributes were used in policy decisions.

Establish interoperability with cloud and on-premise solutions:

• Ensure interoperability between on-premises storage solutions and cloud-based DLP and DRM systems.
  • Implement Application Programming Interface (API) Integration between cloud services and on-premise DLP/DRM solutions to synchronize data protection and access policies across both environments.
  • Use Cloud Access Security Brokers (CASBs) to enforce consistent DLP and DRM policies across cloud environments, leveraging the IdP for authentication and Access Control.
• Cloud storage integration: If the Component uses cloud storage solutions, ensure that DLP and DRM solutions are integrated with cloud-based storage to maintain secure data transfers, encryption, and compliance with Enterprise security policies.

Implement IdP-integrated ABAC:

• Using a phased approach, deploy/enforce the IdP-integrated ABAC solution.

Verify and validate auditing and monitoring across systems:

• Ensure audit logs comply with Enterprise/Component logging standards, from Activity 7.1.2 (Phase One) – Log Parsing.
• Verify and validate integration with Component solutions from the Visibility and Analytics and/or Automation and Orchestration Pillars.

Provide continuous monitoring and updates:

• Conduct regular audits to verify and validate that the interoperability between systems is functioning effectively and that policies are being enforced consistently across the Component environment in accordance with Enterprise requirements.