Graph-Based Data Risk Governance for Traceable Control Mapping
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Solution Overview
Problem
Current systems face challenges in effectively managing data risk due to fragmented governance, unclear accountability, and lack of a consistent data risk taxonomy, leading to difficulties in aggregating, reconciling, and applying different methods for categorizing data, resulting in incomplete and subjective risk management.
Innovation Solution
A computer-implemented system utilizing an intelligent graph platform and data risk management engine that integrates a standardized data risk taxonomy, harmonized classification schema, and policy hierarchy to provide a unified approach for data risk management, enabling traceability, transparency, and systematic aggregation of data risk metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If organizations use fragmented governance and disparate policies to manage data risk, then they can accommodate diverse organizational needs, but it becomes difficult to aggregate, reconcile, and analyze data risk comprehensively
Solution Approach 1:
The system segments data risk management into standardized components (control objectives, policies, standards, risks) that can be independently managed and then aggregated. Each segment follows a consistent taxonomy and classification schema, enabling comprehensive aggregation across the organization while maintaining the ability to address diverse needs through configurable control frameworks.
Solution Approach 2:
The system creates a universal data risk management platform that serves multiple functions: classifying data assets, identifying controls, assessing risks, and generating reports. This multi-functional approach consolidates what would otherwise require multiple separate systems, reducing complexity while maintaining adaptability to diverse organizational requirements through configurable parameters.
2Adaptability or versatility
If organizations distribute data risk accountability across team members, then they can involve more stakeholders, but accountability becomes unclear and holistic management becomes challenging
Solution Approach 1:
The system implements feedback mechanisms that automatically assign and track data risk accountability. When data is classified and controls are identified, the system provides feedback to relevant stakeholders about their accountability obligations and the status of risk management activities. This continuous feedback loop maintains clear accountability while involving multiple stakeholders in the process.
Solution Approach 2:
The system enables self-service functionality where stakeholders can independently view their accountability assignments, access relevant controls and policies, and update risk information without requiring centralized coordination. This self-service approach maintains clear individual accountability while allowing broad stakeholder involvement through automated system interactions.
3Productivity
If organizations use subjective ratings and proxy metrics for data risk measurement, then they can provide quick assessments, but the measurements lack comprehensiveness and scalability
Solution Approach 1:
The system performs preliminary classification of data assets into standardized categories (public, internal, confidential, restricted) and pre-identifies relevant controls before formal risk assessment. This preliminary action enables rapid initial assessments while maintaining measurement precision through the structured taxonomy that ensures comprehensive coverage of risk factors.
Solution Approach 2:
The system transforms subjective risk ratings into objective parameters based on data classification, control coverage, and risk calculations. By changing the measurement parameters from subjective judgments to quantifiable metrics derived from standardized classifications and control inventories, the system achieves both speed and precision in risk measurement that scales across the organization.
4Productivity
If organizations deploy controls based on perceived risk reduction or asset value, then they can prioritize quickly, but they lack a systematic methodology that stands up to scrutiny
Solution Approach 1:
The system implements dynamic control deployment prioritization that automatically adjusts based on changing data classifications, risk assessments, and organizational requirements. The prioritization methodology is not static but dynamically responds to new information while maintaining systematic rigor through automated calculations based on standardized risk parameters and control effectiveness metrics.
Solution Approach 2:
The system replaces manual, ad-hoc control prioritization with an automated mechanical system that calculates priorities based on standardized risk parameters, control coverage, and organizational policies. This automated mechanics substitution provides both speed through algorithmic processing and reliability through transparent, auditable calculation methodologies that can be scrutinized and defended.
Data Source
AI summary
The invention relates to computer-implemented systems and methods for data risk management that provides traceability across governing artifacts which is key to driving effective data risk management and achieving compliance to policy, standards and controls. An embodiment of the present invention is directed to a standardized data risk taxonomy, harmonized classification schema, policy and standard hierarchy, control catalog and standard of care. These components facilitate an alignment to a full information lifecycle with a common definition of data risk, traceability across governing artifacts (e.g., obligations, policies, standards, risks, controls, etc.) and standardized catalogs with defined treatment across harmonized classification of assets.