Security Posture Manager Risk Scoring Cloud DLP
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Solution Overview
Problem
Current data loss prevention (DLP) systems are overwhelmed by the sheer number of incidents in cloud infrastructures, especially with the migration of enterprise data, rendering them ineffective in managing data loss risks across different vectors such as data at rest, in-motion, and at endpoints, due to the increasing scope of data and privacy regulations.
Innovation Solution
A security posture manager is implemented to establish a baseline quantification of data loss risk for sensitive assets, combining static and dynamic scoring components to provide a comprehensive view of risk, including access activity over time and in-transit risks, thereby focusing on the highest-risk assets and tailoring risk assessments to specific contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional DLP systems monitor all data loss incidents in cloud infrastructures, then comprehensive security coverage is achieved, but system effectiveness deteriorates due to overwhelming incident volumes
Solution Approach 1:
The patent transforms the monitoring approach by changing the parameter of incident selection from comprehensive monitoring to risk-scored prioritization. A risk scoring system evaluates multiple parameters (data sensitivity, user behavior, destination risk, device posture) to assign priority levels, enabling security teams to focus on high-risk incidents while maintaining automated monitoring of all data flows.
Solution Approach 2:
The patent introduces an intermediary risk scoring mechanism between raw incident data and security team response. This intermediary layer processes incident information through multiple risk factors and generates prioritized incident lists, acting as a mediator that translates overwhelming raw data into actionable security intelligence.
2Reliability
If DLP systems monitor all data vectors (at rest, in-motion, at endpoint), then comprehensive data loss prevention is achieved, but system complexity increases
Solution Approach 1:
The patent implements a universal risk scoring framework that functions across all data vectors (at rest, in-motion, at endpoint) through a single prioritization mechanism. The same risk factor evaluation system adapts to different data states and transmission contexts, providing consistent incident prioritization without requiring separate complex monitoring systems for each vector.
3Measurement precision
If risk assessments are highly specific to context and requestors, then accuracy of risk evaluation improves, but computational overhead increases
Solution Approach 1:
The patent applies partial action by evaluating only the necessary risk factors for each incident context rather than performing exhaustive assessments. The system selectively applies risk factor evaluations based on incident type, data sensitivity level, and user role, performing detailed contextual analysis only when needed while using streamlined assessments for lower-risk scenarios.
Data Source
AI summary
A DLP system with ongoing risk assessment establishes a baseline quantification of data loss risk (“risk score”) of assets identified as sensitive assets and quantifies other dynamic factors as components to be combined or viewed with the baseline risk score. The baseline risk score provides an initial or static view of data loss risk for a sensitive asset at-rest and can be combined with other scoring components to provide different views of risk for a sensitive asset that represent more dynamic aspects. These scoring components relate to access activity over time or historical activity and in-transit activity. The baseline risk score with the dynamic risk scoring components provides a current view of risk and a trending or historical view of risk for the sensitive asset. The in-transit risk scoring component tailors risk assessment to a requestor to provide another perspective or contextualize risk with respect to the requestor.