Network Sensitive Data Risk Score Calculation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer network security solutions fail to effectively identify and protect sensitive data from compromise, as they lack the ability to assess the security risk of data stored on computers within a network, despite being capable of detecting malware and misconfigurations.
Innovation Solution
Deploying agents on network computers to collect data about the type and security of stored data, calculating a risk score based on potential loss impact and probability, and remediating high-risk computers through techniques like data encryption, relocation, or deletion to minimize exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive data scanning is performed to identify sensitive data, then data security coverage is improved, but system complexity increases
Solution Approach 1:
The system segments the security assessment task by deploying separate agent components on individual computers throughout the network. Each agent independently scans for sensitive data types (SSNs, credit card numbers, bank account numbers, etc.) and security controls on its host system, then reports findings to a central server. This segmentation allows comprehensive network-wide coverage while distributing system complexity across multiple independent units rather than requiring a monolithic scanning system.
2Reliability
If security controls are implemented to prevent unauthorized access, then data protection is improved, but system performance deteriorates
Solution Approach 1:
The system implements self-service security assessment by deploying agents that autonomously scan their host computers for sensitive data and evaluate local security controls without requiring continuous external intervention. The agents automatically detect the presence of sensitive information types, assess configured security controls (antivirus, firewalls, encryption), and generate risk scores based on their findings. This self-service approach enables continuous security monitoring while minimizing performance impact compared to centralized scanning of every system.
3Measurement precision
If detailed security assessment is conducted on each computer, then risk identification accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary action by having agents continuously monitor and pre-assess security conditions on each computer, maintaining an up-to-date profile of sensitive data presence and security control status. When a risk assessment is needed, the system utilizes these pre-collected data to rapidly generate accurate risk scores without requiring time-consuming on-demand scanning. The agents continuously track changes in security controls and data presence, so the central server receives timely updates and can quickly re-evaluate risks when conditions change.
Data Source
AI summary
Systems and methods for sensitive data remediation include calculating a Probability of Loss of data on a given computer based on measures of control, integrity, and potential avenues of exploitation of the given computer, determining an Impact of Loss of the data on the given computer based on a type, volume, and nature of the data, and correlating the Probability of Loss with the Impact of Loss to generate a risk score for the given computer that can be compared to other computers in the network. The computers with higher risk scores can then be subjected to data remediation activity.