Security Policy Ranking for Sensitive Data Exfiltration Risk
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Solution Overview
Problem
Existing cybersecurity measures struggle to effectively identify and mitigate risks of sensitive data exfiltration, particularly in dynamic environments, often failing to prioritize and address vulnerabilities proactively.
Innovation Solution
A method involving a managed computing system that uses an anomaly detector and security policy generator to proactively and automatically calculate risk scores, generate security policies, and configure systems based on anomaly scores, leveraging machine learning for accurate risk assessment and policy recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple security mechanisms are implemented to detect and prevent cyberattacks, then security coverage and detection capability are improved, but system complexity and implementation cost increase
Solution Approach 1:
The patent segments security mechanisms into different types (anomaly detection, data classification, policy generation) and assigns them to specific components (anomaly detector, classifier, policy generator). This modular segmentation allows each component to handle specific security tasks independently, improving overall security coverage while managing system complexity through structured organization.
Solution Approach 2:
The managed computing system implements a universal security framework where a single integrated system performs multiple security functions including anomaly detection, data classification, risk assessment, and policy generation. This multi-functional approach consolidates what would otherwise require multiple separate security mechanisms, improving security coverage while reducing redundant complexity.
2Measurement precision
If security personnel manually analyze and respond to security risks, then response accuracy and customization are improved, but time consumption and labor requirements increase
Solution Approach 1:
The system implements self-service through automated anomaly detection and policy generation. The anomaly detector automatically identifies security anomalies, the classifier automatically categorizes documents and data, and the policy generator automatically creates security policies without requiring manual intervention from security personnel. This automation maintains high response accuracy while significantly reducing time consumption and labor requirements.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computer-based system. Machine learning models and algorithms substitute for human analysts in detecting anomalies, classifying data, and generating policies. This substitution maintains or improves response accuracy through sophisticated algorithms while eliminating the time consumption and labor intensity inherent in manual processes.
3Speed
If proactive security policy generation is implemented, then security response speed and prevention capability are improved, but system resource consumption and computational load increase
Solution Approach 1:
The system performs preliminary actions by proactively generating security policies before attacks occur. The policy generator continuously analyzes system state, identifies potential risks, and creates preventive policies in advance. This proactive approach enables rapid response when threats materialize while distributing computational load through continuous background processing rather than intensive real-time reactions.
Solution Approach 2:
The patent employs parameter changes through dynamic risk scoring and anomaly threshold adjustment. The system monitors system parameters and adjusts security policy generation intensity based on current risk levels. During low-risk periods, computational resources are reduced; during high-risk periods, the system increases its monitoring and policy generation activity. This dynamic parameter adjustment optimizes the balance between response speed and computational load.
Data Source
AI summary
Some embodiments address technical challenges arising from efforts to identify and mitigate security risks, in particular but not only, risks that sensitive data will be exfiltrated. Some embodiments provide or utilize an anomaly detector which is configured to detect a security anomaly in data based on at least a distribution of sensitive information type documents in a collection of documents and classifications of documents by trainable classifiers based on machine learning. Some embodiments provide or utilize a security policy generator which is configured to proactively and automatically generate security policy recommendations, rank at least two of the security policy recommendations, and present at least one top-ranked generated security policy recommendation in a user interface. Some embodiments generate a security policy in a managed computing system based on at least an anomaly score, and then configure the managed computing system according to the generated security policy.