Internal Data Breach Detection via Access Pattern Analysis
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Solution Overview
Problem
Internal data breaches are difficult to detect and prevent as malicious users, who are authorized to access information, can impersonate others by combining data they are allowed to access, making it challenging for businesses to combat and prevent such breaches.
Innovation Solution
A security tool analyzes database queries from internal users to identify deviations from established access patterns, determines security risks, and takes remedial action by preventing access when risks exceed a threshold, thereby detecting and preventing internal data breaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If internal users are authorized to access information they need for their jobs, then their work efficiency is improved, but they may combine authorized data to impersonate others and cause internal data breaches
Solution Approach 1:
The system performs preliminary analysis of user access patterns and data combinations before a breach occurs. By establishing baseline access patterns and analyzing potential data combinations in advance, the system can predict and prevent internal data breaches before they happen, while still allowing normal authorized access to proceed smoothly.
Solution Approach 2:
The system continuously monitors user access patterns and provides feedback by comparing actual access behavior against established baselines. When anomalies are detected (such as unusual data combinations or access patterns), the system triggers alerts or automated responses, creating a closed-loop feedback mechanism that maintains security without significantly impacting productivity.
2Reliability
If traditional security measures are used to prevent unauthorized access, then external data breaches are reduced, but internal data breaches by authorized users remain undetected
Solution Approach 1:
Instead of focusing on preventing unauthorized access from the outside, the system inverts the approach by monitoring authorized access patterns from the inside. It analyzes what authorized users are accessing and how they are combining data, looking for anomalies in authorized behavior that indicate potential internal breaches, rather than trying to block unauthorized access.
Solution Approach 2:
The system introduces an intermediary layer between users and data that monitors and analyzes access patterns without blocking normal operations. This intermediary analyzes the combination of data accessed by users and compares it against expected patterns, detecting internal breaches by analyzing the nature of access rather than by blocking access.
3Difficulty of detecting and measuring
If monitoring of all user access is implemented to detect internal data breaches, then detection capability is improved, but system complexity and processing overhead increase significantly
Solution Approach 1:
The system applies local quality by focusing monitoring efforts on specific high-risk data combinations and access patterns rather than uniformly monitoring all user access. It identifies and prioritizes critical data elements and their potential combinations, applying enhanced monitoring only where the risk of internal breach is highest, thereby reducing overall system complexity.
Solution Approach 2:
The system uses partial action by monitoring only the most critical data combinations and access patterns rather than all possible user activities. It focuses on analyzing combinations of data that would enable impersonation or significant harm, using partial monitoring of key indicators rather than exhaustive monitoring of all user behavior.
4Object-affected harmful factors
If access restrictions are imposed on internal users to prevent data breaches, then security is improved, but user productivity and access to needed information decrease
Solution Approach 1:
The system applies dynamics by making access control adaptive rather than static. Instead of imposing fixed restrictions, it dynamically adjusts monitoring and control based on real-time analysis of user behavior patterns, data sensitivity, and risk levels. Access is granted normally under typical conditions but automatically restricted or flagged when anomalous patterns are detected.
Data Source
AI summary
A security tool analyzes database queries of internal users to determine if these users accessed information in a manner that deviates from established and/or expected access patterns. The security tool can also analyze the behavior of a group of internal users to determine whether that behavior deviates from expected access patterns. The security tool then determines security risks corresponding to the users' access. If any of the security risks exceeds a threshold, then the security tool takes remedial action (e.g., preventing one or more users from accessing information). In this manner, internal data breaches can be detected and prevented. Additionally, in certain embodiments, an internal data breach can be prevented before the internal data breach even occurs.