AI Document Security with Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In shared-resource environments like cloud computing, organizations face challenges in managing and securing large volumes of documents, as existing security mechanisms are limited in determining appropriate access permissions and detecting anomalous behavior, leading to potential data breaches and theft.
Innovation Solution
Implementing a data loss prevention system that uses artificial intelligence and machine learning to analyze documents, assign risk scores, monitor user behavior, and detect anomalies by comparing user activity to peer group patterns, with features like topic modeling and recurrent neural networks to generate alerts for potentially risky access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification process is used to determine document context and access permissions, then security accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables documents to self-classify and self-manage their security context through automated topic modeling and behavior analysis. The topic model automatically determines document contexts without human intervention, and the behavior analyzer autonomously monitors user access patterns, eliminating the need for manual classification while maintaining high security accuracy.
Solution Approach 2:
The patent replaces manual classification processes with automated machine learning systems. The topic model uses natural language processing to automatically classify documents by content, and the behavior analyzer uses pattern recognition to monitor user access, substituting human operational complexity with automated computational mechanisms.
2Reliability
If automated security monitoring is implemented to detect anomalous behavior, then security coverage is improved, but false positives increase
Solution Approach 1:
The behavior analyzer dynamically adapts security monitoring by learning normal user behavior patterns over time. The system continuously updates its understanding of what constitutes normal access behavior for each user, allowing it to distinguish between legitimate variations in behavior and actual anomalies, thereby reducing false positives while maintaining comprehensive security coverage.
Solution Approach 2:
The system incorporates feedback loops where user behavior patterns are continuously monitored, analyzed, and used to refine future detection algorithms. By learning from historical behavior data and adjusting thresholds based on contextual information, the system reduces false positives while maintaining high security coverage.
3Measurement precision
If comprehensive document analysis is performed to determine access permissions, then access control accuracy is improved, but processing complexity and resource requirements increase
Solution Approach 1:
The system segments the document analysis process into distinct functional components: the topic model handles document classification by content, while the behavior analyzer focuses on user access pattern analysis. This segmentation allows each component to specialize in specific aspects of access control, reducing overall processing complexity while maintaining high accuracy through coordinated operation.
Data Source
AI summary
A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.