Application Window Detection for Screen-Sharing Risk Review
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Solution Overview
Problem
Existing collaboration platforms face challenges in identifying and managing privacy, security, and compliance risks associated with the sharing of sensitive applications during screen sharing, image attachments, or chat conversations, requiring manual review of recorded content which is time-consuming and inefficient.
Innovation Solution
A security and compliance platform uses visual-based computer detections to identify application windows in collaboration sessions, employing machine learning models to analyze textual and visual features of screen shares, webcam content, and chat images to automatically flag potential risks, allowing for automated risk assessment and further human review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of recorded collaboration sessions is performed to identify shared applications, then detection accuracy is improved, but productivity deteriorates due to time-consuming manual analysis
Solution Approach 1:
The patent introduces an intermediary system consisting of computer vision models and machine learning algorithms that act as a mediator between the raw video content and human reviewers. This intermediary automatically processes video frames, detects application windows, and generates detection results, thereby eliminating the need for manual frame-by-frame analysis while maintaining high detection accuracy through sophisticated image recognition technology.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer vision system. Instead of human reviewers manually examining video content, the system uses trained machine learning models to automatically detect and classify application windows in screen sharing content, significantly improving productivity while maintaining detection precision through algorithmic analysis.
2Reliability
If comprehensive manual analysis of all visual content is performed to identify sensitive applications, then reliability of security review is improved, but loss of time increases due to extensive manual effort
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive datasets of application interfaces and visual patterns before deployment. These pre-trained models are then applied to collaboration session recordings, enabling rapid and reliable detection of sensitive applications without requiring time-consuming manual analysis of each session. The preliminary training ensures the system is prepared to reliably identify various application types when deployed.
Solution Approach 2:
The system performs self-service security review by automatically detecting, classifying, and flagging sensitive applications in collaboration content without requiring continuous human intervention. The machine learning models independently analyze video frames, identify application windows, and generate security alerts, enabling the system to serve itself in performing security review functions while maintaining high reliability through consistent algorithmic application.
3Productivity
If automated computer vision detection is implemented to identify application windows, then productivity is improved through automation, but device complexity increases due to machine learning models
Solution Approach 1:
The patent implements a universal machine learning detection system that can identify multiple types of applications and interfaces across different collaboration platforms using a single set of models. The system is designed to detect various application windows, document interfaces, and sensitive content types uniformly, reducing the need for separate specialized tools and thereby managing complexity while maintaining high productivity through multi-functional capability.
Solution Approach 2:
The patent manages system complexity by adjusting and optimizing parameters of the machine learning models, such as detection thresholds, confidence levels, and processing frame rates. By carefully tuning these parameters, the system achieves high detection efficiency while controlling computational resource requirements and processing complexity, allowing automated detection to run efficiently without excessive system complexity.
Data Source
AI summary
A security and compliance platform ingests content from heterogeneous collaboration platforms and processes the content to detect potential regulatory, privacy, and security risks in the conversations. One of the detections that can be applied is the identification of application windows appearing during a collaboration screen share. Because these applications may contain sensitive personally identifiable information (PII), technical trade secrets, or highly confidential corporate information, there is meaningful utility in identifying instances when these applications are shared. If applications shared on screen is detected, a corresponding workflow action may be implemented, for example flagging a session for further analysis in a review screen of the security and compliance platform.