Meeting Recording Security Management via ML Labeling
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Solution Overview
Problem
Meetings and recordings are prone to data leaks due to unauthorized access, eavesdropping, and visual spying, especially in hybrid work environments where sensitive information may be inadvertently recorded or shared.
Innovation Solution
The system predicts meeting security levels, assesses security in recorded meetings, and provides recommendations to modify meeting access before and after the meeting, using machine learning algorithms to apply security labels and ensure appropriate access controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If meeting recordings are circulated for review and accessibility purposes, then information sharing and collaboration are improved, but data leakage risks and security vulnerabilities increase
Solution Approach 1:
The patent applies local quality by implementing security labels and access controls at the segment level rather than uniformly across the entire recording. Different portions of the meeting recording can have different security classifications, allowing granular control over what information is accessible to whom. This enables the system to share accessible portions while protecting sensitive segments from data leakage.
Solution Approach 2:
The patent segments the meeting recording into multiple portions with different security labels. By dividing the recording into segments that can be independently controlled and distributed, the system enables selective sharing of non-sensitive content while isolating sensitive portions, thus improving accessibility without increasing overall data leakage risk.
2Reliability
If security labels and access controls are applied to meeting recordings, then data security and confidentiality are improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent applies preliminary action by automatically analyzing meeting content before distribution and pre-applying appropriate security labels and access controls. The system processes recordings upfront to identify sensitive segments, classify them with security labels, and establish access rules before the recordings are shared, thereby ensuring security without adding complexity to the distribution process.
Solution Approach 2:
The system implements self-service by using automated machine learning models to analyze meeting content, detect sensitive information, and apply security labels without requiring manual intervention. This automation reduces the complexity burden on users while maintaining high security standards through intelligent, self-executing security management.
3Reliability
If automated security assessment is performed on meeting recordings, then data protection effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing automated security assessment on identifying and labeling only the sensitive segments within recordings rather than uniformly processing every portion. The machine learning models target specific patterns and content types that indicate sensitivity, enabling effective data protection while reducing overall processing time by avoiding unnecessary analysis of non-sensitive content.
Data Source
AI summary
Disclosed are various embodiments for preventing unauthorized access to materials and topics for meeting invitations and meeting recording. A meeting recording and other meeting data is received in association with a meeting identifier. The meeting recording and the meeting data is provided as input to a machine learning engine to generate labelled recording data. A user-specific recording package is generated to include data limited to the at least one meeting section corresponding to a set of security labels approved for a user account.