Contextualized Security Event Remediation via User Profiles
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Solution Overview
Problem
Modern security systems lack personalized and contextualized environment security information, forcing security personnel to deduce anomalies from disparate sensor data and navigate generic user interfaces, which is inefficient and prone to errors.
Innovation Solution
A method and apparatus that collect and analyze sensor data to detect security events, generate contextual information, and create personalized remediation conversations based on user profiles, guiding security personnel to resolve events effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If security systems report only disparate sensor data, then device complexity is reduced, but security personnel must manually deduce anomalies which increases time consumption and reduces productivity
Solution Approach 1:
The system performs preliminary analysis by detecting security events and generating contextual information before presenting data to security personnel. The processor proactively identifies anomalies, determines their context, and prepares personalized notifications, eliminating the need for manual deduction and reducing response time.
Solution Approach 2:
The system introduces an intermediary layer between raw sensor data and security personnel. This intermediary component (the processor with event detection and contextual generation capabilities) automatically interprets and contextualizes sensor data, presenting only relevant information to personnel while maintaining system simplicity.
2Ease of manufacture
If security systems use generic user interfaces, then ease of manufacture is improved, but information details are missed which reduces reliability of security responses
Solution Approach 1:
The system applies local quality by customizing the user interface presentation based on individual user profiles. Each security personnel member receives personalized notifications and information tailored to their specific needs, roles, and preferences, ensuring that relevant details are highlighted for each user while maintaining a unified system architecture.
Solution Approach 2:
The system changes presentation parameters dynamically based on user profiles. The processor adjusts notification content, information prioritization, and interface elements according to stored user profile parameters, allowing the same generic interface to deliver customized, reliable information to different users.
3Reliability
If security systems provide detailed contextual information, then security response reliability is improved, but information overload increases which worsens ease of operation
Solution Approach 1:
The system extracts only the most critical and relevant contextual information from the full sensor dataset. By identifying and presenting only essential details related to detected security events, the system avoids information overload while maintaining sufficient context for reliable security responses.
Solution Approach 2:
The system applies partial action by providing contextual information selectively rather than exhaustively. It presents sufficient detail for reliable security assessment while omitting redundant information, striking the optimal balance between information completeness and operational ease.
4Measurement precision
If security systems collect comprehensive sensor data, then measurement precision of security events is improved, but data processing complexity increases which worsens device complexity
Solution Approach 1:
The system performs preliminary data processing by pre-defining security event criteria and contextual parameters. This advance preparation allows comprehensive sensor data to be processed efficiently without increasing operational complexity, as the processing logic is pre-established and automated.
Data Source
AI summary
Disclosed herein are apparatuses and methods for providing personalized and contextualized environment security information. An implementation may comprise collecting a plurality of sensor data from a plurality of sensors located in an environment, detecting a security event in the environment based on at least a portion of the plurality of sensor data, and generating contextual information for the security event based on at least a larger portion of the plurality of sensor data. The implementation may further comprise detecting a first user accessing security information for the environment on an output device, retrieving a user profile of the first user, and generating a remediation conversation based on the user profile, the security event, and the contextual information. The implementation may further comprise outputting at least a first portion of the remediation conversation on the output device.