Automated Security Profiles for Information Handling Systems
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Solution Overview
Problem
Information handling systems face challenges in protecting sensitive information from unauthorized viewers, as users may unintentionally expose private data when unaware of onlookers, highlighting the need for automated security measures to prevent data privacy breaches.
Innovation Solution
An information handling system uses sensor data and machine learning algorithms to determine the relevance of individuals to the information displayed, generating an onlooker intent score to automatically apply security profiles, such as activating a privacy screen, to prevent exposure of sensitive information to unauthorized individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated security measures are implemented to prevent data privacy breaches, then information security is improved, but device complexity increases
Solution Approach 1:
The security system is segmented into multiple independent components: sensors for detecting onlookers, machine learning algorithms for analyzing sensor data and determining relevance, and executable instructions for applying security profiles. This modular architecture improves information security through comprehensive monitoring while managing complexity by dividing the system into manageable, specialized modules that can be developed and maintained independently.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring the environment with sensors and pre-evaluating potential security threats before actual data exposure occurs. Machine learning algorithms analyze sensor data in advance to determine individual relevance and generate onlooker intent scores, allowing the system to proactively apply security profiles before sensitive information is compromised, thereby improving reliability while maintaining manageable complexity through preventive rather than reactive security measures.
2Measurement precision
If multiple sensors and machine learning algorithms are used to detect onlookers and determine relevance, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges multiple sensors (cameras, microphones, other detection devices) and machine learning algorithms into a unified security monitoring system. This integration improves measurement precision by combining data from multiple sources to accurately detect onlookers and determine their relevance to displayed information. The complexity is managed by consolidating these components under a coordinated framework where executable instructions orchestrate the combined sensors and algorithms to work together efficiently.
Solution Approach 2:
The machine learning algorithms serve multiple functions: detecting onlookers from sensor data, determining relevance of individuals to displayed information, generating onlooker intent scores, and informing security profile application decisions. This multi-functionality improves measurement precision across multiple detection tasks while reducing device complexity by eliminating the need for separate specialized systems for each function.
3Reliability
If security profiles are automatically applied based on onlooker intent scores, then information security is improved, but ease of operation decreases
Solution Approach 1:
The security system operates autonomously by automatically detecting onlookers, analyzing their relevance to displayed information, generating onlooker intent scores, and applying appropriate security profiles without requiring manual user intervention. This self-service capability improves data privacy protection by ensuring consistent security enforcement while simplifying ease of operation for users who don't need to manually manage security settings or monitor for onlookers.
Solution Approach 2:
The system incorporates feedback mechanisms where machine learning algorithms continuously analyze sensor data and user interactions to refine onlooker intent scores and improve security profile application decisions. This feedback loop enhances information security by adapting to new patterns and threats while maintaining ease of operation, as the system learns and improves automatically without requiring user reconfiguration or manual adjustments.
Data Source
AI summary
An information handling system (IHS) may receive, from a first sensor of the information handling system, first sensor data. The IHS may detect an individual other than a user of the IHS in a field of view of the first sensor based on the first sensor data. The IHS may then, in response to the first sensor detecting the individual, receive, from a second sensor of the IHS, second sensor data. Based, at least in part, on the first sensor data and the second sensor data, the IHS may determine relevance of the individual to an application displayed on a display of the IHS. The IHS may then generate an intent score for the individual based at least in part on the determined relevance. The IHS may then determine whether a security profile is to be applied to the IHS based, at least in part, on the intent score.