Personalized AI Security Models for Data Centers
Keywords:
AI-Driven Self-Service, Physical Security Management, Data Center Security, Personalized Security Models, Customer Experience Optimization, Security Usability Design, Trust in AI Systems, Security Control Effectiveness, Self-Service Security Systems, Decision Support Systems, User-Centric Security Design, Security Personalization Factors, Human-Centered AI, Secure Customer Interfaces, Access Control Systems, Behavioral Security Analytics, Customer Trust Modeling, Security Risk Mitigation, Adaptive Security Frameworks, Intelligent Security Services.Abstract
Using artificial intelligence to create a personalized approach to customer self-service in physical security management, enabling a positive customer experience without compromising security. Physical security management in data center environments is becoming increasingly customer centric. Providing data center customers with self-service capabilities for physical security management is thus creating a dichotomy. While such capabilities can improve customer experience through reduced waiting times, they can also create an opportunity for misuse. Distrust in the technology implemented can lead to customers deliberately circumventing security controls, thus mitigating their effectiveness. The mismatch between customer trust and security remains an area of research.
Personalized models for self-service and decision-support services in the AI domain improve a user’s experience with the services they consume while increasing their trust and reliance on the associated technology. Personalized models now need to be created for physical security management in data center environments. The first step is determining the personalization factors for self-service capabilities and decision-support services offered to customers of physical security management services in data centers. Factors, such as age and user technology experience level, are mapped back to the customized service framework for inclusion. Once the personalization factors are known, a user needs assessment is performed with focus groups. Input from the focus groups drives the customizations made to the UI supporting self-service capabilities.
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