Qualitative analysis of security-aware platform engineering: Integrating AI-driven security controls in surveillance device lifecycle management

The proliferation of Internet of Things (IoT) surveillance systems introduces complex security challenges spanning technical implementation and human interaction domains. This article presents a qualitative analysis of security-aware platform engineering that integrates artificial intelligence (AI) driven security controls throughout the surveillance device lifecycle. With the global deployment of IoT devices projected to increase substantially in the coming years, addressing security vulnerabilities becomes increasingly critical as a majority of these devices remain susceptible to multiple security risks. The Adaptive Security-Aware Platform Engineering (ASAPE) framework proposed in this article harmonizes technical security implementation with human factors engineering across pre-deployment, deployment, operational, maintenance, and end-of-life phases. By examining user engagement patterns across numerous surveillance devices and interviewing multiple stakeholders, five distinct vulnerability patterns were identified: security-convenience tradeoffs, alert fatigue, knowledge decay, uneven implementation, and end-of-life negligence. Implementation results demonstrate that AI-augmented security platforms can achieve substantial improvements in security metrics while maintaining operational efficiency, with contextual orchestration reducing policy violations and lifecycle governance decreasing security incidents during transitions. The framework's integrated approach yields a significant return on security investment compared to conventional implementations, demonstrating the viability of comprehensive AI-driven security measures for IoT surveillance ecosystems.