A Comprehensive Review of Telemetry Data-Driven AI Cybersecurity Solutions in IoT-Based Insurance Ecosystems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I3P112Keywords:
Internet of Things (IoT), Telemetry Data, Artificial Intelligence (AI), Cybersecurity, Insurance, Predictive Underwriting, Fraud DetectionAbstract
The Internet of Things (IoT), artificial intelligence (AI), and telematics are all contributing to the digitization of the insurance industry. Because of the Internet of Things' (IoT) explosive growth, insurance systems are now able to collect data in real time from linked devices like wearables, cars, and industrial sensors. While this connectivity enhances risk assessment, claims processing, and operational efficiency, it also introduces significant cybersecurity and privacy challenges. Improving cybersecurity in insurance ecosystems based on the IoT is the focus of this article, which offers a thorough analysis of AI solutions powered by telemetry. Discuss normal and adversarial scenarios in Industrial IoT environments, including scanning, denial-of-service, ransomware, backdoor, and injection attacks, and highlight the role of AI in automated threat detection, anomaly identification, and real-time mitigation. Furthermore, the paper examines AI applications in predictive underwriting, fraud detection, and usage-based insurance, alongside key challenges such as data privacy, legacy system integration, ethical concerns, and regulatory compliance. Finally, future directions, including privacy-preserving AI, Explainable AI, blockchain integration, and edge intelligence, are outlined to foster secure, transparent, and scalable adoption of AI in insurance. The study highlights the crucial role of telemetry-driven AI in developing resilient, equitable, and trustworthy insurance ecosystems
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