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AI security and cyber risk in IoT systems.

Petar Radanliev1,2, David De Roure3, Carsten Maple4

  • 1Department of Computer Science, University of Oxford, Oxford, United Kingdom.

Frontiers in Big Data
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

This study explores cyber risks in low-memory Internet-of-Things (IoT) devices. It introduces a dependency model for better cyber risk assessment and management in emerging technologies.

Keywords:
AI securityInternet-of-Things (IoT)artificial intelligencecyber risk assessmentcyber risk estimationcyber risk insurancecyber risk managementrisk impact assessment

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Area of Science:

  • Cybersecurity
  • Risk Management
  • Internet-of-Things (IoT)

Background:

  • Low-memory connected devices, such as drones and robots, are integral to emerging technologies.
  • The proliferation of Internet-of-Things (IoT) devices presents unique cybersecurity challenges.
  • Current risk management methods may not adequately address the complexities of IoT environments.

Purpose of the Study:

  • To critically analyze cyber risks associated with low-memory IoT devices.
  • To evaluate the appropriateness of existing risk management strategies for IoT.
  • To propose enhanced approaches for IoT risk assessment and management.

Main Methods:

  • Critical reflection on current risk assessment methodologies.
  • Development of a dependency model tailored for IoT data strategies and cyber risk.
  • Analysis of how new approaches influence cyber risk policy and data strategy.

Main Results:

  • Identified challenges in managing cyber risks for low-memory IoT devices.
  • Presented a novel dependency model for cyber risk estimation and assessment.
  • Demonstrated the model's applicability for cyber risk insurance and enterprise-level risk assessment.

Conclusions:

  • A holistic understanding of IoT cyber risks is crucial for effective mitigation.
  • The proposed dependency model offers a suitable approach for estimating and assessing IoT risks.
  • Further research is needed to address remaining open questions in IoT risk assessment and management.