Title: IoT Complete Self-Assessment
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IoT Standard Requirements
7 Sample Requirements:
- New objects as the plethora of different device types, devices, gateways and IoT platforms need to be maintained because they are decentralized trust servers of the organizations using them. Management and governance enables organizations to meet both compliance and business requirements. Will your IAM system handle the increased number of relationships between users, devices, services and policies?
- The pharmaceutical industry is also taking advantage of digital progress. It is using IoT for supply chain security in packaging and tracking of drugs. There are new companies using computer chips in pills for tracking adherence to drug regimens and associated biometrics. Using this as an example, how will we use and protect this sensitive data?
- Do we put an IAM architect in the IoT center of excellence? Hastily deployed pockets of identity infrastructure need to be maintained for the full lifetime of the devices. You do not want to set a presence of systems with low assurance levels that an organization later must handle. Do you need end-to-end authentication and authorization?
- When developing and capitalizing on IoT solutions, do we as owners consider the societal cost, systemic risk, and risk externality to avoid what may be called designer hubris. In other words, why add features when theyre not needed and contribute to the insecurity/fragility of the whole system?
- Traditional data protection principles include fair and lawful data processing; data collection for specified, explicit, and legitimate purposes; accurate and kept up-to-date data; data retention for no longer than necessary. Are additional principles and requirements necessary for IoT applications?
- Extensibility: extensibility could be a huge challenge at the edge of the network, unlike a mobile system, the things in the IoT could be very dynamic. when the owner purchases a new thing, can it be easily added to the current service without any problem?
- Sensors and the IoT add to the growing amount of monitoring data that is available to a wide range of users. How do we effectively analyze all of this data and ensure that meaningful and relevant data and decisions are made?
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