Why Is Data Masking Important In IoT Landscape?

Data Masking

Pointers at Glance

  • Data masking is essential for protecting personal information and sensitive data collected by IoT devices.
  • As the number of connected IoT devices continues to grow, data masking helps to secure the overall IoT ecosystem by making it more difficult for malicious actors to access and misuse personal information.

Hiding data from attackers is an important technique to combat major data challenges. As per IoT, the data masking technique helps protect personal information and addresses collected by IoT devices.

Why Is Data Masking Required For IoT?

Data masking can secure sensitive data while transmitting between cloud and IoT devices. IoT Analytics predicts that there will be 18% growth in IoT devices by 2023 reaching 14.4 billion. By 2025, it might increase to 27 billion.

An IoT-enabled reception system in a building could automatically verify visitors’ identity and issue them an access card, which would necessitate collecting personal information. Industrial IoT ecosystems usually collect less personal data compared to regular IoT systems. But, privacy concerns still exist in IoT and IIoT.

How Will Data Masking Help In IoT?

As IoT usually deals with huge amounts of data, it gains from masking sensitive data like personal information that helps protect an individual’s privacy and prevent data breaches. It is challenging for malicious actors to access and misuse it, and it helps in securing the overall IoT ecosystem.

With data masking, IoT enterprises can create realistic test data sets while ensuring the end-to-end security of sensitive data.

Data masking also helps organizations comply with strict regulations, such as HIPAA and GDPR, that require them to protect sensitive data. It enables companies to reduce the costs associated with data breaches like legal fees, loss of customers, etc., and protect the privacy of individuals. Simultaneously, it also enables IoT management organizations to govern the ecosystem strictly, ensuring data integrity, accuracy, and consistency.

Skip to content