Safeguarding the IoT Landscape With Data Masking Techniques

As businesses aim to provide personalized experiences to their customers, they are increasingly integrating connected IoT devices into their operations. However, as the IoT ecosystem expands, protecting data from malicious individuals who may try to access and misuse personal information becomes essential. According to MarketsandMarkets forecasts, the global IoT security Market size will grow from USD $20.9 billion in 2023 to USD $59.2 billion by 2028 at a Compound Annual Growth Rate (CAGR) of 23.1% during the forecast period. 

One of the key strategies for safeguarding data in this complex ecosystem is data masking. It can impact the IoT landscape and its role in protecting Personally Identifiable Information (PII), preserving data utility, and mitigating cybersecurity risks.

Introduction to Data Masking

Data masking is essential for protecting personal information and sensitive data and is a modern way of simulating realistic data for various business development and testing processes. This fictionalized data mimics its real counterpart and is used for all functional needs, such as training, testing, auditing, etc., while protecting sensitive information from unintended users.

Importance of Data Masking In IoT Infrastructure

The IoT ecosystem has various potential threats to the security of sensitive data generated, stored, and transmitted between IoT devices. Here are some common scenarios where data threats may arise:

Malicious Firmware or Software

When IoT devices are vulnerable or infected with malicious firmware or software, it increases the risk of data breaches. It is important to regularly modify or update the firmware or software on IoT devices to address this, applying data masking rules to protect sensitive data.

Insufficient Data Encryption

Since IoT devices constantly exchange sensitive information within a network, failing to encrypt the stored and transmitted data can lead to theft or misuse. Implementing robust data encryption measures is crucial to safeguard sensitive information in transit and at rest.

Risk to Unused Data

The sensitive data stored in IoT devices are disposed of when no longer needed and also require masking, as unmasked data can be prone to misuse.

Safe Testing Data

The data is crucial for the testing and development of IoT devices. Thus, the developers and testers require realistic data to build more customized devices that cater to customers’ needs. Data masking is crucial before sharing sensitive data with testers and developers. By masking sensitive data, the realistic datasets maintain the structure and relationships of the original data but obscures the sensitive information. It enables efficient development and testing of IoT devices without the risk of data breaches.

Meet Regulations Compliance

The widespread adoption of IoT has impacted various industries, including healthcare, finance, and telecommunications, enabling customized products and services for customers. Many such areas have to adhere to strict data protection regulations, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). These regulatory compliances can be taken care of with Data masking of sensitive data. This masking allows organizations to use and share data for analysis, research, and other business purposes while protecting sensitive information.

Common Data Masking Techniques in IoT

Below are the common data masking techniques used in the IoT landscape to protect sensitive information.

Data Substitution/Tokenization 

This involves replacing sensitive data with a unique identifier or token, which is another realistic value. For example, data substitution/tokenization is used for protecting the credit card number details of individuals by replacing the name of the credit card holder with another realistic name.  This technique ensures that sensitive information is not exposed, even if unauthorized access occurs.

Encryption

This is usually considered an original way of modifying data for protecting sensitive information and an alternative to masking. It is the only technique capable of recovering the original data values with the decryption key. The authorized parties with the decryption key can access and decipher the data. Encryption of IoT data adds an extra layer of security during storage, transmission, and processing.

Partial Masking

This involves partially conceding sensitive data while preserving its format and length. For example, credit card numbers, social security numbers, or email addresses are masked by replacing certain digits or characters with placeholder symbols like “X”. Partial masking makes data useful for analysis or testing without exposing sensitive information.

Differential Privacy

This aims to protect individual privacy by introducing controlled randomness while maintaining the quality of data required for analysis of the dataset. The addition of noise or randomness to the query results or the presence or absence of a particular individual's data does not significantly affect the outcome of a query or analysis. This ensures that no individual's data can be identified or retrieved from the aggregated data.

Some Applications of Data Masking in the IoT Landscape, With Examples of Data Masking Tools 

Data masking tools are essential for protecting sensitive information and preventing misuse. These tools help eliminate the risk of exposing complex data by replacing it with false or fictitious data. They are commonly used during application development and testing, where end-user inputs need to be masked. This article has compiled a list of top and commonly used data masking tools suitable for small, mid-sized, and large enterprises. These tools provide a reliable solution for masking data and safeguarding sensitive information from unauthorized access or misuse.

Use Cases of Data Masking

Data masking has a wide range of applications, including:

Data Masking Helps in Maintaining Secured Data for Auditing

It is important to have an effective security measure that helps maintain data accuracy while auditing. For example, Oracle's Data Masking and Subsetting solution safeguards audit trails by masking sensitive data using various masking techniques, including format-preserving masking, randomization, and substitution, with access control mechanisms, enforcing role-based access controls. It also enables the creation of subsets of data while applying masking techniques to save time and storage costs and improve efficiency while reducing risk. The solution ensures data privacy, integrity, and compliance during auditing procedures.

Cloud Data Protection Is Required to Protect Data Stored and Processed in the Cloud

Data masking tool emphasizes encryption to protect sensitive data in transit and at rest. This involves using strong encryption algorithms to secure data as it travels between different cloud infrastructure components and when stored in databases or other storage systems. One such data masking solution I have tried is K2view, which offers a built-in feature to identify and locate sensitive data, i.e., Personally Identifiable Information (PII), automatically and categorizes or classifies it as information to be masked according to pre-defined rules.

Additionally, different types of data masking, such as static (masked and stored separately), dynamic data (masked in real-time) or in-flight data masking (masking during transmission) methods are used to protect unstructured data (images, PDFs, text files, and more) by creating synthetic copies of such data while maintaining the context for testing purposes.

Data Masking Also Helps in Providing Necessary Access Controls

IBM Data Masking employs Role-Based Access Control, which ensures that access to sensitive data is granted based on predefined roles within an organization. This means that only authorized users with specific roles or responsibilities can access and manipulate masked data. RBAC helps prevent unauthorized access and minimizes the risk of data exposure. In this way, IBM InfoSphere Optim Data Privacy works in conjunction with access control solutions to enforce and manage access controls for masked data. The access controls are typically defined and enforced through the IAM system, while IBM InfoSphere Optim Data Privacy focuses on providing robust data masking and privacy features.

Conclusion

In an IoT landscape, data masking is crucial to ensure data security, privacy, and compliance. By effectively implementing data masking techniques, organizations can protect sensitive information, comply with privacy regulations, and foster the implementation of IoT technologies, ultimately leading to a safer and more reliable IoT ecosystem.