The digital age has created tremendous data that requires space to store, access, and retrieve. Finding such tremendous storage systems costs organizations much and that’s the reason cloud platforms are sought after.
Cloud platforms are scalable, accessible, and cost-efficient. However, there’s an alarming concern – data privacy. With the average global cost of a data breach reaching a staggering $4.45 million in 2023, the prime concern is to ensure seamless data privacy across various entities such as servers, network connections, and software.
To combat data breaches, a powerful technique, data masking is employed that plays a crucial role in maintaining data privacy on cloud platforms.
What is Data Masking?
Data masking is a security technique that protects sensitive information such as personal information, payment details, patient health records, and intellectual property. It is also known as data anonymization and data obfuscation. The data is secured by protecting sensitive data by replacing, hiding, or transforming original data with fictional or altered data using data obfuscation tools.
Organizations can share internal or external data while sharing sensitive information for testing, development, or analysis without the risk of exposing sensitive data. In scenarios like real-time testing of data, the risk of unauthorized access or misuse of sensitive data is minimized while retaining the usability of it.
A few data masking techniques used are substitution, shuffling, encryption, and tokenization are used widely. Before we look at how it is actually done, let’s look at what is data masking and discuss some practical scenarios where data masking can play a pivotal role.
How Does Data Masking Achieve Data Privacy in a Cloud Environment?
Implementing data masking on cloud-based platforms depends on the cloud service provider and the tools that you are using. It begins with data discovery and classification wherein the data to be masked is identified based on its sensitivity.
Some common types of data that can be classified as sensitive are:
Personal Identification Information – Employee ID no, full name, salary, passport number, social security number, etc.
Patient Health Information – Insurance information, tests, laboratory results, medical histories, and health conditions that are collected by health service providers for the purpose of identifying appropriate care.
Card Payment Information – Credit and debit card transactions handled by merchants are collected by the Payment Card Industry Data Security Standard (PCI DSS) to secure cardholder data.
Such data can be secured by appropriate masking techniques based on the type of data – personal information, financial data, or intellectual property (designs, business plans) using any of the masking techniques.
Then, choose a data masking tool that’s compatible with your cloud environment and supports the desired masking techniques.
Challenges in Implementing Data Masking
Every transaction, interaction, and decision is data-driven and implementing privacy for such huge data is a tedious task. Let’s take an example: while developing new software for a banking application, developers need real-time datasets such as bank transactions. Details such as names, account numbers, addresses, and other financial details need masking so that the privacy of the individual is not compromised.
Due to the dynamic nature of banking transactions, it is difficult to keep masked datasets updated. This makes the previously masked data irrelevant for testing newly developed software.
And, when it comes to data such as the age of a customer, masking hampers the data relationships created in the database making it difficult to track details that are related to the age of the customer.
Types of Data Masking
Depending on the specific needs and challenges of an organization, different types of data masking are available that you can use as per your needs.
Static Data Masking (SDM) involves permanently altering sensitive data at rest before transferring or sharing. This protects dormant data but lacks flexibility for active applications.
Dynamic Data Masking provides access to real data while temporarily masking it in transit. Authorized users can view the original data without relying on masking copies.
On-the-Fly Data Masking integrates masking capabilities into ETL processes for real-time transformation of streaming data enabling live data analysis without exposure.
Because of the growing demands concerning data privacy, there are more data masking techniques and tools evolving to match the need.
AI-powered NLP techniques, deep learning models, and machine learning algorithms are recent advancements that can be applied to mask sensitive information in text data.
The Evolution and Growth of Data Masking
The trajectory of data masking is clear. As the healthcare sector grapples with breaches costing over $10.93 million and the US faces average breach costs of $9.48 million, the need for robust data masking solutions has never been more pressing.
The global data masking market is projected to grow from $582 million in 2022 to $1.9 billion by 2027, at a CAGR of 26.7%. Driving this growth is rising adoption across customer experience platforms, widespread cloud migration demanding stronger data security, and stricter regulatory compliance.
Integrated data masking capabilities are no longer nice to have. Leading CDPs and analytics tools now provide on-the-fly masking and row-level security to enable compliant and ethical data usage.
With such a pivotal role in data security, it’s no surprise that data masking often raises questions. Let’s address some of the most common queries readers might have.
Frequently Asked Questions
- How does data masking differ from data encryption?
While encryption protects data in transit and at rest, it still allows authorized users full access to the original data. Masking goes a step further by obscuring data from specific users.
- Are there specific industries or sectors that benefit more from data masking?
Highly regulated sectors like healthcare, banking, and insurance gain enormously by using data masking for compliance. Data-rich sectors like retail, media, and technology also leverage masking heavily.
- How does data masking ensure compliance with international data privacy regulations?
By pseudonymizing personal data, masking helps comply with regulations like GDPR and CCPA limiting personal data use. It also aids HIPAA compliance in the US healthcare sector.
Conclusion
The indispensable role of data masking in safeguarding data in cloud environments is clear. As businesses strive to harness data-driven insights, data masking provides the pivotal assurance of privacy.
With a robust data masking strategy, organizations can balance the immense power of data analytics with an ethical approach to data security.