Go Beyond Data Masking by Creating a Comprehensive Data Protection Strategy that Works for Your Business
As more reports of massive data breaches surface, implementing a robust data protection strategy is not an option but a must. Sensitive data must be secure whether it’s in use, in transit, or at rest. No matter where the data is stored or viewed, it must be protected to accomplish National Institute of Standards and Technology [NIST] requirements and many other regulations. Protecting data, your most sensitive assets is critical.
Data protection comes in a variety of methods, technologies, and approaches. Data masking is a technique commonly used to deidentify sensitive data by concealing it on the user interface screens. The level of risk tolerance depends on environmental factors such as on-premise, cloud, production, or non-production. Unfortunately, some organizations assume data masking provides adequate data protection, putting their businesses and sensitive data at risk.
Here’s why data masking should only be considered within a larger data protection strategy.
What Is Dynamic Data Masking?
Data masking is a technique that replaces sensitive values with non-sensitive values in a data set on the user interface screen. This adds a layer of protection to sensitive data to reduce sensitive data risk. There are two main types of data masking: dynamic data masking and static data masking.
Dynamic data masking (DDM) is used to protect data on the move. It does not change cleartext data at rest. Agents are created to mask all or parts of the data when displayed to unauthorized users who see the information before it reaches authorized users.
DDM is a data protection method used in production environments. It is the default data-protection method often offered natively, such as cloud providers, because it’s simple to implement and allows for rapid protection.
Why Data Masking Is Not Enough?
While data masking can be effective in certain contexts, it does not provide comprehensive data protection for businesses for several reasons:
- LIMITED SCOPE: In production environments, DDM is often used to accelerate the process of using sensitive data by replacing real values with fake values at the point data is displayed to the user. Because the data is superficially masked just-in-time for the user, DDM trades off robust security for ease of use. DDM doesn’t address the underlying security vulnerabilities or risks associated with the data itself. It merely conceals the data rather than securing it.
- DATA RELATIONSHIPS: Data masking can fail to preserve the relationships and dependencies between different pieces of data. This can lead to inconsistencies or inaccuracies in analytics, reporting, and data processing, potentially undermining the integrity of business operations.
- PERFORMANCE AND IMPACT: Depending on the complexity of the masking techniques and the volume of data involved, data masking can introduce significant overhead and performance issues, impacting the efficiency of data processing and application performance.
- REGULATORY COMPLIANCE: While data masking can help businesses comply with certain data protection regulations by obscuring sensitive information, it may not be sufficient to meet all regulatory requirements. For example, GDPR or HIPAA may mandate specific security measures and controls beyond simple data obfuscation.
- INSIDER THREATS: Data masking may not effectively protect against insider threats, where individuals within the organization have legitimate access to the data. Insiders with sufficient knowledge or privileges may still be able to access and misuse sensitive information, even if it’s masked.
- EMERGING THREATS: To combat the ever-evolving cyber threats such as advanced persistent threats (APTs), sophisticated malware, and social engineering attacks, businesses need comprehensive security measures that go beyond simple data masking to mitigate these risks effectively.
Use Additional Methods to Fortify your Data Protection Strategy
An array of data protection methods can include data masking along with tokenization and encryption.
For example, a business can choose to mask a customer’s street, monitor city and state, and tokenize the name and Social Security Number (SSN)/National Identification Number (NIN). This allows less sensitive data such as city and state to be available for analytics without the need to de-tokenize before use, while the highly sensitive name and SSN/NIN data elements are replaced by tokens and, therefore, remain useless to bad actors. Data is simultaneously protected and available to provide valuable business insights.
Conclusion
Data masking alone will not help organizations safeguard sensitive data and preserve the privacy of customers, partners, and employees. But when businesses adopt a multi-layered approach to data protection through a robust data protection platform, they are demonstrating they understand the complexity of data protection. They’re proving to customers that they’re effectively aligning a protection method with the level of data sensitivity.