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December 25, 2023

Data Masking Best Practices: Enhanced Data Security

You’ve probably read a lot about how technologies like data masking tools can help your organization “enhance” its data security. But what does that really mean?

When industry experts talk about “enhancing” data security, they mean adopting a comprehensive approach that strengthens data protection against breaches, unauthorized access, theft, and other forms of compromise. Such strengthening goes far beyond applying one or two new security measures. Instead, it involves a broad spectrum of strategies, including data masking techniques, including:

  • Implementing new security technologies that meet current compliance regulations and standards.
  • Strengthening authentication processes.
  • Conducting routine security audits and assessments.
  • Employing data masking solutions to reduce data leakage.
  • Investing in employee training and awareness of data making best practices.
  • Developing a robust data security policy that includes incident response planning.

Today, barely a week goes by without a news story reporting a serious theft of sensitive information. Sadly, many of these breaches could have been prevented if the victims had implemented existing tools and data privacy best practices, including data masking, to neutralize them. Here’s how your organization can use data masking to stop cybercriminals dead in their tracks.  

How Data Masking Works

Data masking, also known as data obfuscation, replaces sensitive or confidential information such as names, addresses, credit card numbers, and health information, with realistic values that maintain the data’s integrity and format for operational use. The original data remains available for people to use in application development, testing, and analytics.

Much like the Native American Code Talkers and the European Enigma Machine encrypted sensitive military communications to make them incomprehensible to anyone other than those generating the messages, data masking encodes sensitive information, rendering it indecipherable to unauthorized users. Whether it’s Personally Identifiable Information (PII), Payment Card Information (PCI-DSS), Protected Health Information (PHI), or Intellectual Property (IP), data masking ensures vital information remains secure yet functional, something that’s particularly critical in non-production environments where data’s integrity and structure need to be maintained without exposing the actual sensitive information.

Best Practices for Data Discovery in Data Masking

As quickly as a new data security technology is released, cyber attackers find novel ways to get around it, and regulators issue new laws to address the emerging threats and keep consumers protected. Data masking is a practical solution that makes it easier to adapt to these and other regulations, including internal company policies, contractual requirements, and industry standards. It safeguards against data breaches and simplifies compliance, ensuring organizations manage data responsibly and securely.

By following a basic set of data masking best practices, your organization can make its data masking strategy as effective as possible.

  1. Perform comprehensive data discovery. You can’t initiate data masking without first identifying which data needs protection. A thorough scanning of databases to locate sensitive information ensures no critical data is overlooked in the masking process.
  2. Tailor data masking techniques. Not all data require the same level of masking. Apply masking techniques appropriate for the sensitivity of the data and the specific environment requirements, such as production or non-production. This approach maintains the data’s utility while ensuring compliance with various regulatory standards.
  3. Conduct routine compliance reviews. Given that data regulations constantly evolve, regular reviews are critical to ensuring your chosen data masking techniques align with the latest internal, contractual, and governmental regulations. By staying up-to-date, you avoid costly non-compliance penalties, foster trust, and safeguard your reputation.

Data Anonymization vs. Data Masking

Understanding the nuances between data anonymization and data masking helps ensure optimal data security and compliance based on specific requirements.

  • Data anonymization permanently alters data to prevent identification. It’s ideal for public data sharing or analytics, where user privacy is paramount, as it removes or modifies personal identifiers and makes re-identification nearly impossible.
  • Data masking temporarily and reversibly obscures sensitive details. It is suitable for development or testing environments where real data is necessary, but exposing sensitive information is risky.

Which you choose depends on the data utility need versus the imperative for privacy protection.

Developing a Comprehensive Data Masking Strategy

Data masking’s primary purpose is to guarantee sensitive information protection without inhibiting or compromising its accessibility. A well-crafted data masking strategy aligns with organizational goals while integrating seamlessly into a broader data security framework and adhering to current compliance requirements.

  • Establish goals and objectives tailored to your organization’s unique requirements, including PII, PCI-DSS, PHI, or IP, and identify each type’s associated risks. Focus on minimizing these specific risks while maintaining data usability for legitimate purposes. Objectives might include preventing data breaches, ensuring privacy compliance, and maintaining data quality for testing and development.
  • Integrate into a broader data security strategy. Data masking doesn’t exist in isolation; it’s part of your organization’s larger data security and governance framework. This ensures the masking works in concert with other security measures, such as encryption and access controls, to provide a comprehensive defense against data breaches. It also helps streamline processes and policies across different departments, ensuring consistent data protection.
  • Continually adapt to compliance and industry standards. An effective framework must be flexible enough to adapt to evolving regulatory requirements, including the highly specific provisions of regulations like GDPR and HIPAA. Regular audits and updates to your data masking strategy further help you stay compliant.
  • Mask only necessary data elements. A targeted approach to masking reduces the overhead costs and complexity associated with data protection and compliance. Selective masking also allows for broader data sharing within the organization or with third parties, as the risk of exposing sensitive information is significantly lessened.
  • View data masking as a long-term solution. The data masking process should be scalable and repeatable. Repeatability ensures consistency across various data handling instances, while scalability acknowledges that the volume and types of data your organization manages will likely grow and change over time.

A sweeping data masking strategy is critical for modern organizations, safeguarding sensitive information, ensuring compliance, and enabling cost-effective data management, while aligning with your organization’s broader objectives and data security initiatives.

Selecting and Implementing Data Masking Tools

When choosing data masking tools, selecting those tailored to your organization’s specific needs is crucial. They should integrate easily into existing IT infrastructures, enhance data security without disrupting current systems, and align with industry standards. Here’s why.

  1. Compatibility with industry standards. Every industry has its own regulatory standards. For instance, the healthcare sector must comply with HIPAA. Financial institutions are typically bound by GDPR and PCI-DSS standards. Enterprises need to implement data masking tools compatible with their industry’s specific regulations, ensuring compliance and avoiding legal and financial penalties.
  2. Integration with existing IT infrastructure. Seamless integration minimizes workflow disruptions and improves the employee learning curve. The data masking tool you select should work with existing databases, applications, and cloud services to avoid data security enhancement coming at the cost of operational efficiency.
  3. Scalability and flexibility. Data management needs to change as companies grow. A scalable data masking tool efficiently handles increasing data volumes and adapts to new data types as they’re introduced. Flexibility allows for customization that meets your changing needs and a dynamic regulatory environment.
  4. AI-based scanning capabilities. AI-powered tools are valued for their efficiency in scanning databases and other sources for sensitive elements like PII or financial information. These advanced tools use machine learning algorithms to quickly identify and categorize sensitive details, streamlining the data masking process, saving time, and enhancing accuracy, all of which help to reduce the risk of unmasked sensitive data slipping through.

In the long run, the ideal data masking tool is the one that aligns with your organization’s unique regulatory requirements, integrates with existing systems, scales to your company’s growth, and uses AI for efficient data identification.

Effective Data Masking Techniques

You can use various types of data masking to protect sensitive data, including:

  • Dynamic Data Masking (DDM). A real-time data masking technique that doesn’t alter actual data but masks it when queries are run. DDM is particularly useful in environments where data’s frequently accessed but shouldn’t be fully visible to all users, such as in customer service operations. For example, a call center agent might only see the last four digits of a customer’s credit card number.
  • Static Data Masking (SDM). A sanitized copy of the database where sensitive data is masked is created and then used in non-production environments such as testing and development. SDM is especially beneficial in situations where developers need to work with realistic data without accessing sensitive information. A financial institution, for example, might choose SDM to protect customer data in its development processes to ensure developers can perform their tasks effectively without compromising customer privacy.
  • On-the-fly. Sensitive data is altered in real time with scrambled characters. Healthcare providers and financial institutions often use it to protect sensitive information and reduce breach risks while making it easily accessible to those who frequently need it.

The key to effective data masking is balancing data utility with security. You should select techniques based on your organization’s specific requirements. For instance, DDM is a good choice for real-time, user-facing environments; SDM is better suited for back-end processes.

How Velotix Can Help

To sum up, you should create a data masking standard that:

  • Adopts current best practices.
  • Encourages continuous improvement and adaptation.
  • Facilitates sensitive data discovery.
  • Maintains referential integrity among distinct data sources.
  • Considers governance.
  • Can be repeated at scale.

These and other best practices are easier to achieve than you might think. Velotix’s AI-driven solutions offer secure data access at scale, allowing your business to advance at a comfortable pace while making data available to users and for applications and algorithms. We’re the only AI data security platform that helps enterprises discover, protect, and access their data while remaining compliant. Contact us today to learn more or book a demo.

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