Key points:

• Data redaction permanently removes sensitive information, making it irretrievable, while data masking temporarily disguises it with fake data for controlled use.
• Both techniques are vital for cybersecurity, protecting sensitive information and ensuring compliance with data privacy regulations.
• Metomic's data security platform offers automated redaction, discovery, and masking features, helping organizations safeguard sensitive data within their SaaS ecosystem.

Data masking and redaction are essential cybersecurity techniques to protect sensitive information. While both methods aim to safeguard data, they differ in their approach and purpose. Let's explore the key differences between them.

What is Data Redaction?

Data redaction involves editing or obscuring sensitive or Personally Identifiable Information (PII) in documents or datasets to safeguard privacy while retaining the content's value. Techniques range from blacking out text in physical documents to digitally modifying files to hide personal details, ensuring the non-sensitive data remains accessible and secure.

Employed in settings like legal documents, government information releases, and corporate communications, data redaction and encryption are essential in healthcare, finance, and legal sectors to protect against privacy breaches. It aligns with privacy laws and mitigates the risk of data leaks by controlling access to sensitive information.

Within cyber security, data redaction acts as a proactive defence, contributing to a stronger security framework. An example could be stripping patient records of PII before research use, balancing the need for privacy with the utility of data.

What is Data Masking?

Data masking is disguising original data with modified content, characters, or other data types or constructs to protect sensitive information from unauthorised access while maintaining its usability for certain processes or users. This method ensures that the data remains functional but does not expose sensitive information.

There are three primary types of data masking:

• Static Data Masking (SDM): SDM involves creating a sanitised database version where sensitive data is replaced with fictitious, realistic data. It's used in non-production environments for testing, development, or training without risking real data exposure.
• Dynamic Data Masking (DDM): DDM masks data on the fly as it is queried from the database, ensuring that unauthorised users only see masked data. It's suitable for environments where data must remain secure yet accessible for various operational needs.
• On-the-Fly Data Masking: Similar to DDM, this method masks data in real-time as it is accessed. However, it is specifically designed for situations requiring immediate data protection with minimal impact on system performance.

Data masking significantly contributes to cyber security and compliance by ensuring that sensitive data is inaccessible to unauthorised users, thus reducing the risk of data breaches and meeting data protection standards.

For instance, in a business environment, data masking can protect customer information in a customer service system. Customer service representatives may see masked credit card numbers, ensuring they can assist customers without accessing their full financial details. This secures sensitive personally identifiable information and helps businesses comply with regulations like GDPR or PCI DSS, which mandate stringent data privacy measures.

What's the difference between Data Redaction and Data Masking?

Data Redaction

Data redaction is a definitive process of permanently removing sensitive data to prevent its recovery or misuse. This method is essential when information must be irretrievably concealed, such as in legal documents where privacy is mandated by law or regulation. Redaction renders parts of the document inaccessible, providing a strong safeguard for sensitive data. However, this permanent deletion of identifiable information also means that the data can no longer be used for subsequent analysis or processing, which may limit its utility in some contexts.

In practice, data redaction is the method of choice for finalised documents to be released publicly or shared outside of the organisation. Organisations can confidently comply with data protection requirements by ensuring that redacted elements are irreversibly obscured. Yet, the irreversible nature of data redaction necessitates careful consideration and application, as it removes the possibility of revealing the redacted information later, even under secure conditions.

Data Masking

In contrast, data masking is a reversible technique that disguises sensitive data by replacing it with fictitious but realistic alternatives. This allows the data to remain usable for development, testing, or analysis purposes without exposing sensitive information. Data masking is adaptable, supporting secure data handling in dynamic environments where access to functional data is required without compromising the underlying structure or sensitive information.

Data masking's reversible nature allows businesses to use their data effectively while maintaining a strong data security posture. For instance, it enables the analysis of customer behaviour using real-world data scenarios without risking the exposure of real customer data. The approach is suitable for continuous development and iterative processes, allowing authorised personnel to work with data that retains its operational integrity and can be restored to its original state when necessary.

When to Use Data Redaction vs Data Masking

Scenarios Best Suited for Data Redaction:

Data redaction is the method of choice when sensitive information must be permanently removed from a dataset or document. IT teams should employ redaction when preparing documents for environments where the data's confidentiality is paramount and there will be no future need to access the original sensitive information.

Redaction obscures or removes sensitive information from documents, including legal materials, classified information submitted to public records, or reports shared with unauthorised parties. In essence, redaction is used when the information is no longer needed for future processing or when its exposure could lead to compliance violations or privacy breaches.

Scenarios Best Suited for Data Masking:

Data masking should be used when there is a need to work with realistic data without exposing sensitive information. This is particularly relevant in software development and testing environments, where IT teams require a data structure that behaves like real operational data without the risk of compromising actual sensitive information. Data masking is also useful during user training or in demonstration environments where there is a need to protect real underlying data while still showcasing system capabilities.

Role of Security Teams and Organisational Implications

Security teams are tasked with integrating data protection strategies, such as data redaction and masking, into an organisation's broader security protocols. These teams are responsible for identifying which pieces of company data are at risk and determining the appropriate protective action, whether redaction or masking. They must develop, implement, and monitor policies that dictate the proper handling of sensitive information, ensuring these practices are in line with both the organisation's security requirements and legal compliance mandates.

Adherence to data protection regulations is a critical component of the security teams' mandate. These teams need a thorough understanding of laws like GDPR and HIPAA to align their data protection strategies accordingly. Redaction is often crucial for meeting legal requirements that demand removing personal data from documents accessible to the public. At the same time, masking sensitive data is essential when data must be utilised in operational settings without violating privacy laws.

Implementing a data privacy strategy with redaction and masking extends benefits beyond the security department. When all employees are knowledgeable about these data protection practices, it fosters a security-aware culture across the organisation. This company-wide adherence to data protection protocols significantly reduces the risk of data breaches. A clear data protection strategy can enhance customer trust and bolster the organisation’s reputation, providing a competitive edge in markets where data security is a top concern for consumers.

How Metomic Can Protect Your Data

Metomic's data security platform is a key solution for businesses concerned with data security, such as data redaction and masking.

Here's how Metomic's features align with these strategies:

• Automated Data Redaction: Metomic offers automatic redaction for sensitive data that may find its way into your SaaS ecosystem. This can help minimise the amount of sensitive data across your tools, reducing your attack surface.
• Automated Data Discovery: The foundation of effective data redaction and masking lies in pinpointing sensitive data's exact locations. Metomic’s automated discovery tool dives deep into an organisation's SaaS environments, pinpointing the whereabouts of sensitive data. This precision is important for identifying which data requires redaction or masking, ensuring no sensitive information is unprotected.
• Data Loss Prevention (DLP): Metomic's DLP feature supports data masking efforts. It automatically blocks the sharing of sensitive information in unsuitable settings, reducing the risk of unintentional disclosures. This tool is essential for applying data masking consistently, ensuring financial and personal data remains secure across all applications.
• Real-Time Alerts and Human Firewall: Beyond technology, the human element is crucial in data redaction and masking strategies. Metomic’s alert system educates users about security policies, emphasising the importance of correctly handling redacted and masked data. This initiative cultivates a proactive security culture, reducing the likelihood of breaches due to human error.
• Advanced Access Controls: Proper data masking involves strict control over data access. Metomic's advanced access controls allow for the meticulous management of who can see data (and when) which is important for keeping masked data secure and preventing unauthorised access.
• Compliance with Global Regulations: Compliance is a major reason for employing data redaction and masking. Metomic helps ensure that data management practices meet the standards set by regulations like HIPAA, PCI DSS, and GDPR, which is invaluable for businesses navigating the complex terrain of data protection laws.

By incorporating Metomic into their data security strategy, businesses can make their data redaction and masking efforts more effective and elevate their overall approach to protecting sensitive information. Metomic offers a streamlined path to enhanced operational efficiency and compliance, proving to be a crucial partner in the secure management of sensitive data within the SaaS ecosystem.

To discover how Metomic can help improve your data security, get in touch with one of our cyber security experts or book in a personalised demo today.