What Is Data Masking and What Are Its Benefits

Protection methods for sensitive data have evolved to encompass new forms of thinking to combat constant threat vectors and attacks. Protecting against malware, brute force attacks, and malicious code, data engineers at Cisco, Microsoft, and others craft unique methods to counter the never-ending aggression.

Data Masking and data encryption are two similar technologies with different characteristics, purpose, and technique. Encryption converts plaintext into ciphertext using algorithms and a well-defined authentication key.

Information concealment by masking protects sensitive data by replacing characters with realistic, fictitious records. Even though the output after masking seems gibberish, the underlying data is unaffected.

There are forms of data protection on the same level as masking in terms of security and effectiveness. 

• Data Obfuscation involves introducing noise and randomization into the dataset, making it much more difficult to reverse engineer the database. This type of masking is perfect for protecting large sensitive datasets from poisonous mining techniques.
• Anonymization removes any identifying information from the data. This method makes it impossible to link the data back to an identity or specific information. Anonymization is used for securing analytics when the parties do not want to compromise the individuals' privacy.
• Tokenization replaces sensitive data with a unique identifier with no value of its own. Tokenization preserves the format of the original data but ensures sensitive information is never exposed.

Each method can be used with data masking to provide layered security for sensitive information. Appropriate data protection depends on the specific level of security required in the face of potential threats.

What is Data Masking?

Data masking may be the most important protection technology to surface in decades, with few knowing its existence. The exact origins of masking are cloudy. However, data masking technology and early forms of encryption have been used in industries such as cryptography and finance for decades.

The skyrocketing rise of database and data processing in the 80s and 90s led to extensive encryption and data masking deployment.

The technology of information concealment protects sensitive data by replacing characters with realistic but fictitious datasets. Data is structurally similar but is inauthentic and altered. Alteration of the data can be encryption, character shuffling, or substitution.

Even though the output after masking seems gibberish, the underlying data is unaffected.

Staging sites are often used to cleanse and transform the data prior to masking. Masked data is then delivered for testing and analytics. On the fly masking moves data from one environment to another, including a production or testing environment. On-the-fly masking is ideal for large organizations with continuous software development. 

Early forms of data protection were slow and unreliable, with substantial time lags. Managers asking for a specific report had to wait until the data was called up and formatted in the correct sequence. Data in transition, within a network, or sent to different endpoints, while never touching a drive, was prone to corruption.

Over time data science engineers overcame significant roadblocks in masking data within large corporate structures. 

1. Masked values must be consistent across several databases within the company. Depending on the situation, call center applications may get their information from the engineering group or the finance department. Data masking algorithm values in one database must be repeatable, meaning the data across the organization will always yield the same output.
2. Hackers should never be able to reverse engineer masked or obfuscated data. If a predator has the sense that a database can be reverse-engineered, they will need some degree of knowledge of a person's real-life identity in the company.
3. Applications within the masked dataset must operate as expected and without error. Social security number and credit card algorithm validations are examples of the required functionality. 

Several forms of masking emerged while implementing distinct concealment solutions for each industry.

• Static data masking replaces sensitive information at rest in non-production environments. Private concealment is a one-way transformation that cannot be undone. Static masking is used in analytics, testing, and training and is deployed on production database copies. This technique makes the information look real without exposing the original data.
• Dynamic data masking is prevalent in production environments and used to block access to sensitive information while data is in transit. Dynamic masking is performed in real-time while protecting sensitive data on demand. As a rule, dynamic masking is used in role-based applications, such as customer queries in health environments and read-only scenarios.
• Deterministic masking involves mapping two data sets so that one character replaces another. This type of masking is convenient but inherently less secure. For example, John Smith is always replaced by Jonathan Jameson. 
• Data scrambling reorganizes the characters in entirely random order. Scrambling data is a simple way to implement masking, but it can only be used on uncomplicated information and is less secure than other types of concealment.

What are the Challanges of Data Masking?

Malicious code and malware can infiltrate and damage computer systems, even though sensitive data is masked and encrypted.

• Spyware can gather information on a victim and their system and then be sent back to the hacker. If spyware is installed on a system that incorporates data masking, an attacker could bypass the concealment and install crippling malware.
• Trojan Horse malware is disguised as legitimate software and can be installed on a system with data masking. Attackers typically use social engineering techniques to gain access to a system. Once downloaded, the malicious code can design a backdoor to sensitive areas, spy on users, and steal data.
• Keyloggers are prevalent malware code predators use on systems with data masking: the keylogger code records logins and keystrokes used by the owner.

To Wrap Up

Data masking is an effective means of protecting sensitive information from increasingly sophisticated attack methods. Database technologies are becoming overwhelmed with the massive influx of information from every source imaginable, with no end in sight. Brilliant hackers will find ways to break into systems with masked or encrypted data. 

The inevitable weak link is the individual user and employees tasked with maintaining the data. Unwitting users are the downfall of every computer and network.