May 19, 2020

Why is Data Masking imperative to your Data Security Strategy?

Why is Data Masking imperative to your Data Security Strategy?

Have you ever wondered why superheroes wear a mask? Apart from their need to make a fashion statement, superheroes often wear masks to conceal their identity. This way they can protect themselves, their loved ones and often a whole country from danger. Data masking also follows a similar concept to protect your enterprise’s sensitive data from the eyes of your competitors, the public or even unauthorized employees.

In 2019, more than 100,000 people who applied through Alaska’s Division of Public Assistance fell victim to a data breach that compromised their personal information including social security numbers, income-, and health-related data.

Enterprises that overlook data protection regulations can be fined a massive sum of money. But that penalty is nothing compared to the reputational damage that destroys their customers’ trust. Data Masking is an effective tool that enterprises can adopt as part of a broader data security strategy to minimize the exposure of sensitive data.

Data Masking explained:

According to Gartner, by 2022, the consistent enterprise-wide use of Data Masking or similar de-identification techniques will increase to 50%, an increase from 20% in 2018.

Data Masking desensitizes enterprise data to protect them against breach or misuse. It gives your sensitive information a different identity that can only be unlocked by authorized personnel. It acts on a preset or custom-made set of rules to hide the true identity of the sensitive information before access or at the time of access, differing on a case-by-case scenario.

Data masking can be applied to several forms of sensitive data, such as Personally Identifiable Information (PII), Health data, Intellectual Property, etc. A PII, such as a Date of Birth, may contain 8 digits, for eg. 05-02-1994. When masked, the data may be presented as 00-01-2019. Here, the masking technique has hidden the true information with fictitious data while retaining the original format to protect it from exposure or misuse.

Why should you consider Data Masking while designing an effective Data Protection Strategy?

Architecting an efficient enterprise data security strategy can be extremely complicated and overwhelming. The ever-evolving data security threats may prove devastating to an enterprise and may even end its future at times. This amounts to multiple factors that an enterprise should consider when it aims to secure its databases. Data masking can help ease this complexity and make uncomplicated sense while designing a data security strategy. If you want to keep a piece of sensitive information hidden, just mask it.

Here are the top reasons why organizations should incorporate data masking in their data security strategy:  

  • Testing in Non-production environments- The most common factor that pushes enterprises to consider Data Masking is to desensitize data in its non-production environments.

Enterprises often duplicate production data for use by non-production teams. Functions like employee training, application development and testing, testing software patches and upgrades, building analytical models, etc. can necessitate the cloning of production data.

Non-production functions may include third-party workers or contract employees who may leave the production data unprotected intentionally or unintentionally. The data may even be subjected to tampering when it moves across the enterprise and on to cloud environments. Having more than one copy of sensitive data in an enterprise can make it increasingly vulnerable to misuse and data exposure.

Data masking enables enterprises to circulate critical data by maintaining their form and integrity while also keeping the actual data hidden. It also allows the seamless functioning of your enterprise as it doesn’t interrupt any business operation while acting on the real dataset.  

  • Shield against Internal threats- Authorized employees such as application developers, data analysts, etc. may have a dedicated need for data to perform their functions. But, this needn’t always mean access to real production data.

According to a recent study by the Ponemon Institute, there has been an alarming 47 percent increase in the number of insider-caused cybersecurity incidents in the last two years. Unintentional employee errors, contractors’ negligence, third-party oversight, etc. can lead to unnecessary data exposure. These result from providing legitimate data access to specific business units.

Data masking can minimize such threats by masking sensitive data and providing realistic test data to teams to ensure appropriate results at par with what real datasets would produce.  

  • Shield against Outside threats- Enterprises often share their data with market analysts and consultants while healthcare providers share patient data with medical researchers.

In addition to gaining a competitive advantage, such practices may lead to data breach, misuse or compromise. Employing Data Masking techniques can bring the risks down as they prohibit real data from exiting your enterprise while enabling you to share test datasets with authorized outsiders.

  • Compliance with data regulations- Data Masking is an effective remediation technique to govern data privacy in an enterprise. Data privacy regulations such as CCPA and GDPR regulate how data should be stored and handled in organizations.

-1798.150 under CCPA states-

Any consumer whose non encrypted or non redacted personal information as defined in subparagraph (A) of paragraph (1) of subdivision (d) of Section 1798.81.5, is subject to unauthorized access and exfiltration, theft, or disclosure as a result of the business' violation of the duty to implement and maintain reasonable security procedures and practices appropriate to the nature of the information to protect the personal information may institute a civil action for any of the following:

  • (A) To recover damages in an amount not less than one hundred dollars ($100) and not greater than seven hundred and fifty ($750) per consumer per incident or actual damages, whichever is greater.
  • (B) Injunctive or declaratory relief.
  • (C) Any other relief the court deems proper.

Businesses can avoid the above penalties and damages by including Data Masking techniques in their core data security strategy. This will allow them to encrypt and or redact consumers’ PII and prevent monetary and reputational damages caused by CCPA violation.

-Article 32 under GDPR specifically mentions that data controllers and processors should implement appropriate technical and organizational measures to ensure a level of security appropriate to the risk including the pseudonymization and encryption of personal data-

Taking into account the state of the art, the costs of implementation and the nature, scope, context and purposes of processing as well as the risk of varying likelihood and severity for the rights and freedoms of natural persons, the controller and the processor shall implement appropriate technical and organizational measures to ensure a level of security appropriate to the risk, including inter alia as appropriate:

  1. the pseudonymization and encryption of personal data;
  2. the ability to ensure the ongoing confidentiality, integrity, availability and resilience of processing systems and services;
  3. the ability to restore the availability and access to personal data in a timely manner in the event of a physical or technical incident;
  4. a process for regularly testing, assessing and evaluating the effectiveness of technical and organizational measures for ensuring the security of the processing.

Non-compliance with GDPR and CCPA will have businesses paying up to EUR 20 million and USD 7,500 per violation, respectively. Enterprises can consider leveraging Data Masking’s data security capabilities to avoid such penalties and ensure compliance.

Apart from the above, an increase in data migration to public clouds, a rise in the use of data analytics, etc. also fuel the demand for Data Masking technology.

Types of data masking:

A data breach at the Oregon State University compromised the PII of 636 students in 2019.  The breach exposed critical information such as Social Security numbers, addresses, telephone numbers, etc. of the students

The probability of such breaches can be lowered by using an efficient sensitive data security software that contains multiple data masking techniques to protect your organization’s critical database.

Kogni provides several data masking techniques that alter your sensitive data from their original state to ensure that they stay protected-

  • Format Preserving Mask- Here, the masked data looks similar to the original data and can be deployed in non-production environments. The format of the original data is maintained in this type of Data Masking technique while effectively protecting the true identity of the underlying data.

  • Hashing-  Hashing is a data masking technique that transforms your sensitive data into fixed length, obscure alphanumeric strings.
  • Character Masking- This technique masks your sensitive data with a string of asterisks.
  • Obfuscate- Data Obfuscation is yet another data security technique that copies and scrambles sensitive information to conceal its true identity.
  • Perturbation- Data perturbation adds ‘noise’ to your sensitive database while enabling individual record confidentiality.
  • Synthesize- This technique generates random data to hide the original data input and gives out a realistic data with the same format.
  • Tokenize- This data security technique replaces the given critical data with desensitized placeholder tokens. It also preserves the relationship between the concealed original data and the generated token values enabling the retrieval of the true data.
  • Encryption- Kogni’s Encrypt feature encodes any critical or sensitive information within unstructured, semi-structured and structured data files and returns an unreadable/indecipherable output.
  • Redact- Kogni de-identifies any sensitive information within an image using the Redaction technique. It inspects the image for text, discovers sensitive data within the text, and then returns an image with any matching sensitive information replaced with a hash symbol (#)

Kogni’s uncomplicated Data Masking technology is easy to implement and is a comprehensive approach to your data security strategy. With Kogni, you can make an affordable investment to achieve a secure environment that will preserve your enterprise’s reputation and bring in customer loyalty for years to come!