As data use has become ubiquitous, data breaches have followed suit. Though down from a peak of 125 million compromised data sets in late 2020 – which was at least partly attributable to the sudden shift to remote work during the pandemic – data breaches still expose millions of data assets every day.
Avoiding data leaks or breaches – and the resulting damages to your organization’s reputation, trustworthiness, and revenue – requires an advanced, proactive approach to preserving data security and privacy. Data obfuscation does just that.
In this blog, we look at what data obfuscation is, its benefits, the most common obfuscation techniques, and key capabilities for implementing it.
Data obfuscation is the process of scrambling data to obscure its meaning. By hiding the data’s actual value, data obfuscation renders it useless to attackers while retaining its utility. Even if a breach were to occur, obfuscated data would be useless to attackers, and therefore remain protected.
Data obfuscation is often used to protect personally identifiable information (PII), such as full names, home and email addresses, bank account numbers, and health records. Organizations use data sets containing PII to analyze trends, provide customer service, and more; obfuscation allows them to do so while maintaining data privacy compliance standards.
Let’s say a developer at a pharmaceutical company is building an application that aggregates clinical trial data in order to analyze trends and predict outcomes in real time. To do so, they need to leverage PII and protected health information (PHI) in a non-production environment. With legacy controls, the developer would either have to request and wait to be granted access from a central IT or governance team – which could take weeks – or else develop an inaccurate or imprecise application built on low fidelity data.
However, dynamic PHI and PII obfuscation techniques allow the developer to access realistic data with little to no downtime. Since obfuscated data no longer contains personal and otherwise sensitive information, it is both less risky to use and more valuable.
One of the top benefits of data obfuscation is its ability to secure non-production environments, like the one in the example above. This minimizes the risks of building new applications and programs. As organizations focus on developing proprietary types of AI models, deploy data products, and unlock new innovations, data obfuscation will be critical for ensuring those initiatives are done quickly and securely.
Obfuscating data is key for effective and secure data sharing. Despite being linked to better business outcomes, many organizations struggle to share data – even internally. Regulatory requirements like the GDPR, data localization laws, industry standards, and contractual obligations all restrict how data can be transferred across lines of business and external partners. By altering PII, obfuscation mitigates risks of inadvertent data exposure or access by bad actors.
Data obfuscation also enables self-service data access by allowing data teams to develop, test, analyze, and report on data, without having to wait for data to be individually provisioned. This facilitates decentralized data architectures and increases data supply chain efficiency by reducing the burden on IT and governance teams. Obfuscating data increases confidence that sensitive data use won’t come at the expense of customers’, employees’, or users’ data privacy.
The three main techniques used to obfuscate data are data masking, data encryption, and data tokenization. Each is a subset of data obfuscation, but while encryption and tokenization are reversible, data masking is not.
Data masking creates a fake but highly convincing version of data. It allows functional data sets to be used for demonstration, training, or testing, without revealing actual user data.
Essentially, data masking uses the same format as existing databases, but changes datas’ values. The changes cannot be reverse-engineered to reveal the original data points – numbers and characters may be scrambled, hidden, substituted, or even encrypted.
Encryption scrambles information in a data set and can only be reversed by authorized users with the necessary encryption key.
In asymmetric encryption, one public key and one private key are required to decrypt data; in symmetric encryption, just one private key is necessary for encryption and decryption. Either way, the data can’t be manipulated, analyzed, or used in any way until it’s decrypted.
Data tokenization is a form of obfuscation where the replacement value, called a “token,” has no extrinsic meaning.
Key segregation separates the key used to generate the token from the pseudonymized data through process firewalls. The token is a new value that is meaningless in other contexts, and it’s not possible for an attacker to infer the original data’s values using the token.
While these techniques are the most common, others exist as well. Nulling, for example, replaces part of the data with null-valued variables, while blurring offsets data values by a predetermined amount. Randomization, as the name implies, randomly reorders characters and numbers.
When looking for the right tool to obfuscate your organization’s data, there are several capabilities to consider. Specifically, you’ll be best served with a tool that enables:
Immuta de-risks data by automating data discovery, security, and monitoring. Our data security platform allows you to obfuscate sensitive data quickly and at scale, with confidence that you have full coverage across any platform in your tech stack. By de-risking data, Immuta gives you the freedom to deliver more value from it.
Learn more by requesting a demo with our team.
