What Are Data Anonymization Best Practices? The Top 5 Tips

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The data anonymization landscape is broad and constantly evolving as new technology comes online, regulations are passed, and the limits of what can and can’t be done with data expand. This constant state of flux begs the question: where should you begin when it comes to anonymizing sensitive data?

Regardless of your organization’s data masking standards, there are a handful of best practices that all data teams should know in order to ensure data anonymization measures are sufficient and sensitive data remains private.

Why Do I Need Data Anonymization?

To understand how these best practices emerged, it’s important to start with a baseline – what is data anonymization and why do you need it in the first place?

Data anonymization aims to transform sensitive data by encrypting or removing it from a data set. “Sensitive data” is a blanket term that encompasses:

  • Personally identifiable information (PII), like names, home addresses, and social security numbers
  • Protected health information (PHI), including medical records and insurance information
  • Non-personal commercial sensitive information, such as supplier information or intellectual property (IP)

While sensitive data analytics is beneficial in some ways – for example, when making a holistic and informed medical diagnosis – it can also be easily exploited and used to cause harm. Despite a growing body of data compliance laws and regulations aimed at reducing risk and increasing the penalties for mishandling sensitive data, data breaches are on the rise and cost organizations an average of more than $4 million each.

Data anonymization is not just one of the most effective defense mechanisms for organizations to avoid noncompliance fines, but in some cases it is mandated by law. This is true for the EU’s pioneering General Data Protection Regulation (GDPR), which is often considered the world’s most influential data privacy law. To avoid financial and reputational damages, and enable advanced analytics that can make or break an organization’s success in today’s world, data anonymization is an essential part of a data team’s toolkit.

What Are the Top 5 Data Anonymization Best Practices?

It can be hard to know where to start when implementing a successful data anonymization strategy. These best practices help remove the guesswork:

1. Understand Your Data

Starting to build a data privacy and anonymization strategy without knowing what data you have is like making a recipe without checking the ingredient list or your cupboard. Flying blind is likely to lead you down the wrong path, causing confusion, frustration, and disappointing outcomes.

Getting a handle on your data is an important first step in the anonymization process. Organizations that do this successfully have a way to monitor what type of sensitive information they are collecting, storing, and using, without having to assign someone the task of manually combing through any and all data. For companies with limited data sets and no plans to grow, the manual approach might be feasible; but for most modern organizations, it is both unscalable and subject to the risks of human error.

The easiest way to start understanding your data – and continue to do so as new data enters your ecosystem – is to implement a tool that automates sensitive data discovery and classification. This removes the need for manual oversight, increasing data engineering productivity and confidence that all data is accounted for. Solutions that integrate with data catalogs like Alation and Collibra can pull classification metadata from the catalog for simple, unified discovery, tagging, and policy implementation. Streamlining the data discovery process pays dividends in both time saved and data security.

[Read More] How to Simplify Snowflake Security with Alation and Immuta

2. Prioritize Your Data Use Cases

Continuing our earlier analogy, knowing what ingredients you have, but not what you want to make with them, doesn’t get you any closer to having a finished meal. Likewise, unless you know how that data is being used, it’s impossible to determine what must be done to protect it.

The most efficient way to prioritize your data use cases is to consult the people using it – data consumers. Auditing how and why data is being used, both at the organizational level and the departmental level, will allow patterns to emerge that can help inform the scale and intended outcomes of sensitive data use. Repeat this same process with data platform teams to pinpoint the sources of data requests and any bottlenecks.

Finally, bucketing use cases will help streamline which data anonymization techniques are best suited for your organization, as well as clarify which rules and regulations apply to your data.

3. Map Relevant Legal Requirements

With a handle on your sensitive data and how it’s being used, you can better determine which data protection legislations will influence your data anonymization process. Since each regulation has different standards and language about what it takes to satisfy them, this step should involve your legal and/or compliance team.

For instance, if you collect and use California residents’ data, you must ensure your data practices and corresponding anonymization measures abide by the California Privacy Rights Act (or CCPA 2.0). Does your organization process, store, or transmit credit card information? You’ll have to plan for the PCI DSS. On top of these and other relatively well known regulations, it’s important to account for any relevant data localization laws, data use and sharing agreements, and industry-specific standards. Holistically assessing all pertinent rules can help narrow in on what tools and techniques are best for your organization’s needs.

[Read More] The Five Data Localization Strategies for Building Data Architectures

4. Assess Your Data Stack

The growing need for data access control has led many leading cloud data platforms to offer some level of built-in protection. These data security and privacy controls are typically fundamental, but lack the granularity necessary to ensure data is sufficiently anonymized in accordance with various rules and regulations. Furthermore, relying on controls that are tied to a specific platform limits a data team’s ability to modernize their existing tech stack, as each newly added technology requires creating and implementing the same controls from scratch. This opens the door to inconsistent data policy enforcement and increased risk.

When assessing your data stack, evaluate the data anonymization capabilities native to your platform(s) against the legal requirements, use cases, and types of sensitive data you’re handling. Are those controls sufficient for your current and anticipated future needs? Are they scalable across lines of business and potentially other cloud platforms?

A platform’s data anonymization capabilities should be given the same weight as its other features – and if gaps exist, it is worth investing in a cloud-agnostic solution.

5. Future-Proof Your Capabilities

Data use today is nearly unrecognizable compared to a decade ago, but one thing remains the same: early adopters of cloud technology are well ahead of their slower-to-adopt counterparts when it comes to effectively leveraging their data. Future success with data is built on decisions that are made today – which is why data teams that invest in scalable, agile data anonymization strategies will have a competitive advantage over those that do not.

One key mechanism to ensure that data policy implementation is adaptable and able to grow in tandem with organizational growth is to separate policies from platforms. Centralizing policy management in a single access control plane ensures consistent policy enforcement, regardless of how many data users, sources, or platforms are added. Dynamic, attribute-based access control also gives data teams the flexibility to write data policies once and apply them everywhere, with context-informed access decisions that are made at query runtime.

No one knows what the future of data use holds, but ensuring that data anonymization capabilities are agile and easily scalable makes navigating future needs a less daunting task.

What Are the Top Data Anonymization Tools?

Following these best practices and assessing your organization’s needs will help determine which data anonymization tool is best positioned to reach your data-driven objectives. While there are a multitude of platforms that claim to enable data anonymization, some of the most well known are:

  • Immuta, the leading data access platform that delivers scalable data security and privacy controls that are dynamically applied and easily auditable
  • ARX, an open source software that is able to transform data and analyze its utility post-anonymization
  • Clover DX, which is focused on anonymizing production-level data sets

Facilitating the discovery, security, and monitoring of data across any cloud platform, the Immuta Data Access platform is uniquely positioned to help resolve questions asked in each best practice stage. By automating sensitive data discovery and classification, using attribute- and purpose-based access control to dynamically enforce data policies and advanced privacy controls – including anonymization techniques – at scale, and providing always-on monitoring and auditing capabilities, Immuta ensures that users have access to the right data at the right time – so long as they have the rights.

See how easy it is to transform data with anonymization when you try our free self-guided walkthrough demo.

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