Data classification is the process of organizing and categorizing data based on its importance and sensitivity. Classifying data lets you protect your most critical assets, make informed decisions about data access and retention, comply with regulations, and mitigate risks.
In this post, we’ll explore some of the challenges that can complicate cloud data classification, along with the benefits that come with this crucial step—and how a DSPM tool can help make the entire process much simpler.
Why do you need data classification?
When you collect data as part of your business, whether it’s payroll records, credit card data, Social Security numbers, medical records, financial records, intellectual property (IP), or anything else, you need to handle it responsibly.
Data security today is focused on the entire data lifecycle. Handling data isn’t something any organization can take lightly, which is why data security has also been the subject of increasing regulation. That means you need to be aware of proper data handling strategies.
At every stage of the data lifecycle, mishandling data could open your business up to financial loss and reputational damage.
Yet companies often overlook entire stages of this lifecycle in their data security plans. For instance, the final destruction phase might get skipped, leaving data remnants on storage devices even after disposal. Or data may not be as well guarded while in transit, leaving it open to man-in-the-middle and other attacks that take advantage of weak encryption.
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Challenges of data classification in the cloud
Cloud data adds a whole other world of complexity and is more vulnerable for several reasons.
It’s highly distributed, making it tough to track down. It’s ephemeral, which means you can’t always destroy data properly. The shared responsibility model of cloud providers can muddy the waters when it comes to understanding who needs to do what. And finally, you may also have large quantities of shadow data lurking in places you don’t control.
Let’s look at a few of the biggest obstacles in the way of classifying your cloud data.
Cloud environments are constantly changing
Cloud is called cloud because, well, it’s like a cloud: constantly shifting shape based on demand and dozens of other factors. Infrastructure and data are constantly being added, modified, and removed. Plus, you’ve got multiple interconnected components—VMs, storage, databases—making it difficult to identify, track, and classify data.
In addition, third party-providers add complications when it comes to figuring out where your data is, how sensitive it is, and who needs access to it. Subcontractors, for example, may have different data security practices and standards, increasing the risk of security incidents.
Data sprawl and silos
Multi-cloud environments breed inconsistency, almost by definition. Different providers have different security rules, and as your tech stack grows, it becomes harder and harder to keep track of which rules have been applied where. And understanding who has access to which data? Almost impossible.
This can lead to two major problems: data silos and data sprawl.
When you have diverse data types stored in a range of different locations in the cloud, it becomes difficult to manage consistently. Access controls or architectures implemented in one “silo” need to be applied to every other “silo.” This is often a painstaking manual process.
Data sprawl, a subset of the bigger issue of cloud sprawl, happens when data proliferates and duplicates to a point where businesses can lose control. This makes it tough to manage your data and comply with regulations, and it significantly raises the potential for data breaches.
Classifying siloed or sprawling data, especially unstructured data, is notoriously hard using automatic methods. And manual methods are tedious and time-consuming.
User awareness and training needs
Data classification is a big, organization-wide project. That means two things in terms of your users: You need a high degree of buy-in, and you need customized training, because every organization has different needs and industry standards when it comes to classification.
Ideally, you’ll automate as much of the classification process as possible, but there is still some manual intervention required. And if your team isn’t fully on board, and isn’t properly trained, you’re going to wind up with human error—leading to misclassification (oversecuring or undersecuring data) and possibly even accidental disclosure of sensitive data.
How data classification works in the cloud
The goal of data classification is creating and implementing a framework to right-size the security measures your organization needs to take. Not too much, as this is costly and hampers your agility, and not too little, because this opens you up to unwanted risk.
The process of putting this in place will involve cross-disciplinary teams and stakeholders from multiple ranks of your organization. Together, you’ll move through the following steps, which we’ll look at in a little more detail below.
Inventory cloud data: Identify and examine the data and where it’s located. Some tools make this step visual, helping you picture your entire cloud environment more easily.
Plan a classification scheme: Determine what types of data need to be protected and which labeling categories you will need.
Tag and label data: Sensitive data discovered in the inventory process will be tagged with a more secure classification level. Ideally, this step will involve some degree of automation through strategies like content- or context-based classification, but manual classification is usually necessary as well.
Apply classification policies: Roll out classifications as planned, adjusting as needed. For example, if new data types are discovered, or if regulatory compliance rules change over time.
Perform ongoing monitoring and updates: Classifications and data types change, and no classification is static. You’ll have to involve stakeholders from across your organization and adjust your policies and procedures as needed.
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Benefits of classifying cloud data
While handling data is complex, classifying cloud data lets you use your data more securely. Not only that, but knowing exactly what and where your sensitive data is lets you save money and save time when it counts the most—during an audit or security incident. Here are some other benefits:
Enhanced data governance and compliance: Cloud data classification gives you clarity on data ownership and can improve your consistency in data handling. And with a clear audit trail, compliance reporting becomes much simpler.
Better risk management: Cloud data classification helps you prioritize your security efforts and understand where you can improve your program. Insight into data sensitivity helps with risk assessment and management, and it also helps you create business continuity plans that make sense based on actual risk.
Reduced attack surface: Cloud data classification lets you build uniformity and consistency into your security program. This minimizes unauthorized access and lets you target security controls to focus on your most sensitive data.
Faster incident response and breach containment: Finally, cloud data classification helps you deal with incidents in progress. You’ll know exactly where your sensitive data resides, so you can focus containment efforts. You’ll also have a better understanding of the impact and scope, which helps you contain the blast radius and prioritize remediation efforts.
Data security and classification
Cloud data classification is an important step on the way to implementing granular access controls and encryption based on data sensitivity levels. This is the key to least privilege, zero trust, and many other best practices.
Understanding the level of confidentiality and risk for each data type lets you put safeguards in place to protect your most sensitive assets. More importantly, it’s a key enabler for moving towards a data-centric security approach.
Traditional approaches
Traditional approaches to security tend to be perimeter-focused, building “castle walls” around your environment. Traffic is checked at the “gate.” The drawback? Once a user is inside, it’s easy to gain access to sensitive data.
Network-centric approaches build on perimeter-focused techniques by adding access limitations through micro-segmentation and other measures like behavioral traffic analysis. This approach can protect endpoints, networks, and applications. But without a deep awareness of where your crown jewel data is located, it can’t ensure airtight coverage where you need it.
Data-centric security approach
A data-centric approach, on the other hand, works from the assumption that perimeters and networks can and might be breached. So it focuses on protecting the data itself.
Data-centric security is platform-agnostic and works well for today’s distributed workforce. And it saves teams work because monitoring and alerting are prioritized based on classification levels. Data-centric tools let you know immediately when your most essential assets are at risk, so you can lock down your data and minimize exposure.
But data-centric security works from the assumption that you know exactly where your data is and what type of data it is. So you first need to find and classify all your data—an extremely daunting job.
Fortunately, today’s data security posture management (DSPM) tools are up to the task.
The role of DSPM in data classification
DSPM is a tool or toolset that adds data-centric security to your existing security stack. It does this with a number of data-related capabilities:
Data discovery and classification
Identity access control management (IAM)
Encryption for data at rest and in transit
Data loss prevention (DLP) to monitor and prevent unauthorized data exfiltration attempts
DSPM extends traditional data classification methods by automatically discovering sensitive data across cloud environments and assessing its security posture.
DSPM performs automated discovery of all your sensitive data, giving you visibility across multi-cloud environments, SaaS platforms, and hybrid infrastructures in real time. With its ability to classify data based on context like metadata and usage patterns—as opposed to simply using static labels—you get more accurate risk assessment and adaptive classification compared to static methods.
Following data discovery, DSPM lets you define custom classification policies at a granular level, giving you precise control. For example, it allows you to differentiate between different levels of data criticality, especially for highly sensitive data like PII, financial records, and intellectual property. This in turn makes it simpler to meet internal data-protection standards or regulatory mandates like GDPR, HIPAA, or PCI DSS.
With classification policies in place, DSPM continuously monitors data and its environment. Unlike traditional static approaches, DSPM will dynamically reclassify data as needed as usage and other factors change to proactively mitigate risks. It also aligns with access control mechanisms already in place, helping you make sure that only authorized users have access to sensitive data.
Wiz DSPM
When DSPM is integrated with other security tools as part of a cloud native application protection platform (CNAPP), you get the highest possible level of protection for your data by aligning access controls and enabling automated incident response. As part of a CNAPP, DSPM also helps cut alert fatigue and prioritize critical security events by providing context-aware alerts for your top-priority data.
Wiz is a CNAPP solution that builds DSPM right into the mix. It provides all the benefits of DSPM to protect your sensitive data while also performing other mission-critical security tasks.
You shouldn’t have to choose between data-centric security and traditional approaches that focus on networks and apps. Both are important, and that’s why Wiz supports both.
Since your teams have so much on their plate, why not make their lives easier?
Wiz DSPM lets your teams work more effectively because it correlates data from all your security tools, so you can…
Uncover hidden vulnerabilities and apply consistent policies
Assess data risks alongside other cloud risks (to capture more threats)
Enhance compliance with regulatory requirements thanks to continuous assessments
Wiz is easy to roll out and configure. There are no agents to deploy, and you can manage your entire environment through a single pane of glass so that nothing falls through the cracks.
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