Top 5 Cloud Data Security Best Practices for Cyber Awareness Month and How to Get There

Top 5 Cloud Data Security Best Practices for Cyber Awareness Month and How to Get There

Cloud data security is a serious challenge most organizations face today. The cloud was designed for collaboration where every file or data element can be shared with anyone halfway around the globe. Cloud data can also be copied, duplicated, modified and passed along very easily. To illustrate the challenge, think about 100 different variations of a redlined sensitive contract that need to be protected, and consider that each variant can have different access privileges.

Saying that cloud data security is a complex issue may be the understatement of the year. But considering this time of the year, with October being Cyber Awareness Month, let’s consider the Top 5 best practices for addressing today’s cloud data security challenges:

  1. Identify all sensitive data in the cloud without burdening information security teams to craft rules or complex policies. The list of cloud data that needs protecting will be long, and includes intellectual property, financial information, and PII/PCI/PHI.
  2. Know what data is being shared with whom, including everyone spanning internal users and groups to external third parties.
  3. Track data and its lineage as it moves across the cloud environment.
  4. Quickly identify where cloud data may be at risk and provide actionable insights. For example, sensitive data discovered being shared out of accordance with corporate security guidelines or where access or activity violations are happening should trigger major red flags.
  5. Remediate those issues as they are happening. For example, look to promptly fix access control issues or permissions, or disable sharing of a sensitive file that ought not to have been shared.

It might be debated that there are other best practices to consider, but it can’t be debated that these five best practices are critical for securing cloud data and keeping it safe.

However, identifying best practices for cloud data security is one thing, but effectively executing on them is another. This is because enterprises struggle with three important data challenges. First, there is massive growth in data, often it increases exponentially from year to year. Second, there is massive migration of data to the cloud. And third, the data that is worth protecting has become a very complex environment.

All of these factors present unique challenges to cloud data security. Traditional ways of protecting data like rule writing to discover what data is worth protecting or relying on end-users to ensure that data is shared with the right folks at all times simply doesn’t work in the cloud environment where it is now simple for employees to create, modify and share sensitive content with anyone.

A new approach called Data Security Posture Management (DSPM) is proving itself as a key technology area to help enable these cloud data security best practices. DSPM identifies and remediates risks to cloud data, powered by automated tools that make it possible to secure content at an atomic level without unnecessary overhead or new IT skills.

To understand DSPM, consider the similarly named Cloud Security Posture Management (CSPM) category. These solutions improve security by targeting cloud configuration errors, and they were a response to a host of security breaches related to misconfigured Amazon S3 data storage buckets.

Like CSPM, DSPM also focuses on misconfigured access privileges that can lead to data loss. DSPM solutions, however, tackle a more extensive and complex threat surface. A moderately complex cloud estate may house a few dozen storage instances and accounts for a handful of administrators. Contrast that threat surface with the complexity of an organization’s entire collection of cloud data, which can run to tens of millions of files, which is what DSPM protects.

The rise of automated DSPM solutions offers four capabilities essential to robust data protection and following these best practices:

  • Content discovery and categorization that provides the proper context for evaluating security best practices
  • Detection of access misconfigurations, inappropriate sharing, and risky use of email or messaging services
  • Evaluation of risks associated with data access and use
  • Risk remediation with the flexibility to tailor actions to suit business requirements

Unlike CSPM, where protected assets – storage buckets, administrative interfaces, online applications, and the like – are well-defined and understood, user-created cloud data is far more complex. Content categories range from valuable source code and intellectual property to regulated customer information and sensitive strategic documents. Accordingly, content discovery and accurate, granular categorization are essential precursors to effective DSPM and following these best practices.

Detecting misconfigured access settings, overshared files, or the use of risky channels (like cloud-based collaboration tools) is especially challenging. Even with highly accurate data categorization, hard and fast rules surrounding who can and can’t view a specific data category usually don’t exist. It’s a high-stakes problem because over-constrained data can quickly impact business operations and agility, while overshared data is a potential security risk.

Of course, simply finding at-risk data doesn’t cover all these best practices. Assessing risk, remediating misconfigured access permissions, and fixing sharing errors are important best practices that complete the DSPM cycle. There’s no magic bullet: Different organizations have different definitions of what’s critical, what’s trivial, and what’s at risk. Evaluating and quantifying risk gives focus to the process of fixing it.

All these best practices – categorizing content, detecting misconfigurations, and analyzing risk – can be accurately completed in DSPM solutions using deep learning technologies. With deep learning, the data (and related information about storage and usage) tells a rich and valuable security story. Capable deep learning solutions autonomously categorize data; then compare access configurations, storage locations, and data handling practices across similar files to spot and assess risk.

Data Security Posture Management protects your organization from data loss and breaches. Understanding your data, assessing risk, and remediating overly permissive access to sensitive information is at the heart of DSPM. Accurate, autonomous DSPM forms the foundation for more effective access control and following best practices for data security.


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