Cloud Data Security Best Practices

The cloud is now ubiquitous for organizations that compete with data. But, as with any other commodity, it comes with risk. Do the time and cost savings, flexibility, and capacity that the cloud offers outweigh the threat of becoming the next headline-generating data breach or fine?

The answer is yes, if the right processes and controls are in place. Cloud data security is achievable – and with a few best practices, it may be easier than you think.


Why is Cloud Data Security Important?

The rapid migration of data from on-premises environments to the cloud changed the way organizations think about data security. Whereas on-prem hardware and software are stored in-house and maintained by IT teams, cloud technologies exist on remote internet servers and the provider manages upkeep, including product updates and backups.

As cloud services have matured, so have their security mechanisms. The perception that on-prem is more secure and controllable than the cloud is no longer prevalent. Yet, the exponential growth of data volumes, users, and sharing means that data leaks and breaches still abound. More than 80% of organizations have reported a cloud data security incident and 97% of data engineers surveyed face challenges implementing data access controls to help prevent such incidents.

Not only are data security breaches costly – the average price tag on data breaches is $4.24 million per incident – but they can also damage a company’s reputation. A survey of data professionals found that within the next two years, customer trust is expected to be the top motivator for improved data policy management practices. Failure to secure cloud data is a surefire way to lose that trust, and the revenue that comes with it.

With customers on high alert about how their data is being handled and jurisdictions worldwide passing regulations dictating its use, cloud data security has never been more important or scrutinized.


7 Cloud Data Security Best Practices

While there are no guarantees in life, following the best practices for cloud data security is the best approach to mitigate risk and keep your data safe. Here are the top seven.

1. Understand Your Risks
You wouldn’t buy an insurance policy that was meant for someone else. So, why would you take a one-size-fits-all approach to data security? The most effective data security strategy is the one that’s tailored to your organization’s needs – which includes its risks.

Before making any decisions about how to secure your cloud data, look at the bigger picture. This will help ensure all elements of your data environment are taken into account, so you can achieve truly end-to-end data security. This involves understanding:

  • Tech: the tools that are in your tech stack and their current data security measures
  • Team: the people who are involved in data use and policy management, including data owners; data architect and engineering teams; governance, risk, and compliance stakeholders; and end users
  • Rules & Regulations: the internal and external requirements that apply to your organization’s data use
  • Behaviors: the ways in which data is accessed, used, and shared, both within your organization and with third parties
  • Data Types: the categories of information your organization collects, including personal and otherwise sensitive data

Examining these factors holistically can help pinpoint what data needs to be protected, where it resides, who is responsible for it, and which regulations apply to it. This helps clarify the potential risks to data. For instance, if you have protected health information (PHI) in a database with elementary access controls and no one designated to oversee its security, there is a high risk of unauthorized access and HIPAA violations.

Once risks have been identified, your organization can determine its risk tolerance, or the amount of risk deemed acceptable in pursuit of key objectives. This will vary based on the feasibility of an adverse event actually happening, as well as the degree of impact it may have. Ultimately, understanding risk will help identify data security blind spots so you can be prepared to mitigate or respond to risks.

2. Educate Data Users
The best-case scenario for cloud data security is responding to threats before they materialize. That makes data users the first line of defense against unauthorized data access and use. However, they are only effective if they’re educated about how to detect a threat.

Educating data users across your organization – from IT and engineering to marketing and sales – can help stave off attacks that could compromise network security, and by extension, data security. Many companies mandate cybersecurity trainings that teach employees how to detect phishing, social engineering, and other threats, but emphasizing the potential downstream impacts could shed light on why vigilance is critical for even non-technical teams.

For teams that regularly interact with data, creating awareness about data security frameworks also helps cultivate accountability for safe data practices. Explaining the mechanisms in place to protect data, such as discovery, access control, and monitoring, can help avoid scenarios in which data consumers find workarounds to get to data faster, which could increase risk exposure. Similarly, having clear roles and responsibilities for data security, such as who creates, manages, approves, and enforces policies, is essential for bypassing ambiguity, and streamlining threat detection and response.

3. Choose the Right Data Security Framework
As with risk, data security is not one-size-fits-all. What works for one organization’s objectives might be insufficient for another. That said, NIST has identified five core functions of a cybersecurity framework that all data-driven companies should prioritize in order to keep their assets safe: identification, protection, detection, response, and recovery. The best data security strategies will incorporate these functions, but the trick is getting them to work in tandem across increasingly complex data environments.

As of 2022, nearly 90% of organizations had a multi-cloud data strategy and of those, 80% used both public and private clouds. But to get the greatest ROI from those platforms, they must all work efficiently and in sync – which includes consistent data security and accessibility. It’s therefore not hard to see why relying on cloud-native governance and access controls could be a headache for data teams. Relying on built-in access controls in a multi-cloud or hybrid environment is not sustainable, not to mention that it could result in duplication and mismatched levels of data security, disparate policy enforcement, and elevated risk of exposing sensitive data.

Just as the separation of storage and compute platforms revolutionized data management with flexibility, efficiency, and cost savings, it is becoming increasingly important to separate policy from data platforms to reap many of the same benefits. The right cloud data security solution will integrate with all of your platforms, and allow you to centralize policy management so access controls can be deployed universally with minimal overhead.

4. Discover and Classify Your Sensitive Data
You can’t secure what you don’t know exists. However, organizations are liable for all their data – and as such, it’s their responsibility to make sure it’s accounted for and protected.

Sensitive data discovery resolves that dilemma by identifying information in a data set that could be considered private or protected, so that it can be tagged and classified for streamlined access control implementation. Personally identifiable information (PII) like social security or driver’s license numbers, and protected health information (PHI) including medical history and insurance information, are both considered sensitive data, as is information like trade secrets and intellectual property.

With data use, assets, consumers, and regulations continuing to grow at a rapid clip, the ability to identify and classify sensitive information is key to getting ahead of potential threats and averting noncompliance penalties. As suggested in NIST’s framework, identification is an important first step and best practice in establishing cloud data security.

5. Implement Dynamic Access Controls
Centralizing policy management and enforcement is key for holistic data security, but the quality of the access controls is equally important. Legacy data access control methods such as role-based access control (RBAC) permit or restrict access based on a user’s role within the organization. Access permissions are implicitly predetermined with RBAC, and data teams must explicitly determine the privileges associated with each role.

This approach only provides static enforcement and leads to “role explosion,” or the exponential growth of user roles and corresponding policies. Role explosion puts a substantial burden on data engineers, who must create new policies as user groups, regulations, and use cases evolve. As the number of policies increases, it becomes more difficult to understand which roles belong to which access permissions. Ultimately, this negates the very reason for implementing RBAC – to make data access control more efficient.

A cloud data security best practice that will help streamline secure data operations is implementing dynamic, scalable data access controls. Specifically, attribute-based access control (ABAC) is a more modern, future-proof approach to governing access. Unlike RBAC, ABAC permits or restricts access based on multiple dimensions, including traits about users, data objects, intended actions, and the data environment. Because these attributes are independently provisioned, access decisions are enforced dynamically without the need to create new policies. This makes the ABAC approach, and by extension cloud data security, more scalable and efficient than RBAC. A study by GigaOm found that ABAC reduced policy burden by 93x versus RBAC, which could equate to a cost savings of $500,000.

In addition to access control, dynamic data masking, encryption, and multi-factor authentication provide enhanced security and multiple lines of defense to protect cloud data. Following the zero trust principle, which posits that no user should be implicitly trusted with access to data, helps establish where security and privacy controls must be implemented within your data workflows.

6. Monitor Data Use for Threat Detection
Just as access control and policy enforcement are difficult to manage without the right tools, monitoring how that sensitive data is being used and detecting threats can elude data teams. This is particularly true as data volumes and users grow. Therefore, the best cloud data security strategies incorporate data monitoring and detection capabilities, so risks can be proactively addressed.

The need to monitor data and user activity is especially critical for organizations that collect and utilize sensitive data, like personally identifiable information (PII) and protected health information (PHI). While mechanisms like multi-factor authentication, firewalls, and access controls provide defenses against unauthorized data use, there is always the possibility of a system breach or failure. Always-on data detection is the best way to mitigate the likelihood of such a scenario spiraling out of control.

Data detection capabilities help data owners and governance, risk, and compliance stakeholders answer questions like:

  • What does data access look like over the past 24 hours?
  • Who accessed sensitive data, and for what purpose?
  • What are the most trafficked data sources containing sensitive information?

Cloud data security solutions that offer data monitoring and detection can help organizations manage internal, privacy, and data residency risks, and enable data security posture management and analysis. When combined with data discovery and access control capabilities, this helps organizations achieve broad spectrum data security and minimize potential gaps.

7. Have a Plan for Incident Response and Remediation
Data monitoring and detection are important safeguards against data leaks and breaches, but they are not the end all be all. In the event of a data security incident, having a plan to mitigate and remediate the problem will help ensure you’re able to get back up and running as quickly as possible.

As with medical emergencies, every second counts when a security breach occurs. To ensure communication is clear, there should be a chain of command defining who will oversee response efforts and who must be immediately informed.

Once that has been determined, the first step in an effective response is to identify and contain the source of the issue so it does not spread. This may entail revoking access privileges, disconnecting impacted devices, or removing unauthorized users from the system.

The immediate next step is to eradicate the cause so it cannot happen again. This involves taking a scrutinizing look at the systems in place and pinpointing points of failure. Did the attack introduce any malware into the data environment? Were the policies in place insufficient? Was there sensitive data in your system that was not accounted for? Asking yourself what went wrong will help determine the system patches and updates needed to avoid future incidents.

Once the source of the incident has been addressed, it’s time to think longer term. Have the tests and backups been done to safely get systems back up and running? What tools or processes have been introduced to avoid another breach? Ensuring that your data security protocols can be trusted is a key factor in effective remediation efforts.

Finally, revisit your data security strategy and response plan to update both with lessons learned. This will make your team better prepared to not just temper the impacts of a data breach or leak, but avoid it in the first place.

Putting Cloud Data Security Best Practices into Action

Regardless of how mature your organization’s data security processes are, there’s always room for improvement. To put the tips above into action and strengthen data protection, this guide lays out business, technical, and process-oriented best practices for data teams with nascent, emerging, and mature data security strategies.