What Is Cloud Data Security?

Shifting to the cloud is a necessary step for today’s businesses to get and stay ahead. The cloud allows employees to access data from almost anywhere at any time, which enhances resource accessibility, improves team collaboration, and simplifies administration. This speed and flexibility give developers and data scientists the right tools for staying at the forefront of innovation. 

Amidst these innovations, many businesses struggle to align their data security practices with the speed of digital transformation. Cloud data security is a challenge every business should tackle during their digital transformation journey. Failure to secure sensitive data in the cloud can lead to a data breach or compliance violation, causing devastating results for the business.

But, securing data in today’s fast-paced cloud ecosystems is a tall order. Teams must secure a complex, multi-cloud environment containing several types of data and services such as IaaS, PaaS, and SaaS. In addition, cloud data gets created, copied, shared, and moved daily by any given staff member at the organization. Businesses need a new kind of data security to keep pace: an agile, cloud-native approach that safeguards their sensitive data while supporting rapid innovation and constant motion. 

In this article, we learn more about data security for the cloud, the unique challenges of securing all data within a cloud environment, and best practices for protecting your data in the cloud both reactively and proactively. 

An overview of cloud data security

Cloud data security safeguards your sensitive assets wherever they reside in the cloud. The discipline focuses on protecting cloud data while empowering organizations to leverage that data and meet business goals. Cloud data security includes preventive and detective controls for posture management, access governance, and threat monitoring and response. More specifically, the discipline of cloud data security can… 

• Protect all sensitive data from malicious activity, regardless of location or type, by autonomously discovering and classifying all sensitive data. 
• Reduce the attack surface by identifying and securing any shadow data that is unmanaged by security.
• Prevent data exposures from human error or neglect through data policy enforcement and risk prioritization. 
• Reduce the repercussions of data leaks by limiting user and machine access to sensitive assets and following the principle of least privilege.
• Minimize the damage of a breach by detecting and containing active threats in real time.
• Align data security practices with regulations and standards, preventing privacy and regulatory violations. 

Cloud data security pairs well with other security disciplines, such as cloud infrastructure vulnerability management, application security, and identity management. Together, these practices form the backbone of an organization’s overall cloud security strategy.

What are the challenges of securing data in the cloud?

While cloud computing brings endless possibilities for businesses, securing sensitive assets stored across a cloud environment is daunting. The complexity and speed of a cloud ecosystem make cloud data security incredibly challenging. As teams work to defend their cloud data from threats, they usually run into these pain points: 

1. Data proliferation

Data multiplies quickly in the cloud. Multiple departments have access to public cloud platforms and can make changes without the knowledge or consent of the security team. For instance, developers can move and copy data into new applications and new environments at the push of a button. The pace of change is daily, if not hourly. Soon enough, security is out of touch with the number of sensitive data assets kept within an organization’s cloud stores. And it is impossible to protect what you don’t know about. 

2. Data policies do not travel 

The sprawl of technology in the cloud is unprecedented. Each major cloud provider has dozens of different ways to store and process data, each with its own configurations and controls. Plus, security policies do not automatically travel with the data as it proliferates; they must be reset and re-established with each new copy. In this reality, the only way to apply policies to the data is to provide the policies to developers and data scientists and trust that they will work within those guardrails. But trusting security to others without automated verification is dangerous, especially when security is not their focus. 

3. Opaque data access 

Because the cloud is so complex, it’s difficult to pinpoint who has access to your sensitive data assets. There are disparate access control technologies on each cloud service, making it very challenging to understand which staff members and third-party services have access to a specific data element. Without this knowledge, organizations can’t control who has access to their most sensitive cloud data, drastically increasing the likelihood of data leakage. In addition, when an organization experiences a weaponized third party or insider threat, the security team can’t mitigate the impact because they don’t know who has access to that data. 

4. No centralized privacy and compliance enforcement

When working within a multi-cloud environment, data security teams find it especially difficult to prove compliance. Even if data security practices align well with the regulations and standards, it’s challenging to prove this adherence. Security teams must search for evidence to demonstrate compliance scattered across multiple cloud environments and services. So, it takes lots of time and effort to compile a complete, audit-ready report. The data security team desperately needs this time for other endeavors.

5. Tracking real-time activity is cost prohibitive & noisy

To monitor their cloud environment for real-time attacks or data leaks, a security team must log activity on their sensitive data. But, most organizations don’t know the locations of their most valuable assets. As a result, they only have two options: log everything, which causes rapidly-mounting costs, or do nothing, which significantly increases the likelihood of an undetected data leak. If the team takes the first option and chooses to monitor all of their cloud data, they must wade through irrelevant noise to pinpoint real threats. This alert fatigue means that data leaks can go unnoticed, even with logging practices in place. Of course, doing nothing has its own often costly consequences.

Cloud data security answers these challenges by finding and classifying sensitive data, then monitoring its activity, movement, and access across a complex environment. 

What are the benefits of cloud data security? 

Although cloud data security might seem daunting at first, it pays for itself in dividends. A few benefits of establishing a cloud data strategy include:

1. Enabling innovators to leverage data safely. By establishing data security built for the cloud (versus legacy approaches built for on-premise stores), your organization can secure data while working with — not against — existing cloud practices. Cloud data security uses internal policies to enforce data hygiene and security best practices automatically.
2. Reducing overexposure or risk of sensitive data breaches. Unlike approaches tailored to securing cloud infrastructure, cloud data security follows and defends your sensitive data wherever it goes or resides—and regardless of type—whether structured, unstructured, managed, or self-hosted.
3. Meeting compliance requirements and avoiding regulatory fines. Cloud data security centralizes your data security efforts, making it much easier to prove adherence to the requirements set by internal and external privacy/governance stakeholders.
4. Minimizing risk to sensitive data as soon as it happens. Cloud data security log monitoring enables your organization to respond immediately to suspicious activity by quickly investigating, containing, and minimizing data breaches in real time.
5. Ensuring least privilege in a dynamic environment. Access controls can be challenging to upkeep in an ever-changing cloud ecosystem that leverages a host of third-party services, such as IaaS, PaaS, and SaaS. Cloud data security manages sensitive data access across a varied and dynamic environment, only granting access to those who need it.

To accomplish these goals, cloud data security must fulfill two responsibilities. First, it should proactively secure data as innovators move, copy, and use it daily. In addition, it should reactively monitor data activity and access for irregular events so teams can discover and contain breaches as soon as they begin. 

Best practices for a comprehensive cloud data security strategy

To fully understand your cloud data security posture, your team needs a way to react to ongoing events, prevent compliance policy violations, and get privacy, data security, and SOC teams on the same page. To accomplish these goals, your organizations should focus on a multi-faceted approach with the following best practices:

Discovering and classifying sensitive data

To secure your sensitive data, you must know where it resides, who owns it, and how it relates to the rest of your system. Implementing a data landscape intelligence solution enables you to autonomously discover all data—whether or not it’s known and managed by the security team. The solution classifies and contextualizes the cloud data in a centralized asset inventory or catalog. This process must be compatible with any data type (e.g., structured databases, unstructured files, object storage, data embedded in apps, etc.) 

Enforcing policies across the organization

In addition, your team must enforce data policies proactively, preventing further security issues from arising in the future. A discipline called data security posture management (DSPM) focuses on maintaining a robust data security posture by detecting and alerting on policy violations. For example, DSPM could enforce the policy that all PII must be encrypted, regardless of where it gets stored, copied, or moved. With DSPM, your organization can prevent data overexposure, under-protection, or misplacement.

Monitoring and limiting access

Organizations should consider using a technology like data access governance (DAG) to control user, machine or application access to sensitive data and enforce least privilege access for their most valuable or risky identities. This technology uses visualization to understand which entities have access to sensitive data. Security analysts can then use these visuals to quickly mitigate data overexposure.  

Detecting data leaks

It’s essential to monitor your data stores for real-time threats as well. Data detection and response (DDR) detects data breaches as they occur, enabling teams to contain data exfiltration attempts and prevent further damage. This technology collects details on typical data usage across an organization, then flags when an event falls outside this definition of “normal activity.” Teams should use DDR insights alongside DAG visualizations to contain suspicious data events as soon as possible.  

Working closely with data governance and privacy teams

Your cloud data security strategy must also align with the day-to-day priorities of the governance and privacy teams. For instance, individual cloud data functions should be tightly integrated and compatible with each other, making it easy to gain a complete view of your entire cloud data security program. In addition, there should be a way to generate audit-ready compliance reports. 

Cloud data security in action

WalkMe, a digital adoption platform, is a real-life example of how a cloud data security approach works. Their team needed to secure sensitive customer data stored in the cloud, including AWS data stores (RDS, EBS, etc.) and a GCP environment (including BigQuery). But, they needed to do so without interrupting business growth. 

To respond to this challenge, WalkMe implemented the following cloud data security controls:

• Autonomous discovery and classification for known and unknown data. This process included finding misplaced sensitive data, such as personal information in a lower environment or publicly exposed sensitive information.
• Remediation guidance for fixing these data misplacements, such as a recommendation to remove sensitive data from a lower environment.
• Continuous monitoring for the entire cloud environment, flagging whenever changed, moved, or copied data violates their policies.
• Intelligent scanning for new and emerging risks to sensitive assets.

As a result, WalkMe has visibility into any policy violations and real-time threats that affect their sensitive data and can take quick steps to mitigate these risks. Plus, they can enforce these data security best practices without compromising innovation.

Laminar’s multi-faceted approach to cloud data security 

Gaining comprehensive identification, protection, detection, and response capabilities for a multi-cloud environment might seem overwhelming. But it doesn’t have to be.

Laminar’s solution encompasses all of these factors, providing an agile data security platform for multi-cloud. We’ve consolidated all essential data functions— data landscape intelligence, data security posture management (DSPM), data access governance (DAG), data detection and response (DDR), and privacy and compliance – into a single, integrated platform. 

The Laminar cloud data security platform also leverages the following:

• Secure scanning. Your data always remains in your environment during discovery and classification. We only use your metadata for further analysis and reporting. 
• Rational, high-fidelity AI. Our dynamic ML models, trained on usage telemetry and Laminar Labs research, filter out false positives and minimize alert fatigue.
• Agentless architecture. Laminar uses CSP APIs and an ephemeral disk clone to deploy efficiently and asynchronously scan your environment without affecting performance.
• Autonomous discovery. We provide automated discovery and classification of all cloud data assets, including managed, unmanaged, and shadow data. Our platform discovers all cloud-resident data without credentials or any leads on where your data resides. 
• Contextual understanding. Our agile data security approach empowers you to collect invaluable information on every sensitive asset, such as data owner, content type, location, recent activity, and top users, then uses this knowledge to flag any irregular activity.
• Risk assessment. The Laminar platform detects policy violations, then prioritizes them by sensitivity, volume, and other risk-based factors.
• Guided remediation. Once our solution flags a violation, your team can explore easily-accessible info on why it occurred, which speeds up investigation. Team members can also reference step-by-step instructions on how to fix the violation.

With Laminar’s agile data security platform, you can quickly identify real-time or potential threats determine the security posture of all your sensitive data. Then, you can protect that data with policy enforcement and real-time threat detection and response. 

Discover the Laminar Security platform for yourself today.