With the forecast that more than 175 zettabytes will have been transferred to the cloud by 2025, companies face an unprecedented challenge in data management and security. The increase in unmanaged data, or shadow data, outside the control of security teams is ushering in a new era of challenges in cloud security.
Main Challenges in Cloud Data Security
Organizations encounter several hurdles when attempting to secure sensitive data in the cloud:
Complexity of the Cloud: Data dispersed across multiple cloud platforms and services hinders visibility and understanding of where data is located and what information is stored.
Excessive Permissions: In addition to discovering and classifying data, security teams lack the ability to understand data accesses and comply with data sovereignty requirements, leading to significant security gaps.
Lack of Data Context: Utilizing multiple cloud service providers and security tools creates a lack of contextual intelligence, resulting in alert overload and potential security breaches.
The Solution: Data Security Posture Management (DSPM)
DSPM has emerged as an essential solution for continuously monitoring data in the cloud and protecting it against vulnerabilities and potential risks. According to Gartner, DSPM “provides visibility into where sensitive data resides, who has access to that data, how it is being used, and what the security posture is of the stored data or application.” DSPM offers actionable insights to enhance data security posture, enabling security teams to protect sensitive data with intelligence and context.
Five Key Requirements for Your Next DSPM Solution
To address these challenges, the five essential requirements to look for in your next DSPM solution are detailed below:
Discovery, Classification, Exposure, and Data Posture Management: You cannot protect what you do not see or know. The first step in securing data in the cloud is gaining complete visibility and inventory of your environment. Your DSPM solution should scan cloud data repositories and discover native structured and unstructured data, providing a clear view of the data landscape, inventory, and security posture. The solution should also accurately classify data using content analysis techniques, artificial intelligence, machine learning, metadata, or labeling. It should provide deep context and understanding of sensitive data, identify data exposures, misconfigurations, and excessively permissive accesses that could result in data leakage. It should also notify security teams of the discovery of new data stores or at-risk objects.
Unified DLP Engine Across the Enterprise: Organizations utilize various cloud and data services. Security teams need a comprehensive understanding of the location, movement, and exposure of sensitive data to prevent leaks. A single DLP engine for your entire data protection solution helps create and enforce consistent security policies across the enterprise, ensuring that sensitive data is uniformly protected regardless of where or how it is accessed.
Advanced AI/ML for Threat Correlation: Managing data security risk in a complex environment can be challenging, particularly with ecosystems of multiple security and cloud products generating disconnected alerts. Your DSPM solution should leverage AI, machine learning, and advanced threat correlation capabilities to transform security data into actionable information that uncovers hidden risks or attack vectors. This should be supported by real-time alerts and remediation guidance to enable your security team to focus on what matters most.
Multicloud Support: Most organizations are adopting multicloud strategies, utilizing more than one cloud service provider. Your DSPM solution should seamlessly cover a variety of cloud environments and read from different databases, data stores, object storage, disk storage, managed data stores, and self-managed data stores. It should provide a single, consistent view of data across clouds, geographies, and organizational boundaries, helping security teams assess the risk of sensitive data in multicloud environments.
Compliance Management: Data protection regulations like GDPR, HIPAA, PCI DSS, etc., require safeguarding sensitive data. Your DSPM solution should simplify compliance processes, automatically mapping data posture with internal and external regulatory benchmarks, and generating alerts about misconfigurations or issues that may cause compliance violations. It should provide remediation guidance to mitigate risks and ensure compliance with data protection regulations.
Conclusion
DSPM solutions have become an essential tool for organizations seeking to protect their sensitive data in the cloud. With the mentioned requirements, companies can navigate the complexity of cloud environments, secure their data, and comply with regulations, all while optimizing operations and reducing risks. Integrating advanced technologies like AI and machine learning will be key to ongoing success in data protection in an increasingly digitalized world.
More information at Zscaler.