In today's data-driven world, organizations face an ever-evolving threat landscape. Sensitive data is the crown jewel that attackers seek to exploit, and breaches can have devastating consequences for revenue, market capitalization, and customer confidence. This is especially true for businesses that rely on modern application environments, where engineers continuously modify or create new data stores, microservices, virtual machines, and APIs as part of agile development practices.
Against this backdrop, security teams face a critical yet seemingly impossible quest: gaining a reliable, up-to-date understanding of their infrastructure down to every asset and the data they process. This is where Data Security Posture Management (DSPM) comes in. DSPM provides visibility into where sensitive data is, who has access to that data, how it has been used, and the security posture of stored or application data. It assesses the current state of data security, identifies potential risks and vulnerabilities, implements security controls to mitigate these risks, and regularly monitors and updates the security posture to ensure it remains effective. By doing so, DSPM enables businesses to maintain the confidentiality, integrity, and availability of sensitive data.
You can't secure what you can't find.
As we noted in a previous article, data observability is a crucial step in implementing any sensitive data security program. You can't secure what you can't find. Data observability provides a complete, reliable, and effortless view of all sensitive data in real-time, connecting production and staging environments. It gives security teams insights into what is happening with the data and why, supporting cross-team collaboration with engineering.
In today's fast-paced development environments, data is often backed up, copied, or replicated to new data stores as part of iterative testing and development. This can create "shadow data" - data that is not monitored, managed, or governed by the same security teams, policies, or controls as the original data. Similarly, the demand for data for AI/ML modeling may lead to data being accessed by more users who possess less understanding of proper data security and governance. The adoption of multicloud and hybrid cloud environments further spreads the risk, as data is stored across multiple types of computing environments.
Lack of observability leads to data protection gaps in modern application environments
Lack of observability leads to data protection gaps in modern application environments. There are a range of methods being used to address the data observability problem. On the process side, they have two main options:
The only way to achieve efficiency without compromising security is to gain automated, real-time observability into all assets and prioritize those that deal with sensitive data. However, on the technology side, security teams are forced to use tools designed for different purposes, which fall short of their expectations:
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A robust DSPM solution like Soveren is designed to address these challenges. By automatically discovering sensitive data and assets across the entire application environment, Soveren helps identify and secure shadow data. It provides complete, near real-time coverage by analyzing all services, data stores, API endpoints, third-party integrations, and the connections between them.
Soveren also supports key DSPM best practices:
Soveren helps identify and protect crown jewels in Kubernetes-based environments. It automatically discovers sensitive data and assets, maps the flows between them, and immediately alerts you before risks become full-blown incidents. Cover both data in motion and at rest simultaneously. With Soveren, security teams no longer face a compromise between security and efficiency. You can achieve 100% data observability to avoid blind spots and reduce resources spent on manual security guardrails.
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Unlike other DSPM solutions that provide partial, occasional snapshots of the data landscape by focusing only on static data inside data stores, Soveren provides complete, near real-time coverage by analyzing all services, data stores, API endpoints, third-party integrations and the connections between them. Soveren builds and maintains an automated catalog of data, services, data stores, and API endpoints across your entire application environment and third-party integrations. It discovers and classifies sensitive data with high precision to identify sensitive data flows and top-priority assets.
It then combines asset-related context (connection between services, protocols, associated datastores, etc.) and data-related context (data types, sensitivity, etc.) to detect baseline changes and security risks — such as new assets accessing sensitive data, changes in external PII sharing, public availability of PII, or PCI data outside of the PCI DSS perimeter. You can leverage out-of-the-box policies or set up custom ones to initiate workflows and real-time alerts in tools like Jira and Slack. Such alerts include security, privacy, and compliance changes, misconfigurations, and risks.
Recommended further reading: How Agoda of Booking Holdings uses real-time data observability to protect their application environment
Book a demo to start your DSPM implementation with Soveren.
