Data security posture management (DSPM) provides visibility as to where sensitive data is, who has access to that data, how it has been used, and what the security posture of the data stored or application is. It does that by assessing the current state of data security, identifying potential risks and vulnerabilities, implementing security controls to mitigate these risks, and regularly monitoring and updating the security posture to ensure it remains effective. As a result, it enables businesses in maintaining the confidentiality, integrity, and availability of sensitive data.
In one of our previous articles we stated that data observability is a crucial step in any sensitive data security program implementation.
You can't secure what you can't find.
Data observability is a complete, reliable, and effortless view of all sensitive data in one place connected to production and staging environments in real-time. It provides security teams with insights on what and why something is happening with the data, and supports cross-team collaboration with engineering
Modern application environments are constantly changing as engineers continuously modify or create new data stores, microservices, virtual machines, and APIs. Businesses that don’t know what to protect face an order of magnitude higher probability of data breaches and privacy violations. This impacts revenue, market capitalization, and customer confidence.
Lack of observability leads to data protection gaps in modern application environments
Against this backdrop, security teams face the critical — yet seemingly impossible — quest for a reliable, up-to-date understanding of their infrastructure down to every asset and the data they process.
There are a range of methods being used to address the data observability problem:
With the lack of a perfect solution, most businesses have defaulted to some combination of manual processes, plus data discovery and security tools in an attempt to solve the data observability problem.
On the process side they have two main options:
1) Slow down product releases by proactively acting as gatekeepers — directly impacting revenue and growth
2) Run manual reviews in an effort to identify changes after the fact — wasting time and getting incomplete, out-of-date results
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 that have been designed for different purposes and fall short of these expectations
1) Data discovery tools provide a resource-intensive, one-off, limited, expensive visibility into data stores. Their primary focus is on compliance use cases, such as data inventory, data retention, and data residency, which are less relevant for security teams.
2) Data-at-rest DSPM tools are built to provide data discovery for data at rest and data residency insights (the rest of their functionality is already covered by CSPM tools). Security teams who think they can use DSPM for other use cases end up wasting time and budget.
3) CSPM tools provide valuable insights and risk management capabilities related to cloud infrastructure. However, they lack the necessary context on data processed and shared between assets, forcing security teams to search for the secritical details elsewhere.
Soveren helps identify and protect crown jewels in Kubernetes-based environments. It automatically discovers sensitive data and assets, mapping the flows between them and immediately alerting you before risks become full-blown incidents.
With Soveren, security teams no longer face a compromise between security and efficiency. You can achieve both 100% data observability to avoid blind spots and reduce resources spent on manual security guardrails.
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 realtime alerts in tools like Jira and Slack. Such alerts include security, privacy, and compliance changes, misconfigurations, and risks.
Soveren recently helped Agoda use real-time data observability to protect sensitive data in their application environment.
Book a demo to start your DSPM implementation with Soveren.