Sensitive data observability — you can't secure what you can’t find

Lack of observability → Data protection gaps 

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.

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. 

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 there are  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 Run manual reviews in an effort to identify changes after the fact — wasting time and getting incomplete, out-of-date results

Manually reviewing every major change in a dynamic application environment is impractical. 

For every 1,000 services, it takes over 1,500 employee hours and costs $120k per year.

As such, 80% of security teams admit to doing reviews selectively or irregularly, subsequently uncovering critical unknown unknowns.

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.

Current solutions are not enough

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:

• 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.
• 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.
• 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 these critical 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.

Soveren integrates natively with Kubernetes, analyzing network traffic with zero impact on application latency or performance. It uses proprietary ML algorithms to discover and classify sensitive data within data flows in near real-time with 95% detection rate out of the box.

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.

Data observability is key to data security

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 data stores, 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.

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. 

This results in critical benefits for security teams compared to other DSPMs:

• Gain observability into your entire Kubernetes-based application environment with a complete and automated single source of truth on data and assets
• Proactively detect risks that other tools miss, such as excessive sharing of PCI data externally via an API, eventual exposure of sensitive data from a data store through a chain of services, and data transmissions via unsecure channels 

Soveren is designed to increase security and engineering efficiency by:

• Reducing resources spent on manual reviews with automated infrastructure intelligence
• Improving security posture by adding data sensitivity context to CSPM and SIEM
• Fitting easily into the operational workflows via integrations with tools like Jira and Slack
• Requiring only 15 minutes to deploy with zero effort spent on ongoing maintenance

Find out how you can light up a torch in the K8s darkness with Soveren