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While geopolitical drivers and adversaries may change, one constant remains: OT networks are attractive targets precisely because of their complexity, lack of visibility, and limited tolerance for downtime. Attackers don’t need zero-days—they thrive on missed patches, forgotten assets, and reactive processes. 

As we’ve spent years working with operators and engineers in the electric utility and OT space, we’ve seen some of the same challenges come up again and again. These problems are hardly groundbreaking or new, but they are very real to those working in the trenches to secure critical infrastructure. 

We recently sat down with the U.S. Department of Energy to discuss the success of V-INT, a CESER-funded project that is now integrated into Bastazo’s platform. Developed in collaboration with Network Perception and the University of Arkansas, V-INT gives energy utilities a powerful way to assess their cybersecurity posture using attacker-aware simulations and actionable vulnerability intelligence.

Cybersecurity teams navigating industrial environments face a dilemma like Odysseus between Scylla and Charybdis—either risk disruption by applying uncertain patches or risk exposure by leaving vulnerabilities unaddressed. But what if there was a way identify which threats matter most, enabling safer, smarter decisions?

In the world of cybersecurity, Machine Learning (ML) is becoming an increasingly essential tool. To understand the impact of machine learning, it’s first important to understand the basics. Which is exactly what we cover below.

The Common Security Advisory Framework (known as CSAF) is an important tool in this space, enabling security teams to quickly access machine-readable, structured information about vulnerabilities and their remediations. However, for CSAF to be truly effective, the data provided in a vendor advisory must be consistent and accurate. 

Learn how SSVC (Stakeholder-Specific Vulnerability Categorization) and CSAF (Common Security Advisory Framework) enhance vulnerability management beyond traditional CVSS ratings, which often lack the necessary context for effective vulnerability remediation.

By transitioning to a risk-informed remediation approach, leveraging Stakeholder-Specific Vulnerability Categorization (SSVC) and the Cybersecurity Advisory Framework (CSAF) to enhance decision-making and operational security, organizations can prioritize vulnerabilities based on actual risk impact rather than relying on reactive, compliance-driven patching.

In the field of vulnerability management, remediation, workaround, and mitigation are three common terms used to describe methods of addressing a vulnerability. They are sometimes used interchangeably, but their meanings can vary slightly across different vendors and organizations.