In this episode, I sit with long-time vulnerability management and data science experts Jay Jacobs and Michael Roytman, who recently co-founded Empirical Security.

We dive into the state of vulnerability management, including:

• How it is difficult to quantify and evaluate the effectiveness of vulnerability prioritization and scoring schemes, such as CVSS, EPSS, KEV, and proprietary vendor prioritization frameworks, and what can be done better
• Systemic challenges include setbacks in the NIST National Vulnerability Database (NVD) program, the MITRE CVE funding fiasco, and the need for a more resilient vulnerability database and reporting ecosystem.
• Domain-specific considerations when it comes to vulnerability identifiers and vulnerability management, in areas such as AppSec, Cloud, and Configuration Management, and using data to make more effective decisions
• The overuse of the term “single pane of glass” and some alternatives
• Empirical’s innovative approach to “localized” models when it comes to vulnerability management, which takes unique organizational and environmental considerations into play, such as mitigating controls, threats, tooling, and more, and how they are experimenting with this new approach for the industry

In this episode, we sit down with the Co-Founder and CPO of Seemplicity, Ravid Circus, to discuss tackling the prioritization crisis in cybersecurity and how AI is changing vulnerability management.

We dove into a lot of great topics, including:

• The massive challenge of not just finding and managing vulnerabilities but also remediation, with Seemplicity’s Year in Review report finding organizations face 48.6 million vulnerabilities annually and only 1.7% of them are critical. That still means hundreds of thousands to millions of vulnerabilities need to be remedied - and organizations struggle with this, even with the context of what to prioritize.
• There’s a lot of excitement around AI in Cyber, including in GRC, SecOps, and, of course, AppSec and vulnerability management. How do you discern between what is hype and what can provide real outcomes?
• What practical steps can teams take to bridge the gap between AI’s ability to find problems and security teams’ ability to fix them?
• One of the major issues is determining who is responsible for fixing findings in the space of Remediation Operations, where Seemplicity specializes. Ravid talks about how, both technically and culturally, Seemplicity addresses this challenge of finding the fixer.
• What lies ahead for Seemplicity this year with RSA and beyond

In this episode, we sit down with Varun Badhwar, Founder and CEO of Endor Labs, to discuss the state of AI for AppSec and move beyond the buzzwords.

We discussed the rapid adoption of AI-driven development, its implications for AppSec, and how AppSec can leverage AI to address longstanding challenges and mitigate organizational risks at scale.

Varun and I dove into a lot of great topics, such as:

• The rise of GenAI and LLMs and their broad implications on Cybersecurity
• The dominant use case of AI-driven development with Copilots and LLM written code, leading to a Developer productivity boost. AppSec has struggled to keep up historically, with vulnerability backlogs getting out of control. What will the future look like now?
• Studies show that AI-driven development and Copilots don’t inherently produce secure code, and frontier models are primarily trained on open source software, which has vulnerabilities and other risks. What are the implications of this for AppSec?
• How can AppSec and Cyber leverage AI and agentic workflows to address systemic security challenges? Developers and attackers are both early adopters of this technology.
• Navigating vulnerability prioritization, dealing with insecure design decisions and addressing factors such as transitive dependencies.
• The importance of integrating with developer workflows, reducing cognitive disruption and avoiding imposing a “Developer Tax” with legacy processes and tooling from security.