AI Agents for Vulnerability Management – Mehul Revankar – RSAC26 #2

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Full Segment Notes
Key Moments
  • 0:00 - – RSAC 2026 Interview with Quantro Security
  • 0:30 - – Founders’ Background & Tenable Days
  • 0:45 - – Why Start Quantro Security?
  • 01:10 - – AI is Changing Exploit Development
  • 02:00 - – Rise of AI-Powered Cyber Attacks
  • 02:30 - – Why Defenders Need AI-Native Security
  • 03:00 - – Cybersecurity Productivity Gap Explained
  • 03:40 - – Talent Shortage in Cybersecurity Teams
  • 04:10 - – Building an AI Cyber Defense Advisor
  • 04:50 - – Faster Exploits Than Patches (Big Problem)
  • 05:15 - – Who Should Use Quantro Security?
  • 05:40 - – Enterprise Security Challenges & Tool Sprawl
  • 06:10 - – Improving Risk Detection & Response Speed
  • 06:30 - – MSSPs: Scaling Security with AI
  • 07:10 - – Increasing Efficiency & Profitability with AI
  • 07:50 - – Custom Security Outcomes (One Size → One-to-One)
  • 08:45 - – How AI Reduces Security Team Workload
  • 09:40 - – What Makes Quantro Different?
  • 10:00 - – Building a Proprietary Vulnerability Database
  • 11:00 - – Multi-LLM Orchestration Explained
  • 11:45 - – Context Engineering & Reducing AI Costs
  • 13:00 - – Why Chatbots Alone Don’t Work in Security
  • 13:30 - – Future-Proofing with Flexible AI Models
  • 14:10 - – Competitors: Hyperscalers vs Niche Tools
  • 15:00 - – Why Traditional Tools Fall Short
  • 16:00 - – AI as the Security “Connective Tissue”
  • 16:50 - – Final Thoughts & Closing
Guest
Co-Founder & Chief Product Officer at Quantro Security, Inc.

Mehul is a seasoned Cybersecurity Product Leader with a proven track record of building award-winning products and scaling B2B SaaS and open-source solutions. With over 20 years of experience in enterprise security, he has led product, engineering, and research teams at industry leaders like Qualys, and Tenable. He is now a Co-founder at Quantro Security where he is building agentic AI solutions for Enterprise Cyber Risk Management. Where he leads Product, Sales and Marketing.

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