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AI Explained: Fighting AI Threats With Autonomous Defense

July 16, 2026
10:00AM PT / 1:00PM ET
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This webinar has been cancelled at this time due to unforeseen circumstances.
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As AI systems become more agentic, the security challenge increasingly outpaces traditional review, monitoring, and incident response. Enterprises are shifting from systems that generate content to systems that use tools, make decisions, access data, and take action across environments. Attackers use AI to accelerate the speed and scale of attacks. As a result, defensive systems must become more autonomous to keep pace. But that autonomy has to be governed, through identity, permissions, telemetry, policy, human approval points, and clear control boundaries.

Join David Campbell, a founding member of OWASP, as he makes the case for autonomous defense at the intersection of AI security and governance, where the goal is no longer just securing models, but securing autonomous systems that operate in the real world.

What you'll learn:

  • Why securing systems that take action on their own is a different problem than securing ones that only generate content
  • What autonomous defense looks like in practice, from scoped, least-privilege access to continuous testing of how systems behave
  • The Behavior, Identity, and Control (BIC) framework, and how it governs what a system does, who it acts as, and the limits around it

AI Explained is our AMA series featuring experts on the most pressing issues facing Agentic and AI teams.

Featured Speakers
David Campbell
Founding Member
at
OWASP
David Campbell is a founding member of OWASP's AI Vulnerability Scoring System project. He pioneered large-scale AI red teaming, including a generative AI red team recognized by the Biden-Harris administration, and has briefed the U.S. Congress, the White House, U.K. Parliament, and NATO on AI risk and resilience.
Krishna Gade
Founder and CEO
at
Fiddler AI
Krishna Gade is the Founder and CEO of Fiddler AI, where he is building the control plane for AI systems — helping enterprises monitor, evaluate, and control models and autonomous agents in production. Previously, he time at Microsoft working on search ranking for Bing, and later led data and machine learning teams at Twitter and Pinterest. He is most known for his work on AI Explainability at Facebook/Meta and these days he is now focused on making AI systems trustworthy, scalable, and production-ready for the enterprise.