Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI is changing everything—but with innovation comes new risks. In this episode of AI on the Edge, we dive deep into OWASP's Top 10 for Large Language Models with security leader Steve Wilson (Exabeam). Discover why every tech company is suddenly talking about LLM security and how you can stay ahead. Inside this episode: Why traditional security doesn’t work for AI Learn from Steve’s new book The Developer’s Playbook for LLM Security and get actionable tips to protect your AI systems.

From Model Drift to API Exploitation: The Next Challenge in AI Security In this clip from "Securing AI Part 4: The Rising Threat of Hidden Attacks in Multimodal AI," Diptanshu Purwar and Madhav Aggarwal summarize why external guardrails are the only sustainable defense against the new wave of AI exploitation. Jamison Utter then sets the stage for the next topic in the series: securing the fundamental protocols and APIs that AI agents rely on.

From identification to remediation to drift management. When Reach flags an exposure, it doesn’t stop there. It shows exactly how much risk you’ll reduce by fixing it — and what impact it’ll have on users. In this short demo, CRO Jared Phipps walks through how Reach:︎ Quantifies residual risk reduction (e.g., 62%, 91%, etc.)︎ Weighs that against user impact︎ Guides teams through the remediation process︎ Integrates with Jira or other ticketing systems to track fixes︎ Monitors configurations to prevent drift and maintain baselines.

AI On The Edge – Where Intelligence Meets Risk: Part 2 Join Amar Kanagaraj, Founder & CEO of Protecto, and Sabry Loganathan, Head of AI Strategy at Peloton, as they discuss how a chance project evolved into building world-class generative AI systems.

Trust in AI starts with transparency. In our recent conversation, “No Time to Drift,” Sebastian Goodwin, Chief Trust Officer at Autodesk, shares how his team is putting that principle into practice — by creating AI Transparency Cards. Think of them like nutrition labels for AI: clear, consistent, and designed to help customers understand what’s inside. Each one outlines what the model does, how it’s trained, safeguards in place, and more.