Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Using SSL Inspection and AI Guardrails to Protect Infrastructure How do you protect your AI infrastructure from threats without impacting user experience? In this video, we'll cover the methods organizations can use to inspect encrypted traffic, including what is sent to AI chatbots, and add guardrails to protect against security risks. We'll cover.

In this LightTalk series, A10's Gary Wang talks about all things agentic AIs, MPCs, and APIs. He explains how they are tied together, step by step, using an analogy of planning a trip to Mount Everest. He highlights the workflow's weaknesses and how API protection and AI firewalls can be key to securing this process.

How do AI guardrails protect infrastructure from the unsafe and unpredictable territory of LLM risks? An AI firewall or guardrail device sits between your applications and large language models to keep the data sent and received from LLMs safe, compliant, and high-quality. Its design is to inspect natural-language traffic and protect your infrastructure against LMM vulnerabilities, including prompt injection, jailbreak attacks, data poisoning, system prompt leakage, and OWASP Top 10 vulnerabilities, using advanced, proprietary reasoning models.

My boss dropped a bombshell at 4:00 PM: build a secure, production-ready app from scratch by tomorrow morning. Instead of panicking, I put Claude Opus 4.6 to the test. In this video, I walk you through the entire end-to-end process of using an AI agent to architect, code, and debug a full-stack application. We’ll look at "Plan Mode," how the AI handles environment errors (like Windows SQLite issues), and most importantly, how we verified the AI's code for security vulnerabilities using Snyk.

As AI adoption grows, identity risk grows with it. Dirk Schrader, VP of Security Research at Netwrix, explains why governing human and machine identities is foundational to securing AI systems. How are you governing identity in your AI workflows today?