Demystifying AI Red Teaming
In this report, we cover:
- Why traditional security testing leaves critical gaps.
- The four risk categories executives need to own.
- What a mature, lifecycle-wide red teaming program looks like.
This resource gives you the clarity to ask the right questions, pressure-test your current approach, and take meaningful action before your customers or regulators do it for you. Download it now.
Overview
Your AI passed every security check, but that doesn't mean it's safe. Today's adversaries don't need privileged access or exploitable code. A carefully crafted prompt is enough to expose sensitive data, generate harmful content, or push your system out of compliance. As AI agents take on greater autonomy across your organization, the window between a vulnerability and a real-world incident is shrinking fast.
Download this whitepaper to understand exactly what AI red teaming is, where your exposure lies, and how to build a program that keeps pace with your AI.
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What’s New from Alice
Your LLM Has No Idea What It's Doing
Diana Kelley, CISO at Noma Security and former Cybersecurity CTO at Microsoft, joins Mo to work through the real mechanics of LLM risk: why the context window flattens the trust boundary between system instructions and user data, why that makes reliable internal guardrails essentially impossible, and why agentic AI is less a new threat category and more a stress test for the hygiene debt organizations never fully paid off.
Distilling LLMs into Efficient Transformers for Real-World AI
This technical webinar explores how we distilled the world knowledge of a large language model into a compact, high-performing transformer—balancing safety, latency, and scale. Learn how we combine LLM-based annotations and weight distillation to power real-world AI safety.
