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
Curiouser Soundbites: What D&D Taught Us About AI Governance
If you work in GRC and you've ever felt like the ground keeps moving faster than you can document it, this one is for you. David Wendt, Manager of Innovation and AI Governance at Sherwin-Williams, draws one of the most unexpectedly useful analogies we've heard on Curiouser & Curiouser yet, and it involves Dungeons and Dragons.
AI Governance Needs a Dungeon Master
David Wendt has spent 30 years building models and just as long running D&D campaigns. Turns out both taught him the same things about operating in uncertainty. He joins Mo to talk AI governance at enterprise scale, what real red teaming looks like, and why the smarter move is to stop measuring your AI and start measuring what you actually care about.
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.
