Navigating 7 Executive Challenges in GenAI Deployment
Ninety percent of enterprises are already running generative AI, and most are doing so without the controls needed to keep it safe. Every unguarded model interaction is a potential liability for risks like hallucinations that misleads a customer, a prompt injections that exposes proprietary data, or misaligned responses that quietly erode the trust you have spent years building. The EU AI Act and emerging regulations now demand risk-based controls for high-impact AI systems, and regulators are watching.
Download this report to know exactly where your GenAI deployment stands and what to do about it.
Overview
In this report, we cover:
- How to identify and close the seven critical vulnerabilities that put your brand equity, user trust, and regulatory standing at risk in live GenAI deployments
- How to build an observability and guardrails framework that lets your security and product teams enforce safety policies in real time, without slowing down your engineers
- How to quantify the ROI of AI safety so you can demonstrate its business value in terms your board and P&L will recognize
Use this report to make confident, informed decisions about your GenAI strategy. Download it now and give your team the foundation to deploy AI that earns trust rather than risks it.
Download the Full Report
What’s New from Alice
"Okay, Here is How to Build a Bomb": Millions Download Dangerous LLMs
Thousands of abliterated LLMs have flooded open-source platforms with millions of downloads. These models comply with virtually any request, from bomb-making to malware, and run fully offline on consumer devices.
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.
