Welcome to episode 340 of The Cloud Pod, where the forecast is always cloudy! It’s a full house (eventually) with Justin, Jonathan, Ryan, and Matt all on board for today’s episode. We’ve got a lot of announcements, from Gemini for Gov (no more CamoGPT!) to Route 52 and Claude. Let’s get started!

Titles we almost went with this week

Claude’s Pricing Tiers: Free, Pro, and Maximum Overdrive
GitHub Copilot Learns Database Schema: Finally an AI That Understands Your Joins
SSMS Gets a Copilot: Your T-SQL Now Writes Itself While You Grab Coffee
Too Many Cooks in the Cloud Kitchen: How 32 GPUs Outcooked the Big Tech Industrial Kitchens
Uncle Sam Gets a Gemini Twin: Google’s AI Goes Federal
Route 53 Gets Domain of Its Own: .ai Joins the Party
Thai One On: Google Cloud Plants Its Flag in Bangkok
NAT So Fast: Azure’s Gateway Gets a V2 Glow-Up
Beware Azure’s SQL Assistant doesn’t smoke your joints.

AI Is Going Great, Or How ML Makes Money

30:10 Announcing BlackIce: A Containerized Red Teaming Toolkit for AI Security Testing | Databricks Blog

Databricks released BlackIce, an open-source containerized toolkit that bundles 14 AI security testing tools into a single Docker image available on Docker Hub as databricksruntime/blackice:17.3-LTS.
The toolkit addresses common red teaming challenges, including conflicting dependencies, complex setup requirements, and the fragmented landscape of AI security tools, by providing a unified command-line interface similar to how Kali Linux works for traditional penetration testing.
The toolkit includes tools covering three main categories: Responsible AI, Security testing, and classical adversarial ML, with capabilities mapped to MITRE ATLAS and the Databricks AI Security Framework.
Tools are organized as either static (simple CLI-based with minimal programming needed) or dynamic (Python-based with customization options), with static tools isolated in separate virtual environments and dynamic tools in a global environment with managed dependencies.
BlackIce integrates directly with Databricks Model Serving endpoints through custom patches applied to several tools, allowing security teams to test for vulnerabilities like prompt injections, data leakage, hallucination detection, jailbreak attacks, and supply chain security issues.
Users can deploy it via Databricks Container Services by specifying the Docker image URL when creating compute clusters.
The release includes a demo notebook showing how to orchestrate multiple security tools in a single environment, with all build artifacts, tool documentation, and examples available in the GitHub repository.
The CAMLIS Red Paper provides additional technical details on tool selection criteria and the Docker image architecture.

04:30 Ryan – “It’s very difficult to feel confident in your AI security practice or patterns. I feel like it’s just bleeding edge, and I

340: Azure releases a new SQL AI Assistant… Jimmy Droptables