The Reprompt attack on Copilot, the resale market for stolen AI access, why LLM traffic needs a control room, retrieval as the new control plane, token-level cost telemetry, evaluation gates, and the AI you didn’t approve.
A user clicks a Copilot link, watches it load, and closes the tab. The session keeps running. The data keeps flowing. Reprompt demonstrated what happens when AI assistants inherit user permissions, persist sessions silently, and cannot distinguish instructions from attacks. The vulnerability was patched. The architectural pattern that enabled it, ambient authority without session boundaries, still exists elsewhere.
Read the full articleOperation Bizarre Bazaar is not a single exploit. It is a supply chain: discover exposed LLM endpoints, validate access within hours, resell through a marketplace. A misconfigured test environment becomes a product listing within days. For organizations running internet-reachable LLM or MCP services, the window between exposure and exploitation is now measured in hours.
Read the full articleMost teams don’t plan for an LLM gateway until something breaks: a surprise invoice, a provider outage with no fallback, a prompt change that triples token consumption overnight. This article explains what these gateways actually do on the inference hot path, where the operational tradeoffs hide, and what questions to ask before your next production incident answers them for you.
Read the full articleRAG is no longer a chatbot feature. It is production infrastructure, and the retrieval layer is where precision, access, and trust are won or lost. This piece breaks down what happens when you treat retrieval as a control plane: evaluation gates, access enforcement at query time, and the failure modes that stay invisible until an audit finds them.
Read the full articleUsage triples. So does the bill. But no one can explain why. This is the observability gap that LLM cost telemetry solves: the gateway pattern, token-level attribution, and the instrumentation that turns opaque spend into actionable data.
Read the full articleA prompt fix ships. Tests pass. Two weeks later, production breaks. The culprit was not the model. This piece unpacks the evaluation stacks now gating enterprise GenAI releases: what each layer catches, what falls through, and why most teams still lack visibility into what’s actually being deployed.
Read the full articleAsk a compliance team how AI is used across their organization. Then check the network logs. The gap between those two answers is where regulatory risk now lives, and EU AI Act enforcement is about to make that gap harder to explain away.
Read the full articleHow long would it take you to show a regulator, today, how you monitor AI behavior in production? If the honest answer is “give us a few weeks,” you’re already behind. This piece breaks down how governance is shifting from scheduled reviews to always-on infrastructure, and offers three questions to pressure-test your current posture.
Read the full articleTalk to our team about how we can put these ideas to work in your organization.
Contact Us
