Guides
The questions a review actually asks
Plain, complete answers first, product second. Each guide answers one question a compliance or engineering lead actually searches, and is honest about the limits of every mechanism, including ours.
How do you prove to an auditor what your LLM calls actually did?
Application logs can be edited, so they are testimony, not evidence. What an auditor can actually accept for AI activity: complete records, tamper-evidence, independent verifiability, and how to get all three without a platform team.
What do OSFI B-13 and E-23 mean for a firm's LLM usage?
OSFI's technology-risk guideline (B-13) and revised model risk guideline (E-23) both reach AI use at federally regulated financial institutions. What each asks for at a high level, and the evidence a firm needs to have ready.
What does a SOC 2 audit expect from your AI usage?
SOC 2 doesn't have an AI section, but Type II auditors sample evidence, and LLM calls are activity your controls have to cover. What auditors ask about AI usage, and what evidence answers it.
What is AI tokenomics, and can a dashboard actually control it?
Agentic AI turned token spend from a line item into a risk: a single complex agent task can burn tens of thousands of tokens, and the meter runs in the background. Why watching costs and governing them are different products, and what governing actually looks like.
Do you need an AI gateway or AI observability?
Observability platforms watch your AI traffic; a gateway sits in the request path and decides. Which one you need depends on one question: do you need to see what happened, or prove and enforce what happens? Often the honest answer is both.
What is shadow AI, and how do you actually find it?
Shadow AI is every LLM call your teams make that nobody logged: a developer points a coding agent or script at a provider in minutes and no record exists. Why dashboards can't see it, and how to go looking.