The problem
Your team is already using LLMs for risk analysis. Manually.
An engineer gets a script to run in production. They copy it, paste it into ChatGPT, ask "is this safe?", read the response, and then go back to the terminal. This happens dozens of times a day across the team. No audit trail. No consistency. No infrastructure context.
And the real problem: individual queries pass every check. Together, they form an extraction pattern that only session-level context can detect.
HOW IT WORKS
A proxy, not a tool. Analysis happens where the data flows.
Hoop sits between your engineers, agents, and infrastructure. Every command passes through the proxy where the LLM sees the full picture: the query, the schema, the table sizes, and the user's session history. No SDK. No plugin. No behavioral change required. You deploy the proxy and analysis starts.
Every command passes through the Hoop proxy. The LLM sees the query, the target schema, table sizes, active connections, and user context. Analysis happens before execution, not after.
The AI risk score determines what happens next. Low risk passes through. Medium risk gets flagged. High risk gets escalated to the domain expert with the full analysis attached.
Each analysis builds on previous sessions. A single SSN query is a read. Four SSN queries in 24 hours from the same user is a pattern. The LLM sees the full history.
Use the model that fits your compliance requirements. OpenAI, Anthropic, Google, or your own hosted model. Hoop connects to your provider, your API key, your data policies.
CONTINUOUS LEARNING
Every session makes the next analysis better.
Analyses accumulate over time. Previous risk assessments and their outcomes become part of future context. A DELETE on a table that caused an incident last month gets flagged differently than one on a table that's been safely truncated weekly for a year.
The model doesn't need access to your data to learn. It learns from the patterns of access, the decisions your team makes, and the outcomes of those decisions.
ZERO FRICTION
No new clients. No new workflows. Just a proxy.
The importance of in-transit analysis is that it requires nothing new from your team. Your engineers keep using the same database clients, the same kubectl, the same CLI tools. Hoop sits in the middle as a proxy and starts passing content through LLMs automatically. The analysis, the routing, the escalation, all of it happens transparently in the infrastructure layer.
ORGANIZATIONAL IMPACT
From manual script reviews to automated risk intelligence.
Every AI analysis, every risk score, every routing decision becomes audit evidence. Your compliance team gets continuous visibility into what's happening across every session.
How many scripts did your team paste into ChatGPT this week?
That process already exists. Hoop automates it with infrastructure context, session history, and an audit trail your compliance team can use.