Wire an Agent to Real Data Without It Going Rogue

An agent that can't touch anything is useless. An agent that can touch everything is dangerous. The answer is in the middle, and it isn't a better prompt. The easy way to put AI over your data is to hand an LLM the database and say "have at it". Sometimes the SQL it writes is right. The problem is "sometimes". In this one I walk through the alternative: a governed layer over your data, with scoped permissions, approvals and limits enforced by the system, so the same question gets the same, correct answer every time. Building with agents? https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit - The tension: useful access versus safe access - Why free access and LLM-written SQL isn't good enough - Scoped permissions, approvals and limits enforced by the system - Semantic models (Apache OSI, Snowflake, Malloy) so a metric means one thing 0:00 Useful and safe at the same time 0:42 How do I deploy AI over my data? 1:26 Should everyone LLM their own reports? 2:04 The wrong way: free access, LLM-written SQL 2:38 The right way: scoped permissions, approvals and limits 3:32 The security cost: training on and leaking your data 3:54 Most leaks start inside the building 4:34 Semantic models: teach the LLM how your data fits together 5:10 Gross profit, calculated two ways 5:49 Sensible security defaults, not lockdown Concept To Cloud builds agentic AI that is safe to run in production. Run the AI-Readiness Audit: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit #AI #agents #MCP #aisafety