An internal AI business intelligence tool at Datadog allowed a commercial sales rep to query enterprise-team performance data via SQL, exposing gaps in permissioning assumptions.
Where this was said
At 4:55 · chapter starts 3:50
With broad AI adoption came an unexpected data governance reckoning. Escobar describes a business intelligence tool Datadog built internally that started surfacing data in ways that raised eyebrows. The culprit wasn't a permissions breach in the traditional sense — the data was always technically accessible to someone who knew the right SQL. But AI changed who 'someone who knows SQL' includes. A commercial sales rep, with no SQL expertise, prompted the AI to figure out the query and pulled enterprise team performance data that was supposed to stay siloed [1] — Emilio Escobar "AI doesn't need to hack your permissions system — it just needs to be prompted. A Datadog commercial sales rep used an internal AI tool to …" 04:40 . It wasn't a catastrophic leak, but it was a clear signal: the assumption that data permissioning would serve as a practical access barrier no longer holds when AI can generate any query on demand. Escobar frames it directly: 'AI is going to find a way to get it. All you have to do is prompt it.'
Datadog achieved approximately 98% employee adoption of some form of AI tool, spanning coding agents to general-purpose LLMs.
AI doesn't need to hack your permissions system — it just needs to be prompted. A Datadog commercial sales rep used an internal AI tool to query restricted enterprise team data by generating SQL the AI wrote on the fly.
Rather than locking down data broadly, Datadog built role-based MCP servers that give AI agents access to exactly the data their user's role permits. Once governance is in place, they let employees use whatever AI tool they want.
Datadog implemented role-based MCP servers (e.g., one for SDRs) to control what data AI agents can access by role, replacing overly broad permissions.
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