Lessons from securing AI systems at runtime (agents, MCPs, LLMs)
Over the last year, we noticed a pattern while working with teams deploying AI in production.The hard problems weren’t model choice or prompt quality. They showed up after deployment.Agents started calling tools nobody remembered wiring up. LLMs accessed internal APIs through chains that weren’t obvious from logs. Execution identity diverged from user identity. MCP servers became quiet but...