Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The problem is not a lack of controls. It is connecting them into one attack story. The more time I spend with enterprise AI deployments, the clearer one thing becomes: AI security is incredibly fragmented. There are LLM guardrails, AI gateways, MCP security tools, API security, endpoint controls, SASE, code scanning, and runtime detection. Each solves a real problem, but agentic systems do not experience them as separate layers, and neither do attackers.

Agentic AI has moved from experimental research to live production environments at unprecedented speed, outpacing nearly every technology security leaders have encountered in recent history. Distinguishing themselves from standard chatbots that merely respond and pause, autonomous agents architect multi-step workflows, interface with tools and APIs, maintain contextual memory, and execute operations with minimal human intervention.

These two photos were taken almost ten years apart. The first is from 2016, with Sam Altman at Y Combinator. The second came from an unexpected encounter in Silicon Valley almost a decade later. I’ll come back to it at the end. With Sam Altman at Y Combinator in 2016. I was 22 when I arrived in Silicon Valley on a one-way ticket, with a little bit of cash that was barely enough for one month of living there, and a thesis I wanted to prove.