Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Salt Security and CrowdStrike give joint customers visibility across the full agentic path, from the model to the system where the action lands An employee at a bank asks an AI assistant a routine question. How much money is in my savings account? The assistant answers correctly. The prompt was legitimate. The response was accurate. A model-layer security control inspects both and finds nothing wrong, because nothing is wrong with either. Now look at what happened in between.

AI coding assistants have gotten remarkably good at writing functional code. Syntax correctness rates are approaching 100%. Developers are more productive than ever. And yet the security picture tells a very different story.

Modern organizations don't operate from a single server room anymore. Today's enterprise environment spans dozens of cloud services, SaaS applications, APIs, AI agents, and non-human identities, all of which are continuously changing. A quarterly security audit is no longer a safety net, but now considered a gap.

Agentic AI governance is about keeping powerful, autonomous AI systems aligned, safe, and accountable as they act on our behalf. It’s now a certainty that AI agents will be deployed enterprise-wide. So, we need to look more deeply into those agents, figure out where they are, how to find them, and fully understand what they are doing in deployment so we can prevent attacks. The most dangerous agentic attacks will not look like attacks at the layer where they originate.

Your WAF is doing its job. It's blocking SQLi, XSS, and the usual suspects. But here's the problem: it wasn't built for APIs, and it definitely wasn't built for AI agents. APIs now power nearly every digital experience. And AI agents — the automated systems that access your APIs at machine speed, at machine scale — are the fastest-growing source of that traffic.

AI agents are rapidly moving from experimental projects into everyday business operations. Unlike traditional AI systems that generate content or answer questions, AI agents can take action. They can call APIs, access applications, retrieve data, execute workflows, and make decisions with limited human intervention. That shift is creating a new security challenge for enterprises.

Last week, an AI system crossed a line many still considered theoretical. During an internal cybersecurity evaluation, OpenAI tested a combination of models, including GPT-5.6 Sol and a more capable pre-release model, on ExploitGym, a benchmark that measures whether agents can turn software vulnerabilities into working exploits. The models were run with reduced cyber refusals and without the production classifiers normally used to prevent high-risk cyber activity.

AI agents are moving from experiments into production workflows, and the Model Context Protocol (MCP) is becoming the connective layer that enables those agents to access enterprise data, applications, APIs, repositories, and automation tools. That makes MCP powerful, but also security-critical. As organizations adopt agentic AI, they need to understand not only how MCP improves connectivity but also how it creates new visibility, governance, and attack-surface challenges.