Agentic AI: Security's Next Blind Spot Explained

Agentic AI is already operating in many organizations, executing tasks and consuming data without significant security team involvement. The core issue isn't just about policy, but whether security professionals truly understand this technology. Currently, in most organizations, they don't, and this gap is growing rapidly. You cannot secure what you do not understand. Just like with firewalls and cloud computing, foundational knowledge is crucial before effective defense is possible. Security teams unable to engage with AI engineering risk being bypassed, as business units move forward without their substantive input. This has happened with every major tech shift. The solution starts with engagement, encouraging security teams to build and experiment with agents to gain hands-on familiarity. This understanding is key to addressing the risks. The agentic AI landscape includes general-purpose coding agents like Claude Code and GitHub Copilot, which are already in use. It also includes vendor-built agents powered by the Model Context Protocol, or MCP, which allows agents to connect to external services. Understanding these different categories and their risks is essential for effective security. This directly impacts your organization's ability to protect itself from emerging threats.