Employees adopted generative AI faster than any enterprise technology I've seen in two decades. Security teams are now catching up on assistants, copilots, browser extensions and, increasingly, autonomous agents that act on their own. In conversations with security leaders, the same questions come up again and again.
1. "What AI is actually running in my environment?"
You can't govern what you can't see. The first need is inventory: sanctioned and unsanctioned AI apps, browser extensions, models, AI API usage and the agents and MCP servers that connect models to tools and data. Shadow AI is usually far larger than anyone expects.
2. "Is sensitive data leaving through AI tools?"
Source code, customer PII, credentials and strategy documents get pasted into public assistants every day, usually by well-meaning people trying to work faster. Effective data protection here works inline, in the browser, at the moment of paste or upload, and it coaches the user instead of only blocking them.
3. "What are agents doing on my endpoints?"
Agents don't just answer questions; they read files, call APIs and run commands. Security teams want to know which tools an agent touched, what data it accessed, and to stop risky actions in real time, with a full audit trail.
4. "Can my AI applications be manipulated?"
Prompt injection, jailbreaks and data leakage through model responses are now real attack paths. Frameworks like the OWASP Top 10 for LLM Applications give teams a shared vocabulary for testing and controls.
5. "How do I prove this to auditors?"
Regulations and frameworks such as the EU AI Act and NIST AI RMF are turning AI governance into an evidence problem: policies, inventories, risk assessments and logs that an auditor can review.
The organisations getting AI security right don't ban AI. They make the safe path the easy path.
Security that blocks productivity gets bypassed. Security that gives people a safe way to use AI gets adopted, and that's the only kind that works.
