Tetiana Vashchenko
Most security teams haven’t inventoried their AI agents, let alone assessed the risks those agents introduce in enterprise environments. That’s a problem because AI agents in production environments have something attackers want: credentials, access, and the ability to take action autonomously.
If you have AI agents in production, there’s a good chance you’ve invested in monitoring them — log forwarding to your SIEM, dashboards showing agent activity, maybe some custom detection rules for anomalous behavior.
AI agents are the fastest-growing and least-governed attack surface in enterprise environments today. AI runtime security protects AI models and applications during their active operation, continuously monitoring for threats in real-time.
AI agents have crossed a threshold. They no longer just generate text — they write to databases, call payment APIs, send communications, and modify records autonomously. Traditional security tools (DLP, SIEM, WAF, IAM) were built for human-generated actions and static data flows. They cannot evaluate what an AI agent decides to do at runtime.
I Is No Longer Just Generating Content. It’s Taking Action. Artificial intelligence has come a long way - it's no longer just an assistant, but a real-life doer. What used to be tools for summarizing content and answering questions have evolved into systems that can get real work done in enterprise environments.
Microsoft 365 Defender works. Your organization's email filtering catches the majority of phishing attempts, spam, and malware. The reputation engines are solid. The pattern matching catches known threats. And Defender will continue to improve. But here's what nobody tells you: catching 95% of phishing threats isn't the same as stopping phishing threats.
The difference between a threat intelligence program that works and one that just generates data is the architecture connecting sources to enforcement. Most organizations get the first part right (connecting a feed to the SIEM) and stop there.
This article explains how organizations can reduce SIEM alert fatigue by using validated threat intelligence, risk scoring, and proper threshold tuning. It highlights the cost of false positives, compares open-source and commercial feeds, and shows how context, scoring, and architecture changes improve detection accuracy and reduce operational overhead.
Most threat intelligence programs fail because the data is ingested but never integrated into actual SOC workflows. This article explains how to operationalize threat intelligence in Microsoft Sentinel by connecting structured ingestion, correlation rules, automated enforcement, and dashboards.