Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security teams know all too well the grind of manual investigations and remediation. With the mass adoption of AI and increasingly automated attacks, defenders cannot afford to rely on overly manual, low priority, and complex workflows. Heavily burdensome manual response introduces delays as analysts bounce between consoles and high alert volumes, contributing to alert fatigue. Even worse, it prevents security teams from dedicating time to high-priority threats and strategic, innovative work.

Organizations face continuous threats from phishing, business email compromise (BEC), and other advanced email attacks. Attackers adapt their tactics daily, forcing defenders to move just as quickly to keep inboxes safe. Cloudflare’s visibility across a large portion of the Internet gives us an unparalleled view of malicious campaigns. We process billions of email threat signals every day, feeding them into multiple AI and machine learning models.

Security professionals everywhere face a paradox: while more data provides the visibility needed to catch threats, it also makes it harder for humans to process it all and find what's important. When there’s a sudden spike in suspicious traffic, every second counts. But for many security teams — especially lean ones — it’s hard to quickly figure out what’s going on. Finding a root cause means diving into dashboards, filtering logs, and cross-referencing threat feeds.

Large Language Models (LLMs) are rapidly evolving from impressive information retrieval tools into active, intelligent agents. The key to unlocking this transformation is the Model Context Protocol (MCP), an open-source standard that allows LLMs to securely connect to and interact with any application — from Slack to Canva, to your own internal databases. This is a massive leap forward.