Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

With innovation comes risk. As organizations race to build AI-first infrastructure, security is struggling to keep pace. Multi-Agentic Systems – those built on Large Language Models (LLMs) and Multi-Component Protocols (MCP) - bring immense potential, but also novel vulnerabilities that traditional tools weren’t designed to handle.

CISOs everywhere are feeling the AI fatigue. Every vendor at Black Hat 2025 was hyping ‘AI agents for SecOps,’ so there’s rightfully a lot of skepticism about deploying AI in production, especially in enterprise environments. But the old way of running a SOC just isn’t working anymore. After all the time and money spent on traditional playbooks, we’re still wrestling with the same challenges: alert fatigue, burnout, tool sprawl, and inability to scale.

Model Context Protocol (MCP) is gaining traction because it enables LLMs to interact with live systems and enhance context by retrieving and managing relevant real-time information. LLMs can’t query Salesforce, trigger an Okta password reset, or fetch context from your SIEM, for example. MCP bridges that gap by connecting AI models to real-world APIs, powering AI applications like retrieval-augmented generation and multi-step agent workflows. They’re fast to deploy.

Imagine an autonomous AI agent tasked with a simple job: generating a weekly sales report. It does this reliably every Monday. But one week, it doesn't just create the report. It also queries the customer database, exports every single record, and sends the file to an unknown external server. Your firewalls saw nothing wrong. Your API gateway logged a series of seemingly valid calls. So, what happened? The agent wasn't hacked. Its mind was changed.