Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Large language models (LLMs) are transforming enterprise operations, but their growing use introduces a critical security challenge: securing how they access sensitive data and integrate with existing tools. This is where Model Context Protocol (MCP) servers become a vital, yet often overlooked, part of AI security. These servers act as the crucial link, enabling LLMs to securely connect with diverse data sources and tools, significantly expanding attack surfaces that demand our immediate attention.

Any organization that’s undergone a security transformation knows the promise of zero trust network access (ZTNA): secure, least-privilege access to private applications, anywhere, on any device. But turning that promise into operational reality is often far from simple. Between fragmented tools, complex configurations, and sprawling environments, implementing ZTNA can quickly become a manual, time-consuming, and error-prone process.

AI is no longer a futuristic concept – It has now become the core of how modern businesses are operating. Starting from chatbots to analytics, generative content to automation, AI is across every sector of business. As AI usage increases, the risks and regulation are also increasing.

The companies winning with AI aren’t just deploying agents faster - they’re operationalizing them responsibly. They realize AI agents are creating a new, dynamic attack surface that traditional tools were never designed to handle. These agents span the entire enterprise ecosystem. Microsoft 365 Copilot, Copilot Studio, and Salesforce Agentforce are SaaS‑managed agents. GitHub Copilot, Cursor, and Claude desktop run directly on user devices as device‑based agents.