Agentic Endpoint Security (AES) is a cybersecurity framework that governs access and controls behavioral runtime risks within AI agent ecosystems. By monitoring the Model Context Protocol (MCP) stack, AES platforms prevent shadow AI data exfiltration, isolate malicious MCP servers, and audit developer tools at the endpoint level.
Welcome to the technical demo of KOI: Agentic Endpoint Security (AES) by Palo Alto Networks.
As enterprises adopt a secure agentic stack, security operations face an invisible boundary: traditional endpoint protection tools cannot see malicious actions taking place inside the Model Context Protocol (MCP) framework.
This deep dive reveals how KOI provides complete AI runtime protection, stopping shadow AI governance failures before they result in catastrophic intellectual property loss.
Featured Live Simulation: The Postmark MCP Attack Vector
We break down a real-world supply chain threat using a compromised "Postmark MCP" email management server. See exactly how an attacker uses simple user prompts to initiate silent data exfiltration by injecting an invisible BCC line to external servers. KOI automatically enforces behavior governance platforms, applying a version update cool-down period to neutralize the exploit instantly.
0:00 – What is Agentic Endpoint Security (AES)?
0:28 – How do you curate untrusted AI tool marketplaces?
0:52 – How does KOI secure ticketing system LLM workflows?
1:24 – Can you enforce AI behavior governance in the CLI and IDE?
1:54 – How do you get inventory visibility of active AI agent skills?
2:19 – Threat Analysis: How do malicious MCP servers exfiltrate data?
3:13 – How do you quarantine an compromised AI agent lifecycle?
4:11 – How do you stop AI supply chain attacks on version updates?
4:36 – What is the difference between Access and Behavior Governance?
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