USE CASE

Close the Runtime Security Blind Spot

AI risk materializes when agents execute code, chain tools, load models, and access data. Miggo detects and stops agent hijacking, model supply chain abuse, and tool misuse in real time before they impact production systems.

Move to Active AI Defense

Why AI Runtime Detection Matters

The Execution Gap

AI gateways and firewalls inspect prompts and responses, but they can’t see what happens after the model responds. The real attack surface lives inside runtime execution.

Exploit Velocity Is Insane

A single goal hijack can cascade into database queries, file access, or outbound connections within seconds. By the time an alert is reviewed, the damage may already be done.

Supply Chain Is Now Runtime

Developers connect external models and MCP servers directly into production workflows, where malicious model files, unsafe deserialization, or hidden payloads can execute immediately upon loading.

How Miggo Delivers Agentic Detection and Response

Continuous Discovery & Control over Your Agentic Topology

Execution Level Visibility

You can’t secure what you cannot see. Miggo monitors AI applications from the inside out, tracing model loading, tool invocation, file access, network behavior, and system actions across agents and MCP toolchains. This gives security teams a real time view of what AI systems are actually doing in production, not just what they were designed to do.

Behavior Based Detection

Miggo detects when an agent deviates from its intended behavior. If a support agent attempts code execution, accesses new data paths, or expands its privilege scope, we flag the violation with full context and blast radius.

Agent goal hijacking

Prompt injection that alters execution

Model supply chain compromise

Unauthorized tool chaining

Remote code execution attempts

Active Response with Evidence

Detection without response is just noise. Miggo blocks harmful execution in real time, preventing unauthorized actions before they impact production systems. Every incident is correlated into a clear forensic chain from user input to agent decision to system action, enabling fast containment, confident reporting, and audit ready evidence.