Intelligent Incident Response Agent

This project demonstrates of AI-powered cybersecurity pipeline which coordinated three agents that work together to automatically detect, analyze, and remediate security threats in real-time.

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🤖 Intelligent Incident Response Agent

This project is a demonstration of an advanced, AI-powered cybersecurity pipeline built for the AI Agent Hackathon. It features a coordinated system of three intelligent agents that work together to automatically detect, analyze, and remediate security threats in real-time.

Core Concept

The system is built on a three-agent pipeline, where each agent has a specialized role:

  1. 🚨 The Monitor: Ingests raw security alerts from various sources.
  2. 🧠 The Analyzer: Enriches the raw data with business context using a knowledge base and evaluates the true risk using a large language model.
  3. ⚙️ The Orchestrator: Takes the AI-driven recommendation and executes an automated remediation action, such as isolating a host or patching a system.

This multi-agent approach transforms a simple security alert into an intelligent, automated response that considers business impact and operational context.

🏆 Sponsor Technology Integration

This project proudly integrates technologies from 5 key sponsors, showcasing a modern, AI-native approach to security automation.

Sponsor Category How It's Used in this Project
Horizon3.ai 📊 Power Data Simulated Threat Ingestion: The demo simulates receiving critical vulnerability alerts from Horizon3.ai, acting as a primary trigger for the incident response pipeline. (simulated_data.py)
Redis 📊 Power Data High-Speed Messaging (Pacer): Redis serves as the communication backbone for the agents. The pacer.py module uses Redis Pub/Sub to pass threats from one agent to the next.
LlamaIndex 🧠 Add Smarts Knowledge Base & Context: LlamaIndex is used to build and query a knowledge base (knowledge_base.md) of the company's assets, enabling the AI to understand the business context of a threat.
HoneyHive 🧠 Add Smarts AI Observability (MCP): HoneyHive is integrated to trace and log the AI's decision-making process. It wraps the OpenAI calls, providing crucial observability and fulfilling the MCP requirement.
OpenAI 🧠 Add Smarts Core Intelligence: OpenAI's GPT-4 is the large language model that performs the risk analysis, severity justification, and recommends the final remediation action.

🏗️ System Architecture

The agents communicate in a seamless pipeline, passing data through the Redis Pacer.

 Threat Sources
(Horizon3, Bright Data)
       │
       ▼
┌──────────────────┐
│ 🚨 Agent 1:      │
│     Monitor      │
└──────────────────┘
       │
       │ (Publishes Raw Threat via Redis)
       ▼
┌──────────────────┐   ┌────────────────────┐
│ 🧠 Agent 2:      │──▶│   Knowledge Base   │
│     Analyzer     │   │   (LlamaIndex)     │
└──────────────────┘   └────────────────────┘
       │
       │ (Publishes Enriched Analysis via Redis)
       ▼
┌──────────────────┐   ┌────────────────────┐
│ ⚙️ Agent 3:      │──▶│  Remediation APIs  │
│    Orchestrator  │   │ (Qodo / Speakeasy) │
└──────────────────┘   └────────────────────┘
       │
       ▼
  Action Taken

Built With

  • honeyhive
  • horizon3.ai
  • llamaindex
  • redis
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