detectiq 0.1.7

A detection engineering workbench with LLM capabilities

DetectIQ

DetectIQ is an AI-powered security rule management platform that helps create, analyze, and optimize detection rules across multiple security platforms. It can be used with the provided UI, or just with Python scripts using the self contained detectiq/core module. See examples in the examples directory for more information.

• Quickstart
• Current Features
• Road Map
• Screenshots
• Using as a Package
• Environment Configuration
• Development
• Contributing
• License
• Support & Community
• Acknowledgments

⚠️ IMPORTANT DISCLAIMER

This project is currently a Proof of Concept and is under active development:

• Features are incomplete and actively being developed
• Bugs and breaking changes are expected
• Project structure and APIs may change significantly
• Documentation may be outdated or incomplete
• Not recommended for production use at this time
• Security features are still being implemented

We welcome all feedback and contributions, but please use at your own risk!

Quickstart

To get started, run the commands below. For more information, refer to the docs!

Step 1. Clone the repository.

git clone https://github.com/AttackIQ/DetectIQ.git

Step 2. Set your environment variables (using .env.example as a template).

cp .env.example .env

Step 3. Run the provided start.sh script and pass install as an argument.

bash start.sh install

Step 4. Run the provided start.sh script and pass run as an argument.

bash start.sh run

Step 5. Use your favorite browser to navigate to http://localhost:3000.

Current Features

AI-Powered Detection

• Create and optimize detection rules using OpenAI's LLM models
• Intelligent rule suggestions based on context and best practices
• Automated rule validation and testing
• Upload malware samples and PCAP files for static analysis, automatically adding context for YARA and Snort rule creation
• LLM Rule creation analysis and detection logic returned in the rule creation response

Rule Repository Integration

• Enhanced by community-tested repositories:
  • SigmaHQ Core Ruleset
  • YARA-Forge Rules
  • Snort3 Community Ruleset
• Automatically check and update repositories with rule changes
• Vectorize rules for efficient similarity comparison for more context-aware rule creation engine

Static Analysis Integration

• Automated file analysis for YARA rules
• PCAP analysis for Snort rule creation
• Implicit log analysis for Sigma rule optimization (Explicit Analysis Coming Soon)

Multi-Platform Integration

• Automatic Sigma rule translation to various SIEM queries using pySigma and SigmAIQ wrapper
• Seamlessly create Splunk Enterprise Security correlation rules from Sigma rules

Road Map

• Custom/local LLM models, embeddings, and vector stores
• More integrations with SIEMs such as Elastic and Microsoft XDR
• Explicit log analysis for Sigma rule optimization
• Rule testing and validation
• Rule searching, e.g. "Do I have a rule in place that can detect this?"
• Deployment tracking and workflow automation
• Rule management UI Enhancements
• Authentication and Authorization
• Project refactoring for production readiness
• Chatbot (langchain agents) UI with memory
• Docker containerization and deployment
• Rule management without OpenAI requirements
• More non-webapp examples

Screenshots

Rule Dashboard with Splunk Deployment Option

Rule Dashboard with Splunk Deployment Option

Sigma Rule Creation

Sigma Rule Creation from threat report snippet

YARA Rule Creation

YARA Rule Creation using file analysis from uploaded mimikatz.exe sample

Settings Page

Settings Page

About Page

About Page

Using as a Package

DetectIQ can be installed as a Python package from PyPI:

pip install detectiq

This allows you to leverage DetectIQ's detection rule management capabilities in your own Python projects:

import asyncio
from typing import cast
import os

# Set OpenAI API key
os.environ["OPENAI_API_KEY"] = "your-api-key"

from langchain.schema.language_model import BaseLanguageModel
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from detectiq.core.llm.yara_rules import YaraLLM
from detectiq.core.llm.toolkits.base import create_rule_agent
from detectiq.core.llm.toolkits.yara_toolkit import YaraToolkit

async def main():
    # Initialize LLMs
    agent_llm = cast(BaseLanguageModel, ChatOpenAI(temperature=0, model="gpt-4o"))
    rule_creation_llm = cast(BaseLanguageModel, ChatOpenAI(temperature=0, model="gpt-4o"))
    
    # Initialize YARA tools
    yara_llm = YaraLLM(
        embedding_model=OpenAIEmbeddings(model="text-embedding-3-small"),
        agent_llm=agent_llm,
        rule_creation_llm=rule_creation_llm,
        rule_dir="./rules",
        vector_store_dir="./vectorstore",
    )
    
    # Create agent
    yara_agent = create_rule_agent(
        rule_type="yara",
        vectorstore=yara_llm.vectordb,
        rule_creation_llm=yara_llm.rule_creation_llm,
        agent_llm=yara_llm.agent_llm,
        toolkit_class=YaraToolkit,
    )
    
    # Create a rule
    result = await yara_agent.ainvoke({"input": "Create a YARA rule to detect ransomware"})
    print(result.get("output"))

if __name__ == "__main__":
    asyncio.run(main())

For more detailed examples, see the examples directory.

For instructions on publishing the package to PyPI, see PUBLISHING.md.

Environment Configuration

DetectIQ uses environment variables for configuration. A comprehensive example with documentation is provided in .env.example.

To configure the application:

1. Copy the example file to .env:

   cp .env.example .env
   

2. Edit the .env file with your specific settings:

   # Required for LLM functionality
   OPENAI_API_KEY=your-api-key-here
   
   # Optional configurations
   LOG_LEVEL=INFO
   DEBUG=False
   

3. The same .env file can be used for both the web application and the examples.

Development

DetectIQ includes a comprehensive Makefile to assist with development, testing, and publishing tasks.

Prerequisites

Before development, ensure you have:

1. Python 3.9+ installed
2. Poetry installed
3. Required development dependencies:

   make install-dev
   

This will install all development dependencies, including:

• Testing tools (pytest)
• Code quality tools (black, ruff)
• Package building tools (build, twine)
• Keyring backends (keyrings.alt) for token management

Makefile Commands

To view all available commands:

make help

Common Development Commands

# Installation
make install/local         # Complete local installation (backend + frontend)

# Running the application
make run/local             # Run both backend and frontend servers

# Code quality
make format               # Format Python files using black
make ruff                 # Run Ruff linter
make test                 # Run tests with coverage

# Package management
make update               # Update dependencies
make version              # Display current version
make version-patch        # Bump patch version (0.0.X)
make version-minor        # Bump minor version (0.X.0)
make version-major        # Bump major version (X.0.0)

# PyPI publishing
make pypi-build           # Build package for PyPI
make pypi-check           # Check package with twine
make pypi-publish         # Publish to PyPI

For more details on publishing the package, see PUBLISHING.md.

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project uses multiple licenses:

• Core Project: LGPL v2.1
• Sigma Rules: Detection Rule License (DRL)
• YARA Rules: YARAForge License
• Snort Rules: GPL with VRT License

Support & Community

• Join our SigmaHQ Discord for discussions
• Report issues via GitHub Issues

Acknowledgments

• SigmaHQ Community
• YARA-Forge Contributors
• Snort Community
• OpenAI for GPT-4o Integration