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Introduction:
Open-source intelligence (OSINT) analysts lose roughly 80% of their time to noise, manually sifting through thousands of fragmented news reports, satellite feeds, and SIGINT data streams. Redroom, a new open-source full-stack platform from Owlink.ai, directly solves this by providing a unified, command-and-control style dashboard for real-time geopolitical threat intelligence, aggregating hundreds of global sources into a single, structured picture.
Learning Objectives:
- Master the automated collection of open-source intelligence using scheduled RSS crawler missions and a 3D interactive globe.
- Implement AI-assisted analysis for narrative detection, sentiment scoring, and cross-source validation.
- Deploy and configure a self-hosted geopolitical OSINT dashboard with role-based access control (analyst, admin, super admin).
You Should Know:
1. Rapid Deployment of Redroom OSINT Platform
This open-source platform operates on a modern tech stack (React 19, Node.js, Three.js). The following step-by-step guide will help you set up a production-ready instance on a Linux server (Ubuntu 22.04/24.04), allowing you to aggregate real-time geopolitical intelligence without classified system access.
Step 1: System Preparation & Cloning
First, update your system and install essential tools for building the platform:
sudo apt update && sudo apt upgrade -y sudo apt install git curl wget build-essential -y
Now, clone the official Redroom repository from GitHub:
git clone https://github.com/Owlinkai/redroom cd redroom
Step 2: Environment Configuration
Copy the example environment file and edit it to include your database credentials, API keys for external feeds (news, satellite), and session secrets. This ensures secure communications between the frontend, backend, and crawler services.
cp .env.example .env nano .env Set DB_PASSWORD, JWT_SECRET, SATELLITE_API_KEY, etc.
Step 3: Build Frontend & Backend Artifacts
Install dependencies using `npm` or yarn. The frontend relies on Three.js for the interactive globe component, while the backend manages tRPC routes and crawler missions.
npm install npm run build Compiles React/TypeScript frontend npm run migrate Sets up database schema for articles, facilities, missions
Verification: After build, confirm the Node.js service is listening (e.g., netstat -tulpn | grep 3000).
2. Configuring Automated OSINT Crawler Missions
The core value of Redroom is its scheduled, configurable crawler system. This transforms the typical manual OSINT workflow into automated intelligence gathering.
Step-by-step Configuration:
- Access the admin CMS panel via `https://your-server/admin` using super admin credentials.
2. Navigate to “Missions” → “Create New Mission.”
3. Define the parameters:
- Region: e.g., “Middle East” or “Eastern Europe”
- Source Types: RSS feeds from hundreds of global media agencies (pre-configured list)
- Keywords & Filters: Targets, geopolitical events
- Schedule: Cron expression (e.g., `/15 ` for every 15 minutes)
- The system will autonomously harvest, deduplicate, and sentiment-score articles, linking them to tracked facilities (military, nuclear, energy).
- Analysts view the live feed in the dashboard—rather than reading raw news—and can export structured intelligence reports.
3. 3D Globe Visualization for Threat Mapping
Redroom features a Three.js-based interactive 3D globe for geographical threat visualization. This component plots real-time articles, satellite positions, and facility locations onto a sphere, providing immediate situational awareness.
Using the Globe Interface:
- Navigate to “Globe Selector” from the main dashboard.
- Pan (drag) and zoom (scroll) to inspect regions.
- Threat overlays are color-coded (red = active conflict, orange = elevated tension).
- Animated pulsing markers indicate recent intelligence activity (news spikes, SIGINT anomalies).
- Clicking any marker reveals source-linked intelligence reports, including linked facilities and narrative scores.
Under the hood: The globe is powered by React 19, TypeScript, and Three.js, reading coordinates from a GeoJSON structure defined in the backend API.
4. Satellite Orbit Tracking with TLE Data
For analysts monitoring reconnaissance and communication assets, the platform integrates real-time orbital tracking. It ingests Two-Line Element (TLE) sets from public catalogs to predict passes and map satellites in low Earth orbit.
Command-line OSINT for Satellite Data (Alternative/Complement):
While Redroom provides a GUI, advanced analysts can use dedicated tools like `satintel` for deeper telemetry extraction:
git clone https://github.com/juL101v4n/satintel cd satintel go build ./satintel -tle "https://celestrak.com/NORAD/elements/gp.php?CATNR=25544" Track ISS
This tool parses TLE data, predicts orbital paths, and can be integrated into Redroom via a webhook for enhanced visualization.
5. SIGINT Dashboard for Signal Intelligence Indicators
One of the most powerful features is the SIGINT dashboard, which displays signal intercept indicators and electronic intelligence summaries. While Redroom aggregates this from external feeds, professionals can extend it with local SDR (Software Defined Radio) tools.
Integrating a SIGINT Dashboard (for self-hosted extensions):
Use the open-source “Intercept” dashboard to consolidate local radio decoding tools into a web UI, then feed indicators to Redroom.
git clone https://github.com/smittix/intercept-dashboard cd intercept-dashboard ./install.sh Installs dump1090, rtl_433, multimon-1g ./intercept Launches dashboard on port 8080
This provides real-time decoding of aircraft transponders (ADS-B), weather sensors, and pager traffic, which can be correlated with Redroom’s geopolitical news.
6. AI-Powered Narrative Detection & Disinformation Analysis
The platform’s Narrative Engine uses AI to detect coordinated information operations. It flags suspicious content patterns, sentiment anomalies, and cross-source inconsistencies that suggest disinformation campaigns.
Manual Check & Tutorial: Using an AI Narrative Detection Script
Analysts can supplement Redroom with a Python script that uses LLMs (e.g., via Ollama or OpenAI API) to analyze article clusters. This script fetches recent articles, prompts the model to identify narrative patterns, and prints a risk score.
narrative_detector.py
import requests
import json
<ol>
<li>Fetch article cluster from Redroom API (or other OSINT source)
response = requests.get('https://your-redroom/api/articles?region=EU&limit=50')
articles = response.json()['data']</p></li>
<li><p>Prepare prompt for local LLM (Ollama)
prompt = f"""
Analyze these headlines for coordinated narratives, propaganda markers, and sentiment anomalies:
{json.dumps([a['title'] for a in articles], indent=2)}
Output: JSON with risk score (0-100) and narrative themes.
"""</p></li>
<li><p>Query LLM (example using Ollama running locally)
llm_response = requests.post('http://localhost:11434/api/generate',
json={"model": "llama3", "prompt": prompt, "stream": False})
analysis = llm_response.json()['response']
print(analysis)
This script can be scheduled via cron to automatically feed analysis back into Redroom via its API webhook.
7. Cross-Source Verification & Citation Scoring
To combat misinformation, the platform implements a “Reference Checker” that scores citation integrity. It automatically correlates claims across multiple news agencies and official sources, flagging stories with low cross-source consensus.
Implementation for Custom OSINT Pipelines:
When building your own intelligence gathering, always include multi-source validation. Use Python libraries like `newspaper3k` to scrape and compare article bodies:
pip3 install newspaper3k nltk
cross_validate.py from newspaper import urls = ["https://source1.com/article", "https://source2.com/article"] texts = [] for url in urls: article = (url) article.download() article.parse() texts.append(article.text) Use cosine similarity (scikit-learn) or LLM to check consistency
Redroom automates this scoring, assigning a “Citation Integrity Score” (0-100) to each piece of intelligence.
8. Hardening & Production Deployment
For intelligence operations, security is paramount. Redroom supports role-based access control (analyst, admin, super admin) and can be deployed behind a reverse proxy with SSL.
Production Hardening Steps:
- Set up Nginx as reverse proxy: Terminate TLS (Let’s Encrypt) and proxy to Node.js.
sudo apt install nginx certbot sudo certbot --1ginx -d redroom.yourdomain.com
- Configure database firewall rules: Allow only localhost or specific analyst IPs for PostgreSQL.
- Enable audit logging: Monitor user actions via the CMS admin panel’s built-in logging.
- Regular updates: Pull latest Git changes and rebuild ( `git pull && npm install && npm run build` ).
What Undercode Say:
- Key Takeaway 1: Redroom’s automated pipeline reduces OSINT analysis time by over 80%, freeing analysts from data collection to focus on decision-making.
- Key Takeaway 2: The integration of AI narrative detection with real-time SIGINT dashboards marks a shift from passive monitoring to active, cross-validated intelligence generation.
Prediction:
- +1 The democratization of AI-powered OSINT via open-source platforms like Redroom will force state and non-state actors to adopt more sophisticated deception tactics, accelerating the arms race in narrative warfare.
- -1 The proliferation of such accessible threat intelligence tools could lower the barrier for malicious actors (e.g., hacktivists, cybercriminals) to conduct sophisticated reconnaissance, increasing global instability.
- -1 Over-reliance on automated correlation scoring risks introducing systemic biases if the underlying AI models are not continuously audited, leading to potential false positives and strategic miscalculations.
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