RAG-Based Threat Model: A Video Threat Detection Framework Using Retrieval-Augmented Intelligence

Core Overview of the RAG-Based Threat Model

This project is a video content threat detection framework based on Retrieval-Augmented Intelligence (RAG), integrating speech transcription, hybrid semantic retrieval, and Large Language Model (LLM) reasoning capabilities to achieve threat detection, topic analysis, sentiment assessment, and risk scoring for long video content. It aims to address the pain points of traditional video moderation, which relies on manual work or keyword matching and struggles to handle complex and implicit threats.

Project Source: Released by GitHub user khankriyalrohit on 2026-06-13, original link: https://github.com/khankriyalrohit/RAG-Based-Threat-Model

Background & Motivation

Today, videos have become the main carrier of information dissemination, but long videos hide security risks such as violence and extremism. Traditional moderation methods have the following shortcomings:

• Rely on manual moderation, which is inefficient and high-cost
• Simple keyword matching cannot identify complex and implicit threat content

The RAG-Based Threat Model proposes an innovative solution: combining RAG technology with LLM to build an end-to-end intelligent threat detection pipeline.

Technical Architecture & Key Mechanisms

1. Speech Transcription Module

Extracts the audio stream from the video and converts it into text, while capturing paralinguistic information such as intonation and speech rate to provide clues for sentiment analysis.

2. Hybrid Semantic Retrieval System

• Vector Semantic Retrieval: Encode text into vectors and retrieve conceptually related content based on semantic similarity
• Keyword Retrieval: Precisely match key terms to avoid omissions
• Hybrid Ranking: Combine results from both methods to re-rank and generate the final retrieved content

3. LLM Reasoning Engine

Based on the retrieved context, it completes:

• Threat detection (violence, hate speech, etc.)
• Topic analysis (core topic extraction)
• Sentiment assessment (identification of negative emotions/radical speech)
• Risk scoring (multi-dimensional quantitative results)

Advantages of RAG Architecture

• Flexible knowledge update: No need to retrain the model; updating the retrieval library can introduce new threat patterns
• Strong interpretability: The retrieved context provides traceable basis for decisions
• Cost-effectiveness: Reduces token consumption and lowers reasoning costs

Practical Application Scenarios

1. Content Moderation Platforms

Automatically screen uploaded videos, identify non-compliant content, and risk scores help prioritize high-risk videos, reducing manual pressure.

2. Security Intelligence Analysis

Analyze surveillance footage and online videos to identify threat intelligence; topic/sentiment analysis helps understand content intent and context.

3. Public Opinion Monitoring

Media/PR agencies monitor public opinion on video platforms, timely detecting negative trends and crisis signals.

Key Technical Implementation Points

Data Flow Processing

Adopts a streaming architecture to support real-time/near-real-time analysis:

1. Video input → Audio extraction → Speech transcription
2. Text chunking → Vector encoding → Index construction
3. Query generation → Hybrid retrieval → Context assembly
4. LLM reasoning → Result output → Risk scoring

Scalability Design

• Modular architecture: Supports replacement of speech recognition engines or LLM backends
• Distributed retrieval: Handles large-scale video libraries
• API interface: Facilitates integration into existing systems

Summary & Outlook

The RAG-Based Threat Model demonstrates the innovative application of RAG technology in the field of video content security. Through the organic combination of speech transcription, semantic retrieval, and LLM reasoning, it provides an effective tool for intelligent analysis of long videos.

Future Directions: Integrate visual information to achieve multi-modal threat detection; continuously optimize retrieval and reasoning efficiency.

Value: Provides a reference implementation for developers focusing on content security and AI applications.