AI Threat Detection
Prudent Partners annotated security-related visual data to train an AI model for detecting threat indicators in digital environments. The labeled dataset improved model accuracy for real-time cybersecurity applications.
Case Details
Clients: Pixel Art Company
Start Day: 13/01/2024
Tags: Marketing, Business
Project Duration: 9 Month
Client Website: Pixelartteams.com
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Executive Summary
A leading AI-based risk mitigation platform enhanced its threat detection capabilities by implementing a rigorous visual data annotation process. The objective was to reduce false positives and improve model performance through consistent, granular labeling of real-world elements such as weapons, postures, and false indicators.
Introduction
Background
The project involved annotating surveillance footage to identify threats including weapons, falls, and environmental hazards. High-quality annotations were critical for training machine learning models to enable accurate, real-time threat recognition.
Industry
AI Risk Mitigation / Computer Vision / Security & Surveillance
Products & Services
The project leveraged annotation workflows tailored for threat detection, supporting object detection and classification models capable of identifying high-risk elements in live or recorded surveillance footage.
Challenge
Problem Statement
The initial dataset of approximately 10,000 images contained:
- Inconsistent bounding boxes
- Mislabeled items (e.g., phones labeled as weapons)
- Low-quality visuals
This undermined model reliability and reduced threat classification accuracy.
Impact
- Elevated false positives
- Decreased model performance
- Delayed deployment of AI threat detection systems
Solution
Overview
A structured annotation strategy was implemented, emphasizing class hierarchy, annotation consistency, and decision-tree-based labeling.
Implementation Approach
- Developed a five-level annotation hierarchy for threat classification
- Defined three main object categories: Detected Threats, Common False Positives, and Future Targets
- Re-annotated existing datasets, removing low-quality images and ensuring weapon components were visible
- Applied strict quality control and trained annotators using detailed custom guidelines
Tools & Resources Used
- AI-assisted annotation platforms
- Manual expert review and multi-stage QA
- Metadata tagging for interpretability
Results
Outcome
The cleaned and restructured dataset enabled the AI system to distinguish true threats more effectively.
Benefits
- Improved model accuracy and reduced false positives
- Faster training and deployment cycles
- Enhanced interpretability through metadata tagging
Conclusion
Summary
The project demonstrated the critical role of accurate annotation in threat detection systems, significantly improving AI readiness and reliability.
Future Plans
- Extend annotation to include complex threats such as smoke, PPE violations, and posture-based risk detection
- Continuously refine datasets to support evolving security requirements
Call to Action
Organizations aiming to strengthen AI threat detection models can adopt this structured annotation approach to achieve higher reliability, reduced false positives, and faster deployment cycles.