Mission
The goal of this project is to improve visibility, transparency, and awareness around cyber incidents in Canada. By presenting open data in an accessible format, it aims to support researchers, policymakers, and the public in understanding the evolving threat landscape and fostering informed discussion.
How It Works
The system automatically collects, enriches, and displays information about cyber incidents across Canada. The process follows a transparent and repeatable workflow designed for accuracy and consistency.
- 1. Data Collection — The system periodically gathers incident data from open public sources such as verified news outlets, public disclosures, and cyber incident trackers.
- 2. Parsing & Structuring — Raw text is analyzed to extract key information like date, organization, attack type, and location. Each record is stored in a structured, map-ready format.
- 3. AI Enrichment — The AI layer reads each description, summarizes it, and evaluates impact and confidence scores across multiple dimensions of real-world consequence.
- 4. Geolocation — Locations are translated into map coordinates to visualize incident distribution across Canada.
- 5. Visualization — The dataset is displayed in real time, allowing users to explore incidents, filter by type or region, and analyze national trends.
AI Enrichment Process
Each incident begins with verified public reporting and is analyzed by the AI model to extract structured context such as timeline, threat actor, and impact indicators. The model evaluates evidence across six independent dimensions to generate an overall impact and confidence score.
- Financial — monetary loss, ransom, or remediation cost
- Operational — business disruption or downtime
- Regulatory — legal or disclosure obligations
- Data — scope and sensitivity of data exposure
- Reputation — public perception or media coverage
- Future — lasting or repeatable risk potential
The AI calculates a weighted average from these six dimensions to produce a single Impact Score (0–10), representing the overall real-world consequence. The Confidence Score (0–10) reflects how well-supported the assessment is by the available evidence and consistency across sources.
Together, these values summarize what happened, how severe it was, and how certain that assessment is — without relying on a single “severity index.”
Limitations & Human Context
These AI-generated insights provide consistency and speed but should be interpreted as indicators — not verified truth. The enrichment process reflects probability and language context, not firsthand confirmation of impact or intent.
Some incidents may have limited public details or conflicting reports, reducing confidence levels. Results are designed to guide awareness and research, not serve as definitive analysis.
By combining structured AI inference with transparent source links, this project aims to make cyber threat intelligence more accessible and interpretable, encouraging collaboration between analysts, journalists, policymakers, and the public.
About the Creator
Cyber Attacks Map - Canada is a personal project developed as a way to explore the intersection of cybersecurity, data visualization, and artificial intelligence. It is built and maintained as a learning exercise to strengthen programming skills and experiment with LLM-driven data enrichment pipelines.