breach-insights

🛡️ Breach Insights Dashboard A modular analytics dashboard for visualizing breach patterns, remediation actions, infrastructure exposure, tag interactions, credential hygiene, response timelines, vendor accountability, and jurisdictional contrasts based on PDPC and international case files.

📊 Dashboard Modules

1. Taxonomy Frequency Visualizes breach tags to highlight recurring technical failures and enforcement patterns. Taxonomy Chart 🔗 taxonomy.csv

2. Remediation Actions Summarizes how organizations responded to breaches and enforcement severity. Remediation Chart 🔗 remediation.csv

3. Infrastructure Risk Map Analyzes breach surfaces across platform types (CRM, Email, POS, Git, custom portals). Infrastructure Chart 🔗 infrastructure.csv

4. Credential Hygiene Modeling Breaks down authentication risk types including MFA failure, shared credentials, and dormant accounts. Credential Hygiene Chart 🔗 credential_hygiene.csv

5. Response Lag Analysis Calculates breach-to-discovery and discovery-to-remediation delays to measure organizational responsiveness. Lag Chart 🔗 retention_lag.csv

6. Co-occurrence Matrix Maps how breach tags interact to reveal compound vulnerabilities. Co-occurrence Heatmap 🔗 cooccurrence_matrix.csv

7. Vendor Responsibility Flow Visualizes third-party accountability chains across breach events. 🌐 Interactive Sankey Chart 🔗 vendor_flows.csv

8. Jurisdiction Comparison Compares breach taxonomies, penalties, and remediation patterns across countries. Jurisdiction Chart 🔗 jurisdiction_tag_compare.csv

⚙️ One-Click Dashboard Launcher Run all modeling scripts with a single command using the orchestration shell: .\dashboard_launcher.ps1 This updates all CSV datasets and visual charts in /data/ and /visuals/.

📂 Repo Structure | Folder | Contents | | /data/ | Structured CSV outputs | | /visuals/ | Charts and interactive dashboards | | /scripts/ | Python modules for breach modeling | | Root | PowerShell launcher, README, config files |

🌍 Expansion Roadmap

• Ingest CNIL (France), GDPR (EU), and OAIC (Australia) cases
• Enhance jurisdiction_compare.py with penalty scales and taxonomy divergence
• Add modules for enforcement severity modeling and breach archetype clustering
• Embed dashboard into Streamlit or Observable for public intelligence observatory

📊 Background to dashboard creation: An initial exploratory analysis of six distinct breach-related use cases, using an event study methodology was conducted to assess whether market reactions around breach disclosures indicate statistical significance and whether insights could be drawn.

🧠 Key Insight: Despite applying conventional event study models, the results do not show statistically significant reactions across the sample. This absence of significance highlights potential limitations in traditional modeling frameworks—and suggests:

• Either market efficiency (events were priced in or not considered material),
• Or model limitations (event windows, benchmarks, or estimation methods may dilute observable impact),
• Or a more complex, delayed response that the event study framework doesn't capture well.

🔍 Research Directions: The results shows why there's still room for research and opens new pathways for exploration >>> Breach Insights