Threat Landscape Platform
A platform that turns enterprise and platform risk signals into actionable team-level insights, attack paths, and prioritized remediation narratives.
Problem
Risk signals are often spread across scanners, cloud posture tools, tickets, dashboards, and exception processes. Teams struggle to understand which findings matter, how risks connect, and what they should fix first.
What I built
I built a searchable threat landscape platform that presents enterprise and platform threats in a consumable format for engineering teams. The platform models risk signals, affected services, attack paths, campaigns, owners, and remediation context.
AI usage
AI is used to generate attack scenarios and attack-path narratives so findings are not just raw vulnerability records. This helps teams understand the path from misconfiguration to exploitability and prioritize the most meaningful fixes.
Architecture
- Next.js frontend for interactive risk views and team-focused workflows.
- FastAPI Python backend for APIs, enrichment, and orchestration.
- PostgreSQL for structured storage of threats, mappings, ownership, and history.
- AI summarization and narrative generation for context-rich remediation guidance.
Impact
The value is faster prioritization, clearer ownership, and less manual translation between security findings and engineering action. It demonstrates how platform engineering can make security understandable and actionable at scale.