project-1e0d3a4f

byGAURAV NIKUMBH

Build a SaaS web application called ThreatFlow AI An AI-powered Security Incident Triage and Product Decision Assistant. This application is designed for Product Managers, Security Engineers, SOC Analysts, and Engineering Managers working inside a cybersecurity company. The application should feel like Linear + Notion + GitHub + ChatGPT. Use a clean modern dark UI. Primary colors: Black Dark gray Purple accent Green success Orange warnings Red critical Use rounded cards and subtle animations. Overall Layout Left Sidebar Dashboard New Incident Incident History Engineering Plan Architecture Reports Settings Main Content Area Top navigation with Search Notifications Profile Dashboard Show KPI cards Open Incidents Critical Incidents Average Resolution Time High Customer Impact Upcoming Releases Affected Display Incident Priority Distribution Severity Pie Chart Timeline Latest Incidents Top Root Causes Engineering Workload New Incident Screen Large text area Placeholder "Paste a security incident, CVE, SOC alert, customer escalation, penetration test result, or vulnerability report." Example Customer reports unauthorized API requests. Authentication bypass suspected. Multiple failed login attempts followed by successful access. OAuth token replay may be occurring. Affected product Cloud WAF Buttons Analyze Incident Clear AI Analysis Engine When Analyze is clicked Generate Incident Title Executive Summary Severity Critical High Medium Low Confidence Score CVSS Estimate MITRE ATT&CK Mapping Potential Root Cause Affected Components Affected APIs Potential Customer Impact Business Risk Recommended Response Estimated Engineering Effort Suggested Owner Frontend Backend Platform Security Infrastructure Risk Score Calculate Likelihood × Impact × Exploitability Display Overall Risk Score 0–100 Gauge visualization Color Green Yellow Orange Red Customer Impact Estimate Number of customers affected Revenue impact Compliance impact Reputation risk Support ticket volume Display Low Medium High Critical Engineering Plan Generate Recommended fixes API changes Database changes Infrastructure changes Logging improvements Monitoring improvements Security controls Rate limiting Input validation Encryption Token rotation RBAC improvements Secret management Generate Implementation roadmap Immediate This Week Next Sprint Long Term Generate GitHub Issues Each issue includes Title Description Acceptance Criteria Priority Labels Story Points Dependencies Generate Pull Request Checklist Unit Tests Integration Tests Security Review Documentation Backward Compatibility Feature Flag Monitoring Rollback Strategy Architecture Screen Draw an interactive system diagram Client ↓ Load Balancer ↓ API Gateway ↓ Authentication ↓ Application Service ↓ Database ↓ SIEM ↓ Monitoring Highlight affected services in red. Incident Timeline Automatically create Detection Investigation Containment Fix Deployment Verification Resolution Each stage should have Owner Status ETA Reports Screen Generate Executive Summary Engineering Summary Customer Communication Draft Internal Slack Update Release Notes Lessons Learned Search Allow searching incidents Severity Component API Customer Owner Date History Store incidents locally. Allow reopening. Settings Enable Dark Mode OpenAI Provider Anthropic Provider Gemini Provider Model Selection Temperature Sample Data Include 10 realistic cybersecurity incidents API abuse Credential stuffing Ransomware attempt Container escape Privilege escalation Data leakage Broken authentication SQL injection Cloud misconfiguration Supply chain attack UI Quality Responsive Modern Enterprise SaaS Professional Minimal Animations Fast interactions Cards Badges Charts Timeline Progress indicators Expandable AI reasoning sections Syntax-highlighted code blocks for generated implementation guidance Markdown support AI Logic Whenever an incident is analyzed, the application should automatically: Summarize the incident in plain English. Estimate severity using CVSS-inspired reasoning. Identify likely impacted services and APIs. Infer business impact (customers, compliance, reputation). Produce a prioritized engineering action plan. Generate implementation tasks with acceptance criteria. Recommend architectural improvements. Create executive and engineering summaries. Flag potential AI-related risks such as prompt injection, sensitive data leakage, insecure tool execution, excessive permissions, or hallucinated outputs if the incident involves LLM-powered systems. Explain the rationale behind every recommendation so users can review and challenge AI output rather than blindly accepting it.