CyberMemory AI — SOC Incident Response with Persistent Memory

"CyberMemory AI doesn't investigate every incident from scratch. It learns from previous incidents and uses organizational memory to provide better context for future investigations."

CyberMemory AI is an AI-powered cybersecurity incident response and SOC analyst platform that uses persistent memory powered by Hindsight to learn from previous security incidents and provide context-aware investigation and remediation recommendations for new alerts.

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🔄 The Core Learning Loop

flowchart LR
    A["🚨 Detect<br/>(Incoming Alert)"] --> B["🔍 Investigate<br/>(AI Triage)"]
    B --> C["🧠 Recall<br/>(Hindsight Memory)"]
    C --> D["⚡ Recommend<br/>(Grounded Playbook)"]
    D --> E["🛡️ Resolve<br/>(Containment Applied)"]
    E --> F["💾 Remember<br/>(Vector Knowledge Base)"]
    F -->|Enriches Next Alert| C

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Feedback Loop: Alert → Memory → Insight → Action → New Memory

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🌟 Key Capabilities

• Real-Time SOC Triage & Alert Correlation:
  • Live ingestion telemetry with severity filters (Critical, High, Medium, Low).
  • High information density designed specifically for SOC Tier 1/Tier 2 analysts and Incident Commanders.
• Hindsight Persistent Memory:
  • Multi-hop semantic vector search across previous incidents.
  • Matches historical cases with dynamic cosine similarity scores (e.g. 94% on #INC-2026-00317).
  • Direct Memory → Current Investigation contextual influence indicators.
• Cognitive AI Investigation Workspace:
  • AI Assessment & Confidence Scoring (e.g. 91% triage assurance).
  • 5 sequential, numbered investigation steps with guidance, CLI command snippets (PowerShell/Bash), and live evidence findings.
  • "How CyberMemory AI Used Past Experience" animated memory recall pipeline.
• Incident Resolution & Instant Memory Commitment:
  • Pre-populated root cause analysis and multi-remediation checklist.
  • One-click "Resolve & Store Memory" committing the experience directly into the Hindsight knowledge graph.
• Interactive Hindsight Memory Search & Timeline:
  • Natural language search over historical post-mortems (e.g. "credential compromise involving failed login attempts").
  • Visual timeline of organizational knowledge growth from baseline ingestion to autonomous correlation.
• Enterprise SOC Analytics:
  • 24-hour threat activity timeline.
  • Severity distribution donut chart.
  • Top root causes frequency breakdown.
  • Memory effectiveness metrics: 72% investigations assisted, 84% recommendations supported, 42m saved per incident.
• Floating AI Copilot ("Ask CyberMemory AI"):
  • Docked chatbot accessible across all screens citing past incidents and precedents.
• Interactive 11-Step Guided Demo Tour:
  • Pinned walkthrough bar enabling evaluators, judges, and teams to step through the complete incident response story effortlessly.

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🏗️ Architecture & Service Layer

src/
├── types/
│   ├── alert.ts             # Alert telemetry, severity, MITRE tactics
│   ├── incident.ts          # Incident investigation & resolution payloads
│   ├── memory.ts            # Hindsight memory entry & similarity metrics
│   └── analytics.ts         # SOC telemetry & effectiveness KPIs
├── services/
│   ├── alertService.ts      # Alert catalog, filtering, status updates
│   ├── memoryService.ts     # Hindsight persistent memory store & vector search
│   ├── investigationService.ts # Triage tracking, step completion toggles
│   └── aiService.ts         # AI triage, confidence assessment, Copilot chat
├── context/
│   └── SecurityContext.tsx  # Central state syncing alerts, memories, and tour
├── components/
│   ├── layout/              # Sidebar, Header, DemoFlowBanner
│   ├── common/              # GlassCard, SeverityBadge, StatusBadge, Toast
│   ├── dashboard/           # KpiStatsGrid, ThreatActivityChart, RecentAlertsTable
│   ├── investigation/       # AlertSummaryCard, AiPanel, MemorySection, RecallFlow, Modal
│   ├── copilot/             # FloatingCopilot
│   └── pages/               # Overview, Alerts, Investigation, History, Memory, Analytics, Settings
└── data/                    # Realistic cybersecurity sample data & baseline memories

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🚀 Getting Started

Prerequisites

• Node.js: v18.0.0 or higher (v20+ recommended)
• npm or pnpm

Installation

# Clone the repository
git clone https://github.com/vedhurach-debug/CyberMemory.git
cd CyberMemory

# Install dependencies
npm install

# Start local development server
npm run dev

Open http://localhost:5173 in your browser.

Production Build

npm run build
npm run preview

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📋 The 11-Step Demo Workflow

1. Step 1: Overview Dashboard — Inspect live threat activity, active alerts, and Hindsight Memory metrics.
2. Step 2: Select Alert — Select Multiple Failed Login Attempts on DC-01.
3. Step 3: Investigation Workspace — Review technical indicators (185.73.22.41, admin@company.local, 27 attempts).
4. Step 4: AI Analysis — Observe the automated assessment and 91% confidence score.
5. Step 5: Hindsight Memory Matches — Review 5 matching historical incidents (94% match on #INC-2026-00317).
6. Step 6: AI-Generated Steps — Review the 5 recommended operational actions.
7. Step 7: Follow Steps — Check off completed investigation actions and inspect terminal queries.
8. Step 8: Open Resolution Workflow — Click Mark Resolved.
9. Step 9: Commit Experience — Specify root cause, verify applied remediations, ensure "Save to Hindsight" is checked.
10. Step 10: Confirmation — Verify the success state: "Memory successfully stored in Hindsight".
11. Step 11: Return to Memory — Navigate to the Hindsight Memory page to see the newly minted memory node ready for future alerts!

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🔒 Security & Privacy Notice

All data, hostnames (DC-01, WS-1042), IP addresses (185.73.22.41), and user accounts (admin@company.local) included in this prototype are realistic cybersecurity demo data. No real personal information or production corporate assets are included.

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📄 License

Licensed under the Apache License, Version 2.0.