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🤖 [VISION - Not MVP] Organizational Intelligence Graph

Timeline: Year 2-3, after ML foundation Current Status: Concept only Warning: Do not implement during MVP phase

Concept

A knowledge graph that maps relationships between people, systems, risks, and compliance across the organization, enabling predictive insights and automated recommendations.

Vision

Neo4j-like graph where:
- Nodes: People, Systems, Risks, Controls, Policies, Evidence
- Edges: Owns, Manages, Mitigates, Depends-on, Reports-to
- Properties: Risk scores, Compliance status, Timestamps

Evolution from MVP

MVP (Current)

  • Simple relational data
  • Static relationships
  • Manual connections
  • Limited insights

Vision (Future)

  • Dynamic knowledge graph
  • Auto-discovered relationships
  • Predictive analytics
  • Organizational intelligence

Graph Components

1. Entity Types (Nodes)

People:
  - Directors
  - Executives
  - Managers
  - Technical staff

Systems:
  - Applications
  - Infrastructure
  - Data stores
  - Integrations

Compliance:
  - Frameworks
  - Controls
  - Evidence
  - Assessments

Risks:
  - Cyber risks
  - Compliance risks
  - Operational risks
  - Strategic risks

2. Relationship Types (Edges)

  • Accountability: Who owns what
  • Dependency: What relies on what
  • Mitigation: What controls what risk
  • Communication: Who informs whom
  • Approval: Who approves what

3. Intelligence Capabilities

Automated Discovery

  • Infer relationships from behavior
  • Identify hidden dependencies
  • Discover shadow IT
  • Map informal networks

Predictive Analytics

  • "If Sarah leaves, these 5 systems are at risk"
  • "This change will impact 3 compliance controls"
  • "Budget cuts here increase risk by 40%"

Recommendation Engine

  • "Assign backup owner for critical system"
  • "These 3 people need security training"
  • "Consolidate these duplicate controls"

Use Cases

1. Succession Planning

Query: "What happens if CTO leaves?"
Result: Systems at risk, knowledge gaps, handover requirements

2. Impact Analysis

Query: "Impact of deprecating System X?"
Result: Affected controls, compliance gaps, user impacts

3. Risk Visualization

Query: "Show critical path to Essential Eight compliance"
Result: Interactive graph of dependencies and blockers

Technical Architecture

Graph Database Options

  • Neo4j: Market leader, expensive
  • Amazon Neptune: Managed, AWS lock-in
  • ArangoDB: Multi-model, complex
  • Custom PostgreSQL: Possible but limited

Integration Requirements

  • Real-time sync with core data
  • Graph query language (Cypher/Gremlin)
  • Visualization engine
  • ML pipeline integration

Implementation Complexity

Why Not MVP?

  1. Technical: Requires graph database expertise
  2. Data: Needs rich dataset to be valuable
  3. UX: Complex visualization requirements
  4. ROI: Unclear immediate value

Prerequisites

  • Stable data model
  • Rich activity data
  • ML capabilities
  • Graph database expertise

Business Value

Strategic Advantages

  • Unique market differentiator
  • Deep organizational insights
  • Predictive capabilities
  • "Intelligent" compliance

Revenue Impact

  • Premium tier feature
  • Consulting opportunities
  • Retention driver
  • Expansion catalyst

Resource Requirements

  • Team: Graph database engineer, Data scientist
  • Timeline: 9-12 months
  • Infrastructure: Graph database, ML pipeline
  • Budget: $200k+ annually

Success Metrics

  • Relationship accuracy: >90%
  • Prediction success: >80%
  • Query performance: <1s
  • User engagement: Daily active

Risks and Mitigation

Technical Risks

  • Graph complexity explosion
  • Performance degradation
  • Integration challenges

Business Risks

  • Over-engineering
  • User comprehension
  • Privacy concerns

Evolution Triggers

Implement when:

  • Core platform stable
  • 500+ active orgs
  • Clear use cases validated
  • Technical team scaled

Alternative Approaches

1. Simple Relationship Mapping

  • Use existing PostgreSQL
  • Basic visualization
  • Manual relationships
  • 80% value, 20% effort

2. Partner Integration

  • Integrate with existing tools
  • Lower build cost
  • Faster time to market
  • Less differentiation

Remember: This is our long-term vision for "accumulated intelligence as competitive moat" but requires significant investment and proven market demand.