🤖 [VISION - Not MVP] ML-Driven Pattern Recognition¶

Timeline: Year 2, after 1000+ assessments Current Status: Concept only Warning: Do not implement during MVP phase

Concept¶

Machine learning system that identifies compliance patterns, risk correlations, and optimization opportunities across organizations.

Evolution from MVP¶

MVP Approach (Current)¶

Hardcoded risk patterns
Manual categorization
Static scoring rules
Template-based insights

Vision Approach (Future)¶

ML-discovered patterns
Auto-categorization
Dynamic risk scoring
Personalized insights

Pattern Recognition Capabilities¶

1. Risk Pattern Discovery¶

# Future capability
class RiskPatternML:
    def identify_patterns(self, org_data):
        # Discover correlations like:
        # "Companies without MFA have 3x higher incident rates"
        # "Organizations with recent funding often fail E8_G"

2. Question Intelligence¶

Predict which questions matter most
Skip questions based on prior answers
Identify question clusters
Optimize assessment flows

3. Compliance Prediction¶

Forecast compliance trajectory
Predict audit failures
Identify intervention points
Suggest proactive measures

4. Industry Benchmarking¶

Peer comparison models
Industry-specific risks
Size-based expectations
Maturity progression paths

Data Requirements¶

Minimum Viable Dataset¶

1000+ completed assessments
50+ organizations per industry
12+ months of historical data
Validated outcome data

Training Approach¶

Start with supervised learning on known patterns
Move to unsupervised discovery
Implement reinforcement learning for recommendations
Continuous model improvement

Technical Architecture¶

ML Pipeline¶

Data Collection → Feature Engineering → Model Training → Validation → Deployment
      ↓                    ↓                ↓              ↓            ↓
   Privacy             Eng Team          ML Team      Compliance    Product
   Review            Resources         Resources       Review      Integration

Technology Stack¶

Framework: TensorFlow/PyTorch
Pipeline: Kubeflow/MLflow
Serving: TensorFlow Serving
Monitoring: Weights & Biases

Privacy & Ethics¶

Critical Considerations¶

No PII in training data
Aggregated insights only
Explicit consent required
Right to opt-out
Transparent model decisions

Compliance Requirements¶

Privacy Act compliance
GDPR considerations
Industry regulations
Ethical AI principles

Business Value¶

For Customers¶

Faster assessments (50% reduction)
Better risk identification
Proactive recommendations
Industry insights

For GetCimple¶

Competitive differentiation
Premium tier justification
Reduced support burden
Network effects moat

Implementation Phases¶

Phase 1: Data Foundation (Month 1-3)¶

Implement comprehensive logging
Design feature schema
Build data pipeline
Privacy framework

Phase 2: Basic Models (Month 4-6)¶

Risk classification
Question routing
Simple predictions
A/B testing

Phase 3: Advanced Models (Month 7-12)¶

Pattern discovery
Complex predictions
Personalization
Full rollout

Success Metrics¶

Model accuracy: >85%
False positive rate: <10%
User trust score: >4.5/5
Time savings: >50%

Investment Requirements¶

Team: 2 ML engineers, 1 data engineer
Infrastructure: GPU compute, data warehouse
Timeline: 12 months
Budget: [Post-Series A]

Risk Factors¶

Insufficient data quality
Model bias concerns
Regulatory changes
User acceptance

Evolution Trigger¶

Implement when:

1000+ assessments completed
Clear patterns validated manually
ML team hired
Privacy framework approved

Note: This vision aligns with our philosophy of "accumulated intelligence as competitive moat" but requires significant data and resources not available during MVP.