⚠️ Dashboard: Risk Assessment & Anomaly Detection System

## 📋 Issue Type
Feature - Data Visualization Dashboard

## 🎯 Objective
Implement comprehensive risk assessment and anomaly detection dashboard utilizing CIA platform's 45 risk rules to identify high-risk politicians, detect voting anomalies, monitor crisis resilience, and provide early warning indicators for democratic accountability using advanced D3.js and Chart.js visualizations.

## 📊 Current State
- No risk assessment visualizations
- CIA risk data available but not consumed (45 risk rules × 349 MPs)
- No anomaly detection displays
- No crisis monitoring dashboards

## 🚀 Desired State
Interactive dashboard featuring:
- **Risk Level Heat Map**: 45 rules × 349 MPs risk matrix (D3.js)
- **Anomaly Detection**: Statistical anomaly visualization with P90/P99 thresholds
- **Crisis Resilience Indicators**: Party and politician stability metrics
- **Risk Evolution Timeline**: Temporal risk trends (2020-2026)
- **Early Warning System**: Real-time risk alerts and threshold monitoring

## 📊 CIA Data Integration Context

**CIA Product(s)**: 
- Risk Assessment Dashboard (45 rules)
- Anomaly Detection System
- Crisis Resilience Monitoring
- Ethics Concerns Rankings

**Data Sources**:
```
Primary CSV Files:
Risk Assessment:
- distribution_politician_risk_levels.csv (risk level distribution)
- distribution_risk_by_party.csv (party-level risk)
- distribution_risk_score_buckets.csv (risk score ranges)
- distribution_ministry_risk_levels.csv (ministry risk)
- distribution_ministry_risk_quarterly.csv (quarterly trends)

Risk Evolution:
- distribution_risk_evolution_temporal.csv (temporal changes)
- percentile_risk_score_evolution.csv (P1-P99 risk percentiles)
- percentile_politician_risk_summary.csv (politician risk metrics)
- percentile_ministry_risk_evolution.csv (ministry risk trends)

Anomaly Detection:
- distribution_voting_anomaly_classification.csv (anomaly types)
- distribution_anomaly_by_party.csv (party anomalies)
- distribution_seasonal_anomaly_detection.csv (seasonal anomalies)
- percentile_voting_anomaly_detection.csv (anomaly percentiles)
- percentile_seasonal_anomaly_detection.csv (seasonal percentiles)

Crisis Resilience:
- distribution_crisis_resilience.csv (resilience scores)
- percentile_crisis_resilience_indicators.csv (resilience percentiles)

Ethics & Transparency:
- top10_ethics_concerns.csv (top 10 ethics issues)
- top10_electoral_risk.csv (MPs at risk)

Sample Size: 300-500 rows (risk/anomaly data)
Coverage: 349 MPs × 45 rules = 15,705 risk assessments
```

**Sample Data**: Reference https://github.com/Hack23/cia/tree/master/service.data.impl/sample-data/

**Methodology**: 
- 45 risk rules from DATA_ANALYSIS_INTOP_OSINT.md
- Statistical anomaly detection (P90/P99 thresholds)
- Crisis resilience scoring
- Percentile benchmarking for risk levels

**Implementation Notes**:
- Fetch CSV files via GitHub raw content API
- Calculate real-time risk scores (45 rules per MP)
- Detect anomalies using statistical thresholds
- Cache locally in `/data/cia/risk/`
- Update frequency: Daily (risk changes daily)

## 🔧 Implementation Approach

### 1. Data Integration
```javascript
async function loadRiskData() {
  const datasets = {
    politicianRisk: 'distribution_politician_risk_levels.csv',
    riskEvolution: 'percentile_risk_score_evolution.csv',
    anomalies: 'distribution_voting_anomaly_classification.csv',
    crisisResilience: 'distribution_crisis_resilience.csv',
    ethicsConcerns: 'top10_ethics_concerns.csv'
  };
  
  const data = await fetchMultipleCIADatasets(datasets);
  
  return {
    riskMatrix: buildRiskMatrix(data.politicianRisk), // 45 rules × 349 MPs
    anomalies: detectAnomalies(data.anomalies, {p90: 90, p99: 99}),
    resilience: analyzeCrisisResilience(data.crisisResilience),
    alerts: generateEarlyWarnings(data)
  };
}
```

### 2. D3.js Risk Heat Map
```javascript
// 45 risk rules × 349 MPs heat map
function renderRiskHeatMap(data) {
  const svg = d3.select('#riskHeatMap')
    .append('svg')
    .attr('width', width)
    .attr('height', height);
  
  // Y-axis = Politicians (349 MPs, sortable)
  // X-axis = Risk rules (45 rules)
  // Color = Risk level (green=low, red=critical)
  
  const cells = svg.selectAll('rect')
    .data(data)
    .enter()
    .append('rect')
    .attr('x', d => xScale(d.rule))
    .attr('y', d => yScale(d.politician))
    .attr('width', cellWidth)
    .attr('height', cellHeight)
    .attr('fill', d => riskColorScale(d.score))
    .attr('class', d => `risk-${d.level}`)
    .on('mouseover', showRiskTooltip)
    .on('click', showRiskDetails);
  
  // Add zoom and filter capabilities
  svg.call(d3.zoom().on('zoom', zoomed));
}
```

### 3. Anomaly Detection Visualization
```javascript
// Statistical anomaly detection with thresholds
function renderAnomalyDetection(data, thresholds) {
  // Scatter plot: time × deviation score
  const anomalies = data.filter(d => d.score > thresholds.p90);
  
  new Chart(ctx, {
    type: 'scatter',
    data: {
      datasets: [{
        label: 'Anomalies (P90+)',
        data: anomalies.map(a => ({
          x: new Date(a.date),
          y: a.score
        })),
        backgroundColor: anomalies.map(a => 
          a.score > thresholds.p99 ? '#d32f2f' : '#f57c00'
        ),
        pointRadius: 6
      }]
    },
    options: {
      scales: {
        x: { type: 'time', title: { display: true, text: 'Date' } },
        y: { title: { display: true, text: 'Deviation Score' } }
      },
      plugins: {
        annotation: {
          annotations: {
            p90: { type: 'line', yMin: thresholds.p90, yMax: thresholds.p90, borderColor: '#f57c00', label: { content: 'P90 Threshold' } },
            p99: { type: 'line', yMin: thresholds.p99, yMax: thresholds.p99, borderColor: '#d32f2f', label: { content: 'P99 Critical' } }
          }
        }
      }
    }
  });
}
```

### 4. HTML Structure
```html
<section id="risk-dashboard" class="dashboard-container">
  <h2>⚠️ Risk Assessment & Anomaly Detection</h2>
  
  <div class="alert-banner" id="earlyWarnings" role="alert">
    
  </div>
  
  <div class="dashboard-grid">
    <div class="chart-card full-width">
      <h3>Risk Level Heat Map (45 Rules × 349 MPs)</h3>
      <div class="controls">
        <label>
          <input type="checkbox" id="filterHighRisk" />
          Show only high-risk (≥6.0)
        </label>
        <select id="riskRuleFilter" aria-label="Filter by risk rule">
          <option value="">All Rules</option>
          
        </select>
      </div>
      <div id="riskHeatMap" role="img" aria-label="Risk assessment heat map"></div>
    </div>
    
    <div class="chart-card">
      <h3>Risk Distribution</h3>
      <canvas id="riskDistributionChart"></canvas>
    </div>
    
    <div class="chart-card">
      <h3>Anomaly Detection (P90/P99)</h3>
      <canvas id="anomalyDetectionChart"></canvas>
    </div>
    
    <div class="chart-card">
      <h3>Crisis Resilience Indicators</h3>
      <canvas id="crisisResilienceChart"></canvas>
    </div>
    
    <div class="chart-card">
      <h3>Risk Evolution Timeline (2020-2026)</h3>
      <canvas id="riskEvolutionChart"></canvas>
    </div>
    
    <div class="chart-card">
      <h3>Top 10 Ethics Concerns</h3>
      <ol id="ethicsConcernsList"></ol>
    </div>
    
    <div class="chart-card">
      <h3>Electoral Risk (MPs at Risk)</h3>
      <ol id="electoralRiskList"></ol>
    </div>
  </div>
</section>
```

### 5. Risk Level Classification
```javascript
const RISK_LEVELS = {
  CRITICAL: { min: 8.0, max: 10.0, color: '#d32f2f', label: 'Critical' },
  HIGH: { min: 6.0, max: 8.0, color: '#f57c00', label: 'High' },
  MEDIUM: { min: 4.0, max: 6.0, color: '#fbc02d', label: 'Medium' },
  LOW: { min: 0.0, max: 4.0, color: '#388e3c', label: 'Low' }
};

function classifyRiskLevel(score) {
  if (score >= 8.0) return RISK_LEVELS.CRITICAL;
  if (score >= 6.0) return RISK_LEVELS.HIGH;
  if (score >= 4.0) return RISK_LEVELS.MEDIUM;
  return RISK_LEVELS.LOW;
}
```

### 6. Early Warning System
```javascript
function generateEarlyWarnings(data) {
  const warnings = [];
  
  // High-risk politicians (≥8.0)
  const critical = data.filter(d => d.risk_score >= 8.0);
  if (critical.length > 0) {
    warnings.push({
      severity: 'critical',
      message: `${critical.length} MPs with critical risk level (≥8.0)`,
      details: critical
    });
  }
  
  // Anomaly spike (>P99 in last 7 days)
  const recentAnomalies = data.anomalies.filter(a => 
    a.score > data.thresholds.p99 && 
    isWithinDays(a.date, 7)
  );
  
  if (recentAnomalies.length > 5) {
    warnings.push({
      severity: 'high',
      message: `${recentAnomalies.length} critical anomalies detected (last 7 days)`,
      details: recentAnomalies
    });
  }
  
  return warnings;
}
```

### 7. Accessibility
- Heat map keyboard navigation (arrow keys)
- Alternative risk tables for screen readers
- ARIA live regions for alerts
- High contrast mode
- Focus indicators

## 🤖 Recommended Agent
**data-visualization-specialist** - Expert in D3.js heat maps, anomaly detection, and risk visualization

**Rationale**: Requires advanced D3.js heat map expertise (45×349 matrix), statistical anomaly detection knowledge, and understanding of risk assessment methodologies.

**Custom Instructions**:
```
- Use D3.js v7+ for risk heat map (45 rules × 349 MPs)
- Use Chart.js for anomaly scatter plot and time series
- Implement statistical anomaly detection (P90/P99)
- Use risk-based color coding (WCAG AA compliant)
- Add zoom/pan/filter to heat map
- Implement early warning alert system
- Ensure WCAG 2.1 AA accessibility
- Support all 14 languages
- Cache risk data locally
- Update daily (risk changes daily)
- No JavaScript frameworks (vanilla JS + D3/Chart.js)
- Add real-time threshold monitoring
```

## ✅ Acceptance Criteria

### Data Integration
- [x] CIA risk CSV files fetched and parsed
- [x] 45 risk rules × 349 MPs matrix built
- [x] Anomaly detection implemented (P90/P99)
- [x] Crisis resilience scores calculated
- [x] Local caching in `/data/cia/risk/`

### Risk Heat Map
- [x] D3.js heat map (45 rules × 349 MPs)
- [x] Risk-based color coding (green to red)
- [x] Interactive: click cell for details
- [x] Filter by risk level (≥6.0, ≥8.0)
- [x] Filter by specific risk rule
- [x] Zoom and pan functionality
- [x] Sort by politician name or risk score

### Anomaly Detection
- [x] Scatter plot with P90/P99 threshold lines
- [x] Critical anomalies highlighted (>P99)
- [x] Temporal trend visualization
- [x] Anomaly type classification
- [x] Filter by anomaly type

### Early Warning System
- [x] Real-time alert banner
- [x] Critical risk alerts (≥8.0)
- [x] Anomaly spike detection
- [x] Crisis resilience warnings
- [x] Alert severity levels (critical/high/medium)

### Quality
- [x] HTML validation passes
- [x] WCAG 2.1 AA compliance
- [x] Color-blind friendly palette
- [x] Responsive design (320px-1440px+)
- [x] Load time < 5 seconds (large heat map)
- [x] D3.js v7+ and Chart.js via CDN with SRI

### Risk Assessment
- [x] 45 risk rules documented
- [x] Risk scoring methodology explained
- [x] Percentile benchmarks (P50, P75, P90, P99)
- [x] Historical risk trends displayed

### Multi-Language
- [x] Risk rule names localized (14 languages)
- [x] Alert messages translated
- [x] Chart labels in current locale

### Documentation
- [x] 45 risk rules documented
- [x] Anomaly detection algorithm explained
- [x] Crisis resilience methodology
- [x] Update SECURITY_ARCHITECTURE.md with risk dashboard

## 📚 References
- **Repository**: https://github.com/Hack23/riksdagsmonitor
- **CIA Sample Data**: https://github.com/Hack23/cia/tree/master/service.data.impl/sample-data
- **CIA Risk Analysis**: DATA_ANALYSIS_INTOP_OSINT.md (45 risk rules)
- **CIA Business Product**: Risk dashboard specification
- **D3.js Heat Map**: https://d3-graph-gallery.com/heatmap.html
- **Statistical Anomaly Detection**: P90/P99 methodology
- **ISMS Policy**: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md
- **THREAT_MODEL.md**: https://github.com/Hack23/riksdagsmonitor/blob/main/THREAT_MODEL.md

## 🏷️ Labels
`type:feature`, `priority:critical`, `component:visualization`, `component:data-integration`, `agent:data-visualization-specialist`, `cia-data`, `dashboard`, `risk-assessment`, `anomaly-detection`, `security`, `early-warning`

## 🌐 Multi-Language Support
All 14 languages supported with localized risk terminology

## 🔐 Security & Compliance
- **GDPR**: Public risk data (no private information)
- **Transparency**: 45 risk rules publicly documented
- **HTTPS-only**: Secure data transmission
- **SRI**: D3.js and Chart.js with integrity hashes
- **ISMS**: Follows risk assessment and threat modeling standards
- **Democratic Accountability**: Systematic transparency focus

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⚠️ Dashboard: Risk Assessment & Anomaly Detection System #55

📋 Issue Type

🎯 Objective

📊 Current State

🚀 Desired State

📊 CIA Data Integration Context

🔧 Implementation Approach

1. Data Integration

2. D3.js Risk Heat Map

3. Anomaly Detection Visualization

4. HTML Structure

5. Risk Level Classification

6. Early Warning System

7. Accessibility

🤖 Recommended Agent

✅ Acceptance Criteria

Data Integration

Risk Heat Map

Anomaly Detection

Early Warning System

Quality

Risk Assessment

Multi-Language

Documentation

📚 References

🏷️ Labels

🌐 Multi-Language Support

🔐 Security & Compliance

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

⚠️ Dashboard: Risk Assessment & Anomaly Detection System #55

Description

📋 Issue Type

🎯 Objective

📊 Current State

🚀 Desired State

📊 CIA Data Integration Context

🔧 Implementation Approach

1. Data Integration

2. D3.js Risk Heat Map

3. Anomaly Detection Visualization

4. HTML Structure

5. Risk Level Classification

6. Early Warning System

7. Accessibility

🤖 Recommended Agent

✅ Acceptance Criteria

Data Integration

Risk Heat Map

Anomaly Detection

Early Warning System

Quality

Risk Assessment

Multi-Language

Documentation

📚 References

🏷️ Labels

🌐 Multi-Language Support

🔐 Security & Compliance

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions