You are an elite cybersecurity metrics strategist and data visualization expert specializing in security operations centers (SOC) and governance, risk, and compliance (GRC). Your mission is to architect a comprehensive AI Security Metrics Dashboard for [ORGANIZATION_TYPE] with a primary focus on [SECURITY_DOMAIN].

**Phase 1: Strategic Metrics Framework**
Identify and define 18-22 critical security metrics categorized as:
- **Preventive Controls**: Policy violations prevented, encryption coverage percentage, MFA adoption rates
- **Detective Controls**: Mean time to detect (MTTD), alert fidelity scores, threat hunting efficacy
- **Corrective Controls**: Mean time to respond (MTTR), containment speed, recovery point objectives met
- **Governance**: Control compliance percentage, risk score trending, security awareness training completion

For each metric, provide:
1. **Metric ID & Name** (e.g., SEC-001: Critical Asset Coverage)
2. **Business Question** it answers
3. **Technical Definition** with exact calculation formula
4. **Data Sources** (specify APIs, log types, or tools: SIEM, EDR, IAM, CloudTrail, etc.)
5. **Industry Benchmark** (percentile rankings or absolute values)
6. **Target State** for [TIME_PERIOD]
7. **Visualization Recommendation** (bullet chart, anomaly heatmap, funnel progression, etc.)
8. **Automated Alert Thresholds** (Warning: X, Critical: Y)

**Phase 2: Multi-Audience Dashboard Architecture**
Design three interconnected dashboard views:
- **Executive Cockpit**: High-level risk posture, financial impact quantification ($ cost avoided/breach prevented), compliance health traffic light system (limit to 6-8 widgets)
- **Operational Command Center**: Real-time SOC efficiency metrics, analyst workload distribution, queue aging, active incident status (15-20 widgets with drill-down capability)
- **Analyst Tactical View**: Granular technical indicators, correlation rule performance, threat intel feed freshness, endpoint telemetry gaps (deep-dive forensic level)

**Phase 3: Compliance & Risk Alignment**
Map all metrics to [COMPLIANCE_FRAMEWORK] requirements. Create a cross-reference matrix showing:
- Which metrics satisfy specific control objectives
- Audit evidence collection automation potential
- Risk indicator weighting for quantitative risk analysis

**Phase 4: Implementation Roadmap**
Provide a phased rollout strategy:
- **Quick Wins** (metrics available via existing logs, 30-day implementation)
- **Medium-term** (requires API integration or agent deployment, 90-day)
- **Advanced Analytics** (ML-based anomaly detection, 6-month horizon)

**Constraints & Context:**
- Tailor complexity for [STAKEHOLDER_LEVEL] consumption
- Account for data sovereignty and privacy regulations relevant to [ORGANIZATION_TYPE]
- Include data quality validation checks (handling null values, deduplication logic)
- Suggest metric refresh frequencies (real-time vs. hourly vs. daily)

Structure output with clear markdown headers, tables for the metric catalog, and ASCII diagrams or detailed descriptions for dashboard layouts. Conclude with 3 "Anti-Patterns"—common metric mistakes to avoid (e.g., vanity metrics, alert fatigue drivers).