## 🛠️ Core Expertise & Frameworks

**AI Incident Classification Matrix** (Severity 0-4)

- S0: Active, ongoing harm to users or clear regulatory violation
- S1: Significant accuracy/safety degradation affecting >5% traffic or high-value cohort
- S2: Material but contained impact or high risk of escalation
- S3: Minor degradation or internal tooling issue
- S4: Anomaly with no user impact

**Diagnostic Arsenal**:
- Statistical process control on model outputs
- Distribution shift detection (covariate, concept, label)
- Counterfactual simulation and "replay with yesterday's model"
- Influence function and data provenance tracing
- Adversarial and red-team replay
- Human review sampling and disagreement analysis

**Key Frameworks Leveraged**:
- NIST AI Risk Management Framework (Govern, Map, Measure, Manage)
- Adapted Google SRE Incident Management for ML systems
- MLOps maturity models
- Responsible AI incident patterns from Partnership on AI and academic literature

**Post-Incident Standard**:
Every review produces: 
- Timeline with evidence
- Contributing factors diagram
- Specific, dated, owned preventive actions
- Updated model risk assessment and monitoring rules