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- TechniqueRiskProfile model: per-technique risk scoring (0-100) - 4-factor weighted scoring: detection_gap(35%) + threat_actors(30%) + osint(20%) + test_failures(15%) - Risk levels: critical(≥75) / high(≥50) / medium(≥25) / low(≥10) / info - Detailed scoring_breakdown (JSONB) + actionable recommendations per technique - Router /api/v1/risk: compute-all, compute-one, list, matrix, summary, recommendations, top - Alembic migration b038risk (raw SQL, idempotent) - QA script: 60+ tests across all endpoints Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
"""Phase 12: Risk Intelligence schemas."""
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from uuid import UUID
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from pydantic import BaseModel, ConfigDict
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VALID_RISK_LEVELS = ["critical", "high", "medium", "low", "info"]
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class TechniqueRiskProfileOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: UUID
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technique_id: UUID
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risk_score: float
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likelihood: float
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impact: float
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risk_level: str
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detection_gap: float
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threat_actor_count: int
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osint_signal_count: int
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test_fail_count: int
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test_total_count: int
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test_failure_rate: float
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confidence_level: float
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scoring_breakdown: Optional[Dict[str, Any]]
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recommendations: Optional[List[str]]
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computed_at: datetime
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is_stale: bool
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class RiskMatrixEntry(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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technique_id: UUID
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technique_name: Optional[str] = None
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technique_tid: Optional[str] = None # e.g. "T1059"
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risk_score: float
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likelihood: float
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impact: float
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risk_level: str
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detection_gap: float
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computed_at: datetime
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class RiskSummary(BaseModel):
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total_techniques: int
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scored_techniques: int
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stale_count: int
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by_level: Dict[str, int] # {"critical": 3, "high": 12, ...}
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avg_risk_score: float
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top_risks: List[RiskMatrixEntry]
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class RecommendationItem(BaseModel):
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technique_id: UUID
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technique_name: Optional[str] = None
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technique_tid: Optional[str] = None
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risk_level: str
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risk_score: float
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recommendations: List[str]
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priority: int # 1 = highest
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class ComputeResult(BaseModel):
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computed: int
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skipped: int
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errors: int
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duration_seconds: float
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