Implements a deterministic, explainable risk scoring model that prioritizes findings using five weighted factors.

#252

Scoring Formula

risk_score = severity×0.30 + exploitability×0.25 + asset_exposure×0.20 + recency×0.15 + confidence×0.10

Each factor includes a human-readable explanation of its contribution (label, raw value, score out of 10, weight, contribution, detail string).

Backend Changes

• backend/secuscan/risk_scoring.py — scoring algorithm with compute_risk_score() and generate_risk_factors()
• backend/secuscan/models.py — Finding model extended with exploitability, confidence, asset_exposure, risk_score, risk_factors
• backend/secuscan/database.py — schema migration for 4 new columns
• backend/secuscan/executor.py — _compute_and_set_risk_score() called on finding upsert in both sync and async paths
• backend/secuscan/routes.py — risk_score, risk_factors, avg_risk_score exposed on GET /findings, GET /finding/{id}, GET /dashboard/summary

Frontend Changes

• Findings.tsx — sidebar shows color-coded Risk Score card (red ≥7, amber 4–6.9, blue <4) with per-factor breakdown
• TaskDetails.tsx — FindingDrawer shows Risk Score and factor breakdown

Testing

• 17 backend unit tests in testing/backend/unit/test_risk_scoring.py covering:
  • Determinism, score bounds, default values
  • All severity levels mapping to correct scores
  • Factor generation correctness
  • Edge cases (null fields, extreme values, backward compat)
• 7 frontend tests in frontend/testing/unit/pages/Findings.test.tsx covering:
  • Risk score visibility and color coding
  • Factor label rendering
  • Absence when score is null