468 lines
15 KiB
Python
468 lines
15 KiB
Python
"""Scoring service — granular 0-100 scoring for techniques, tactics, actors, and org.
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Uses configurable weights from Settings to compute coverage scores with
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detailed breakdowns.
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"""
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from datetime import datetime, timedelta
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from typing import Optional
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from sqlalchemy import func
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from sqlalchemy.orm import Session
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from app.config import settings
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from app.models.technique import Technique
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from app.models.test import Test
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from app.models.detection_rule import DetectionRule
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from app.models.test_detection_result import TestDetectionResult
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from app.models.defensive_technique import DefensiveTechniqueMapping
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from app.models.threat_actor import ThreatActor, ThreatActorTechnique
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from app.models.enums import TestState, TestResult
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# ── Technique-level scoring ──────────────────────────────────────────
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def calculate_technique_score(technique: Technique, db: Session) -> dict:
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"""Calculate a 0-100 score for a technique with detailed breakdown.
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Weights (configurable via settings):
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- tests_validated: weight from SCORING_WEIGHT_TESTS
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- detection_rules: weight from SCORING_WEIGHT_DETECTION_RULES
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- d3fend_coverage: weight from SCORING_WEIGHT_D3FEND
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- freshness: weight from SCORING_WEIGHT_FRESHNESS
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- platform_diversity: weight from SCORING_WEIGHT_PLATFORM_DIVERSITY
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"""
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w_tests = settings.SCORING_WEIGHT_TESTS
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w_detection = settings.SCORING_WEIGHT_DETECTION_RULES
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w_d3fend = settings.SCORING_WEIGHT_D3FEND
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w_freshness = settings.SCORING_WEIGHT_FRESHNESS
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w_diversity = settings.SCORING_WEIGHT_PLATFORM_DIVERSITY
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breakdown = {}
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# ── 1. Tests validated with detection ──────────────────────────
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all_tests = (
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db.query(Test)
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.filter(Test.technique_id == technique.id)
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.all()
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)
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validated_tests = [t for t in all_tests if t.state == TestState.validated]
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detected_tests = [
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t for t in validated_tests
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if t.detection_result == TestResult.detected
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]
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if validated_tests:
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test_ratio = len(detected_tests) / len(validated_tests)
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test_score = round(test_ratio * w_tests, 1)
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else:
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test_ratio = 0
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test_score = 0
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breakdown["tests_validated"] = {
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"score": test_score,
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"max": w_tests,
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"detail": f"{len(detected_tests)}/{len(validated_tests)} tests detected"
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if validated_tests
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else "No validated tests",
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}
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# ── 2. Detection rules coverage ───────────────────────────────
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total_rules = (
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db.query(func.count(DetectionRule.id))
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.filter(
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DetectionRule.mitre_technique_id == technique.mitre_id,
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DetectionRule.is_active == True,
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)
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.scalar()
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) or 0
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triggered_rules = 0
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if total_rules > 0:
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triggered_rules = (
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db.query(func.count(TestDetectionResult.id))
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.join(
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DetectionRule,
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DetectionRule.id == TestDetectionResult.detection_rule_id,
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)
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.filter(
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DetectionRule.mitre_technique_id == technique.mitre_id,
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TestDetectionResult.triggered == True,
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)
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.scalar()
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) or 0
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detection_ratio = min(triggered_rules / total_rules, 1.0)
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detection_score = round(detection_ratio * w_detection, 1)
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else:
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detection_ratio = 0
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detection_score = 0
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breakdown["detection_rules"] = {
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"score": detection_score,
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"max": w_detection,
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"detail": f"{triggered_rules}/{total_rules} rules triggered"
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if total_rules > 0
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else "No detection rules available",
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}
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# ── 3. D3FEND coverage ────────────────────────────────────────
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total_countermeasures = (
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db.query(func.count(DefensiveTechniqueMapping.id))
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.filter(DefensiveTechniqueMapping.attack_technique_id == technique.id)
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.scalar()
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) or 0
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# Consider a countermeasure "verified" if we have validated tests
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# with detection for the technique (simplified heuristic)
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verified_countermeasures = 0
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if total_countermeasures > 0 and len(detected_tests) > 0:
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# Rough heuristic: each detected test validates ~1 countermeasure
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verified_countermeasures = min(len(detected_tests), total_countermeasures)
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d3fend_ratio = verified_countermeasures / total_countermeasures
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d3fend_score = round(d3fend_ratio * w_d3fend, 1)
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else:
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d3fend_ratio = 0
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d3fend_score = 0
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breakdown["d3fend_coverage"] = {
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"score": d3fend_score,
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"max": w_d3fend,
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"detail": f"{verified_countermeasures}/{total_countermeasures} countermeasures"
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if total_countermeasures > 0
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else "No D3FEND mappings",
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}
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# ── 4. Freshness ──────────────────────────────────────────────
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# Most recent validated test date
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most_recent_test = (
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db.query(func.max(Test.red_validated_at))
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.filter(
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Test.technique_id == technique.id,
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Test.state == TestState.validated,
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)
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.scalar()
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)
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now = datetime.utcnow()
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if most_recent_test:
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days_ago = (now - most_recent_test).days
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if days_ago < 90:
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freshness_pct = 1.0
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elif days_ago < 180:
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freshness_pct = 0.5
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else:
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freshness_pct = 0.0
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freshness_score = round(freshness_pct * w_freshness, 1)
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freshness_detail = f"Last test {days_ago} days ago"
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else:
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freshness_pct = 0
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freshness_score = 0
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freshness_detail = "No validated tests"
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breakdown["freshness"] = {
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"score": freshness_score,
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"max": w_freshness,
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"detail": freshness_detail,
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}
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# ── 5. Platform diversity ─────────────────────────────────────
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available_platforms = technique.platforms or []
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total_platforms = len(available_platforms) if available_platforms else 3 # default 3
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tested_platforms = set()
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for t in validated_tests:
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if t.platform:
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tested_platforms.add(t.platform.lower())
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if total_platforms > 0 and tested_platforms:
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diversity_ratio = min(len(tested_platforms) / total_platforms, 1.0)
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diversity_score = round(diversity_ratio * w_diversity, 1)
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else:
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diversity_ratio = 0
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diversity_score = 0
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breakdown["platform_diversity"] = {
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"score": diversity_score,
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"max": w_diversity,
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"detail": f"{len(tested_platforms)}/{total_platforms} platforms covered"
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if tested_platforms
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else "No platforms tested",
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}
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# ── Total ─────────────────────────────────────────────────────
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total = min(
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test_score + detection_score + d3fend_score + freshness_score + diversity_score,
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100,
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)
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return {
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"total_score": round(total, 1),
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"breakdown": breakdown,
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}
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# ── Tactic-level scoring ─────────────────────────────────────────────
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def calculate_tactic_score(tactic: str, db: Session) -> dict:
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"""Calculate average score for all techniques in a tactic."""
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techniques = (
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db.query(Technique)
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.filter(Technique.tactic.ilike(f"%{tactic}%"))
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.all()
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)
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if not techniques:
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return {
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"tactic": tactic,
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"average_score": 0,
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"techniques_count": 0,
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"techniques_scored": 0,
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}
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scores = []
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for tech in techniques:
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result = calculate_technique_score(tech, db)
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scores.append(result["total_score"])
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return {
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"tactic": tactic,
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"average_score": round(sum(scores) / len(scores), 1) if scores else 0,
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"techniques_count": len(techniques),
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"techniques_scored": len([s for s in scores if s > 0]),
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}
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# ── Threat actor scoring ─────────────────────────────────────────────
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def calculate_actor_coverage_score(actor_id: str, db: Session) -> dict:
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"""Calculate coverage score for a specific threat actor's techniques."""
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actor = db.query(ThreatActor).filter(ThreatActor.id == actor_id).first()
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if not actor:
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return {"total_score": 0, "techniques_count": 0, "techniques_covered": 0}
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# Get all techniques used by this actor
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actor_techniques = (
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db.query(ThreatActorTechnique)
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.filter(ThreatActorTechnique.threat_actor_id == actor.id)
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.all()
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)
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technique_ids = [at.technique_id for at in actor_techniques]
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if not technique_ids:
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return {
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"actor_id": str(actor.id),
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"actor_name": actor.name,
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"total_score": 0,
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"techniques_count": 0,
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"techniques_covered": 0,
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"techniques_detail": [],
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}
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techniques = (
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db.query(Technique)
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.filter(Technique.id.in_(technique_ids))
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.all()
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)
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scores = []
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details = []
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for tech in techniques:
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result = calculate_technique_score(tech, db)
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score = result["total_score"]
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scores.append(score)
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details.append({
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"mitre_id": tech.mitre_id,
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"name": tech.name,
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"score": score,
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"breakdown": result["breakdown"],
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})
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avg_score = round(sum(scores) / len(scores), 1) if scores else 0
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return {
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"actor_id": str(actor.id),
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"actor_name": actor.name,
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"total_score": avg_score,
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"techniques_count": len(techniques),
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"techniques_covered": len([s for s in scores if s > 50]),
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"techniques_detail": details,
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}
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# ── Organization-level scoring ────────────────────────────────────────
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def calculate_organization_score(db: Session) -> dict:
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"""Calculate the overall organization security score."""
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# All techniques
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all_techniques = db.query(Technique).all()
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total_count = len(all_techniques)
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if total_count == 0:
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return {
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"overall_score": 0,
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"total_coverage": 0,
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"critical_coverage": 0,
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"detection_maturity": 0,
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"response_readiness": 0,
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"techniques_evaluated": 0,
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"techniques_total": 0,
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}
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# Calculate scores for all techniques (with caching for performance)
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all_scores = []
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evaluated_count = 0
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for tech in all_techniques:
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result = calculate_technique_score(tech, db)
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score = result["total_score"]
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all_scores.append(score)
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if score > 0:
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evaluated_count += 1
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# Total coverage: average of all evaluated techniques
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evaluated_scores = [s for s in all_scores if s > 0]
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total_coverage = (
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round(sum(evaluated_scores) / len(evaluated_scores), 1)
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if evaluated_scores
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else 0
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)
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# Critical coverage: techniques with high-severity templates
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# (simplified: techniques that have tests are "critical")
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from app.models.test_template import TestTemplate
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critical_mitre_ids = set(
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row[0]
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for row in db.query(TestTemplate.mitre_technique_id)
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.filter(TestTemplate.severity.in_(["high", "critical"]))
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.distinct()
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.all()
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)
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critical_techniques = [
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t for t in all_techniques if t.mitre_id in critical_mitre_ids
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]
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if critical_techniques:
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critical_scores = []
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for tech in critical_techniques:
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result = calculate_technique_score(tech, db)
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critical_scores.append(result["total_score"])
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critical_coverage = round(sum(critical_scores) / len(critical_scores), 1)
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else:
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critical_coverage = 0
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# Detection maturity: based on detection rule coverage
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total_rules = (
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db.query(func.count(DetectionRule.id))
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.filter(DetectionRule.is_active == True)
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.scalar()
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) or 0
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triggered_total = (
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db.query(func.count(TestDetectionResult.id))
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.filter(TestDetectionResult.triggered == True)
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.scalar()
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) or 0
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detection_maturity = (
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round((triggered_total / total_rules) * 100, 1)
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if total_rules > 0
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else 0
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)
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detection_maturity = min(detection_maturity, 100)
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# Response readiness: based on remediation completion
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remediation_total = (
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db.query(func.count(Test.id))
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.filter(Test.remediation_status.isnot(None))
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.scalar()
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) or 0
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remediation_completed = (
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db.query(func.count(Test.id))
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.filter(Test.remediation_status == "completed")
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.scalar()
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) or 0
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response_readiness = (
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round((remediation_completed / remediation_total) * 100, 1)
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if remediation_total > 0
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else 0
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)
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# Overall score: weighted average of sub-scores
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overall = round(
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total_coverage * 0.4
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+ critical_coverage * 0.25
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+ detection_maturity * 0.2
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+ response_readiness * 0.15,
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1,
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)
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return {
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"overall_score": overall,
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"total_coverage": total_coverage,
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"critical_coverage": critical_coverage,
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"detection_maturity": detection_maturity,
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"response_readiness": response_readiness,
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"techniques_evaluated": evaluated_count,
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"techniques_total": total_count,
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}
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# ── Score history ────────────────────────────────────────────────────
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def get_score_history(db: Session, period: str = "90d") -> list:
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"""Get historical score snapshots.
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Since we don't have a dedicated history table, we approximate by
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computing scores based on test dates within time windows.
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Returns a list of weekly data points.
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"""
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from app.models.audit import AuditLog
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now = datetime.utcnow()
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if period == "30d":
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start = now - timedelta(days=30)
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elif period == "1y":
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start = now - timedelta(days=365)
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else: # 90d default
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start = now - timedelta(days=90)
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# Group validated tests by week
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weeks = []
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current = start
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while current < now:
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week_end = min(current + timedelta(days=7), now)
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# Count validated tests up to this week
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validated_up_to = (
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db.query(func.count(Test.id))
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.filter(
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Test.state == TestState.validated,
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Test.red_validated_at <= week_end,
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)
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.scalar()
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) or 0
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total_techniques = (
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db.query(func.count(Technique.id)).scalar()
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) or 1
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# Simple approximation: coverage percentage as score proxy
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score_approx = round((validated_up_to / total_techniques) * 100, 1)
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weeks.append({
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"date": current.strftime("%Y-%m-%d"),
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"score": min(score_approx, 100),
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"validated_tests": validated_up_to,
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})
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current = week_end
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return weeks
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