Phase 41.5 — SecurityOrchestrator: Unified Defense Pipeline & Threat Intelligence Deep-Dive

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Phase 41.5 — SecurityOrchestrator: Implementation Deep-Dive

Unified Defense-in-Depth Architecture

The SecurityOrchestrator integrates all Phase 41 components into a cohesive security intelligence platform that mirrors real-world security operations: Red Team (attack), Blue Team (defend), and Purple Team (continuous improvement).

Red Team Automation

Scheduled adversarial evaluation using the full attack suite from Phase 41.1:

async def run_red_team(model, test_data, policy):
    """Automated red team evaluation."""
    reports = []
    for attack_config in policy.required_attack_suites:
        report = await attacker.evaluate_robustness(
            model, test_data, attack_config
        )
        reports.append(report)
        
        # Generate threat events for low robustness
        if report.robust_accuracy < policy.alert_thresholds['high']:
            await create_threat_event(
                level='high',
                description=f"Model vulnerable to {attack_config.method}: "
                           f"{report.robust_accuracy:.1%} robust accuracy"
            )
    
    return reports

Blue Team Defense Chain

Multi-layered defense pipeline for production inference:

Input → [InputSanitizer Detection] → [InputSanitizer Sanitization]
      → [Model Inference] → [Output Verification] → Response

At each stage:
  - If threat detected → escalate based on policy
  - Log all detections for threat intelligence
  - Fallback to ensemble model if primary model compromised

Purple Team: Attack-Defense Feedback Loop

The most powerful feature — continuous adversarial improvement:

Iteration 1: Assess → Attack finds 45% robust accuracy
Iteration 2: Retrain with TRADES → robust accuracy rises to 52%
Iteration 3: Stronger attacks (AutoAttack) → finds 48% robust accuracy
Iteration 4: Retrain with TRADES+AWP → robust accuracy rises to 55%
Iteration 5: Final assessment → converged at 54% robust accuracy

Each iteration increases attack strength while improving defenses, converging toward a robust equilibrium.

Security Scoring

Composite security score (0-100):

Component	Weight	Metric
Empirical Robustness	40%	Min robust accuracy across required attacks
Certified Robustness	30%	Certified accuracy at policy ε
Detection Capability	30%	AUROC of adversarial detection

Score interpretation:

• 90-100: Excellent — exceeds all policy requirements
• 70-89: Good — meets most requirements, minor gaps
• 50-69: Fair — significant robustness gaps
• 30-49: Poor — vulnerable to standard attacks
• 0-29: Critical — immediate remediation required

Threat Intelligence

Pattern analysis over historical threat events:

• Attack frequency trends: Detect escalation patterns
• Vulnerability clustering: Identify systematically weak model components
• Transferability patterns: Track cross-model vulnerability correlation
• Drift detection: Alert when robustness degrades over model updates

Policy Compliance

Define organizational security standards:

policy:
  min_robust_accuracy: 0.40      # Must survive 40% of AutoAttack
  min_certified_radius: 0.25     # L2 certified radius
  max_detection_fpr: 0.05        # <5% false positive detection
  required_attacks: [autoattack, pgd, cw]
  retraining_trigger: -0.05      # 5% robustness drop triggers retrain

Compliance is continuously monitored and violations generate alerts.

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See issue #829 for full specification. Capstone of Phase 41 — Adversarial Robustness & Security Intelligence. Integrates #825 (41.1), #826 (41.2), #827 (41.3), #828 (41.4).