Control Effectiveness = (Attacks Prevented + Attacks Detected) / Total Attacks Simulated * 100
Example:
Total simulations: 500
Prevented (blocked): 350
Detected (alerted): 100
Missed (no action): 50
Prevention Rate: 350/500 = 70%
Detection Rate: 100/500 = 20%
Overall Score: 450/500 = 90%
Gap Rate: 50/500 = 10%
Architecture:
Management Console (Cloud SaaS):
- Central orchestration and reporting
- Attack scenario library management
- MITRE ATT&CK mapping dashboard
Simulation Agents:
- Attacker Agent: Simulates threat actor behavior
- Target Agent: Receives simulated attacks
- Network Agent: Tests network-level controls
Deploy agents across zones:
- Corporate network (workstations)
- DMZ (web servers)
- Data center (critical servers)
- Cloud environments (AWS/Azure/GCP)
- Remote/VPN segment
scenario:
name: "APT29 (Cozy Bear) Full Kill Chain"
threat_group: APT29
mitre_attack_techniques:
- T1566.001
- T1059.001
- T1547.001
- T1003.001
- T1021.002
- T1071.001
- T1048.003
phases:
- name: "Initial Access"
actions:
- deliver_phishing_payload:
type: office_macro
target: email_gateway
variants: [docm, xlsm, ppam]
- name: "Execution & Persistence"
actions:
- execute_powershell:
encoded: true
amsi_bypass: true
- create_scheduled_task:
technique: T1053.005
- name: "Credential Access"
actions:
- dump_lsass:
method: [procdump, comsvcs, nanodump]
- name: "Lateral Movement"
actions:
- psexec_lateral:
target: internal_server
- wmi_lateral:
target: file_server
- name: "Exfiltration"
actions:
- dns_exfiltration:
data_size: 10MB
encoding: base64
def map_bas_results_to_controls(simulation_results):
"""Map BAS results to security control effectiveness."""
control_scores = {}
control_mapping = {
"email_gateway": ["T1566.001", "T1566.002", "T1566.003"],
"edr": ["T1059.001", "T1003.001", "T1055", "T1547.001"],
"ngfw": ["T1071.001", "T1071.004", "T1048"],
"siem": ["T1053.005", "T1021.002", "T1087"],
"dlp": ["T1048.003", "T1567", "T1041"],
"ndr": ["T1071", "T1021", "T1040"],
}
for control, techniques in control_mapping.items():
relevant = [r for r in simulation_results
if r["technique_id"] in techniques]
if not relevant:
continue
prevented = sum(1 for r in relevant if r["result"] == "prevented")
detected = sum(1 for r in relevant if r["result"] == "detected")
missed = sum(1 for r in relevant if r["result"] == "missed")
total = len(relevant)
control_scores[control] = {
"total_tests": total,
"prevented": prevented,
"detected": detected,
"missed": missed,
"prevention_rate": round(prevented / total * 100, 1),
"detection_rate": round(detected / total * 100, 1),
"effectiveness": round((prevented + detected) / total * 100, 1),
}
return control_scores
Validation Schedule:
Daily:
- Malware delivery simulation (email gateway test)
- C2 communication simulation (firewall/proxy test)
- Known ransomware behavior simulation (EDR test)
Weekly:
- Full kill chain simulation (APT scenario)
- Lateral movement simulation (network segmentation test)
- Data exfiltration simulation (DLP test)
Monthly:
- Full MITRE ATT&CK coverage assessment
- New threat group TTP simulation
- Regression testing after security control changes
On-Demand:
- After firewall rule changes
- After EDR policy updates
- After new threat intelligence (zero-day response)