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https://lnkd.in/gHq8WTJV
You Should Know:
1. Understanding NIST CSF for Risk Calculation
The NIST Cybersecurity Framework (CSF) helps quantify residual risk by breaking it into five core functions:
– Identify (Asset Management, Risk Assessment)
– Protect (Access Control, Data Security)
– Detect (Anomalies & Events, Continuous Monitoring)
– Respond (Incident Response Planning)
– Recover (Backup & Restoration)
Example Risk Calculation:
Example: Calculating potential loss reduction TotalRisk = ThreatLikelihood Impact ResidualRisk = TotalRisk - (MitigationEffectiveness BudgetAllocated)
2. Key Linux Commands for Security Auditing
Use these to align with NIST CSF:
Identify: List all system users cut -d: -f1 /etc/passwd Protect: Check open ports sudo netstat -tuln Detect: Monitor logs in real-time sudo tail -f /var/log/syslog Respond: Isolate a compromised system sudo iptables -A INPUT -s <malicious_IP> -j DROP Recover: Verify backup integrity sha256sum /backups/important_data.tar.gz
3. Windows Security Commands
Identify: List all installed software Get-WmiObject -Class Win32_Product | Select-Object Name, Version Protect: Check firewall status Get-NetFirewallProfile | Select-Object Name, Enabled Detect: Scan for malware (Windows Defender) Start-MpScan -ScanType FullScan Respond: Force disconnect a suspicious session query session logoff <SessionID> Recover: Test backup restoration wbadmin start recovery -version:<BackupDate> -itemtype:file -items:<Path>
4. Automating Risk Reports with Python
import pandas as pd
Mock risk data
risk_data = {
"Threat": ["Phishing", "Ransomware", "Insider Threat"],
"Likelihood": [0.7, 0.5, 0.3],
"Impact ($M)": [10, 50, 20],
"Mitigation Cost ($M)": [2, 15, 5]
}
df = pd.DataFrame(risk_data)
df["Residual Risk ($M)"] = df["Likelihood"] df["Impact ($M)"] - (0.6 df["Mitigation Cost ($M)"])
print(df)
What Undercode Say:
The NIST CSF provides a structured way to translate cyber risks into financial terms. Use quantifiable metrics (e.g., ALE = SLE × ARO) to justify budgets. Combine technical checks (e.g., `auditd` on Linux, `Get-LocalUser` on Windows) with executive-friendly dashboards. Always simulate breaches (sudo nmap -sV <target_IP>) to validate defenses.
Expected Output:
A data-driven budget proposal with:
1. Risk heatmaps (e.g., generated via `matplotlib`).
- Mitigation ROI (e.g.,
(PreLoss - PostLoss) / ControlCost).
3. Compliance evidence (e.g., `lynis audit system`).
- Incident response playbooks (e.g.,
sudo systemctl isolate rescue.target).
For full details, visit: https://lnkd.in/gHq8WTJV
References:
Reported By: Simonehaddad The – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
