Critical AI Security Guidelines by SANS Institute and Cybersecurity Experts

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A vast number of cybersecurity professionals recently contributed to the SANS Institute draft of Critical AI Security Guidelines. The following SANS experts and industry professionals explore the essential areas of securing Generative AI, which include:

  • Access Controls
  • Data Protection
  • Deployment Strategies
  • Inference Security
  • Monitoring
  • Governance, Risk, Compliance (GRC)

You Should Know:

1. Access Controls for AI Systems

Securing AI models requires strict access management. Below are key Linux and Windows commands to enforce access controls:

Linux:

 Restrict directory permissions 
chmod 750 /path/to/ai_model 
chown root:ai_team /path/to/ai_model

Use ACLs for granular control 
setfacl -m u:user:r-x /path/to/ai_model 

Windows (PowerShell):

 Set directory permissions 
icacls "C:\AI_Models" /grant "AD\AI_Team:(OI)(CI)(RX)" 

2. Data Protection in AI Workflows

Encrypt AI training data to prevent leaks:

Linux (GPG Encryption):

gpg --encrypt --recipient "[email protected]" training_data.csv 

Windows (BitLocker):

Enable-BitLocker -MountPoint "D:" -EncryptionMethod Aes256 

3. Monitoring AI Models for Anomalies

Use logging and SIEM tools to detect suspicious AI behavior:

Linux (Audit Logs):

 Monitor AI model access 
auditctl -w /var/lib/ai_models -p rwxa -k ai_access 

Windows (Event Logs):

Get-WinEvent -LogName "Application" | Where-Object { $_.Message -like "AI Model" } 

4. Securing AI Deployments

Isolate AI containers using Docker security:

docker run --security-opt no-new-privileges -u nobody ai_container 

5. GRC Automation for AI Compliance

Automate compliance checks with OpenSCAP:

oscap xccdf eval --profile pci-dss /usr/share/xml/scap/ssg/content/ssg-linux-ds.xml 

What Undercode Say:

AI security is a growing concern, and these guidelines highlight the need for zero-trust policies, encryption, and strict monitoring. Implementing Linux hardening, Windows security policies, and automated compliance checks ensures AI systems remain resilient against attacks.

Expected Output:

  • AI Security Best Practices
  • Linux/Windows Hardening Commands
  • Automated Compliance Scripts
  • Secure Deployment Strategies

References:

Reported By: Mthomasson Sans – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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