Enterprises are fast embracing the power of Generative AI. Did you started implementing Machine Learning Model protection / ML Vulnerability scanning / LLM Protection tools yet? Do you use any alternate frameworks for protecting AI and ML in your organization?
Network and Security Architect team lead in Finance (non-banking)2 years ago
Protecting AI (Artificial Intelligence) and ML (Machine Learning) systems from cyber attacks is crucial, as these systems often deal with sensitive data and make critical decisions. At very high level - Please see below;
Data Security:Encryption,Access Controls and Anonymization and Masking Model Security: Model Encryption, Regular Model Audits, Model Watermarking Adversarial Attacks: Robust Model Training, Input Validation, Monitoring and Anomaly Detection: Behavioral Analysis: Monitor the behavior of AI systems to detect anomalies that may indicate a cyber attack. Real-time Monitoring: Implement real-time monitoring to promptly identify and respond to security incidents. Secure Deployment: Container Security,Secure APIs Update and Patching:Regular Updates, Vulnerability Scanning Human Factor:Training and Awareness User Authentication: Implement multi-factor authentication for users with access to AI and ML systems. Privacy Considerations: Privacy by Design Data Minimization: Collect and store only the data necessary for the intended purpose. Incident Response: Regulatory Compliance:
Protecting AI (Artificial Intelligence) and ML (Machine Learning) systems from cyber attacks is crucial, as these systems often deal with sensitive data and make critical decisions. At very high level - Please see below;
Data Security:Encryption,Access Controls and Anonymization and Masking
Model Security: Model Encryption, Regular Model Audits, Model Watermarking
Adversarial Attacks: Robust Model Training, Input Validation, Monitoring and Anomaly Detection:
Behavioral Analysis: Monitor the behavior of AI systems to detect anomalies that may indicate a cyber attack.
Real-time Monitoring: Implement real-time monitoring to promptly identify and respond to security incidents.
Secure Deployment: Container Security,Secure APIs
Update and Patching:Regular Updates, Vulnerability Scanning
Human Factor:Training and Awareness
User Authentication: Implement multi-factor authentication for users with access to AI and ML systems.
Privacy Considerations: Privacy by Design
Data Minimization: Collect and store only the data necessary for the intended purpose.
Incident Response:
Regulatory Compliance: