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:
Which tool is best for file content searches for multiple endpoints?
Is IT Availability Management* still needed?
Any comments to elaborate will be appreciated!
* The process responsible for ensuring that IT services meet the current and future availability needs of the business in a cost effective and timely manner. (Hanna & Rance, 2011)
Yes, it remains as described in ITIL 480%
No, it is becoming less relevant20%
Yes, but needs a shift in focus
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Does anyone have experience in implementing master data and reference data systems in a multi-market stock exchange with an off the shelf MDM system? I'd be interested in understanding the extent to which you centralise the mastering of data in the MDM system versus relying on external systems to "Master" and then use MDM to version and make the data available to other systems.
Should I be worried about quantum-safe encryption today?
Yes79%
No20%
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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: