Proven outcomes – Documented success stories and measurable KPIs36%
Implementation confidence – Detailed plan, risk mitigation, and resource readiness47%
Total cost – Clear TCO, price protections, and exit terms39%
Innovation & future readiness – Ability to scale, adapt, and support emerging needs13%
Vendor relationship strength – Cultural fit, governance model, and executive commitment13%
Redefining business goals21%
Optimizing current business goals69%
Setting additional business goals8%
Other
No selling.
No recruiting.
No self promotion.
Rules of EngagementFAQsPrivacy
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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: