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Mastering secure AI on Google Cloud, a practical guide for enterprises
- Organizations face challenges in implementing secure AI solutions due to data privacy and security concerns.
- A secure AI platform relies on infrastructure, data, security, and responsible AI (RAI) as key cornerstones.
- Protecting data integrity and ensuring privacy compliance are crucial for secure AI applications.
- Responsible AI emphasizes fairness, explainability, privacy, and accountability in AI systems.
- Key security considerations on Vertex AI include private networking, service control perimeters, and Firewall Rules.
- Using Cloud Load Balancer and integrated security tools like reCAPTCHA Enterprise and Model Armor enhances AI application security.
- Chrome Enterprise Premium implements a Zero Trust model for secure remote access to AI applications.
- Regular security assessments, IAM role enforcement, and penetration testing are recommended for Vertex AI deployments.
- Securing the AI/ML workflow on Vertex AI involves steps for development, code security, training, model protection, deployment, monitoring, and governance.
- Confidential Computing on Vertex AI encrypts data, ensures code integrity, and restricts data access for sensitive workloads.
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