The evolution of zero-trust for AI security is driving advancements in:
● Edge AI Security: Processing workloads closer to the source reduces attack surfaces.
● Federated Learning: Distributed AI training enhances data privacy while minimizing transfers.
● Quantum-Resistant Cryptography: Next-generation encryption protects AI workloads against future quantum threats.
In conclusion, AI workloads demand security beyond traditional defenses. The zero-trust model ensures strict access control, data protection, and threat detection. By adopting zero-trust, organizations reduce security risks, improve efficiency, and enhance compliance. As Srinivas Reddy Cheruku highlights, the future of AI security lies in adaptive, intelligent, and identity-driven architectures that evolve with emerging threats, ensuring resilience in cloud-based AI environments.
