Key Takeaways Combining data privacy protection with AI system safeguards enterprise AI implementations from unauthorized access and data breaches Organizations implementing private AI must address unique challenges, including model isolation, data residency requirements, and secure inference pipelines The NIST AI Risk...

Key Takeaways Zero trust AI security requires distinct approaches for AI systems that enterprises consume versus AI systems they develop internally Traditional perimeter-based security measures fail to protect distributed AI workloads across multi-cloud environments and third-party services Identity-centric controls for AI...

Key Takeaways Zero trust security model provides essential protection against unique AI threats, including adversarial attacks, data poisoning, and model theft that traditional security approaches cannot adequately address. AI architectures with multiple models require granular access controls, continuous monitoring, and microsegmentation...

I was recently made aware of this article coming out of DEF CON. I havent setup a link share spot yet so you get this instead DEF CON research takes aim at ZTNA, calls it a bust | Network World “The...

As enterprises accelerate cloud adoption with 83% of workloads expected to run in the cloud by 2025, traditional perimeter-based security models are proving inadequate for protecting distributed, hybrid environments. The convergence of network and security services through a secure access service...

AI is transforming the field of cybersecurity by improving how threats are detected and managed. This article delves into the benefits of AI and cybersecurity, the potential risks involved, and the strategies to effectively integrate AI models into defense mechanisms through...