September 9, 2026

Why the Enclave Is the Wrong Shape for an Agentic Workload

Autonomous telecom networks change the shape of the security problem. Agentic workloads move across models, tools, data stores, agents and infrastructure, carrying sensitive network intelligence with them. Paolo Campoli examines why protecting the infrastructure is no longer enough when the workload itself is designed to move.
September 1, 2026

All Roads Lead to…AI?

Rome's roads made the empire powerful by reducing the friction of distance, but the same infrastructure could be used by forces moving against it. Enterprise AI presents a modern version of that trade-off, concentrating in one environment the knowledge that was once scattered safely across separate systems.
August 18, 2026

Stop Trading Proprietary Knowledge for AI Capabilities

Enterprises are making an increasingly unbalanced tradeoff with AI: proprietary knowledge is exposed to more infrastructure in exchange for capabilities that are becoming essential to compete. As AI concentrates valuable information into models and active computing environments, the potential cost of that tradeoff grows, demanding a fundamentally different security architecture.
July 16, 2026

Nadella Named the Reverse Information Paradox. He Missed Its Cause.

Nadella calls it the Reverse Information Paradox: enterprises pay for AI twice, once in models and infrastructure, again in the proprietary knowledge they hand over each time they use it. But the root problem isn't economic, it's architectural: AI turns decades of security thinking on its head, forcing knowledge to become visible before it can be used.
June 25, 2026

Don’t Fence In What Is Designed to Move

For thirty years, perimeter security strictly limited data access in the enterprise. Agentic AI inverts this requirement entirely: agents need broad data visibility to function effectively, yet their distributed workflows span inference pipelines, retrieval systems, persistent memory, and inter-agent communication. Continuous data-level protection across all surfaces remains essential.
May 28, 2026

A New Side-Channel Attack Can Reconstruct AI Models Through Walls

Researchers at KAIST demonstrated that AI model architectures can be reconstructed remotely through electromagnetic emissions from GPUs. ModelSpy exposes a growing gap in AI infrastructure security: confidential computing protects software boundaries, but not physical leakage. As sovereign AI and confidential AI accelerate, encrypted execution may become the next critical layer of AI defense.
April 9, 2026

What the LiteLLM Incident Revealed About AI Pipeline Risk

The LiteLLM incident was a reminder that modern AI breaches often begin with the software supply chain. Once credentials are exposed, the real problem becomes control over access, environments, and downstream systems. That is where the blast radius grows, even when core assets remain protected.
March 19, 2026

DataKrypto and Google Cloud: Making the Future of Confidential AI a Reality Today

Today, we anounced a significant milestone in DataKrypto’s mission to deliver the cryptographic foundation for Confidential AI: our completion of the Google Cloud ISV Startup Springboard Program and the availability of our flagship product, FHEnom for AI™, on the Google Cloud Marketplace.
March 13, 2026

McKinsey’s Breach Reveals a Design Flaw in Every Modern AI System

On March 9, 2026, CodeWall.ai published a consequential AI security disclosure: “How We Hacked McKinsey’s AI Platform.” Codewall’s autonomous offensive agent — with no credentials, no insider knowledge, and no human guidance — achieved full read and write access to the production database behind Lilli, McKinsey’s internal AI platform used by more than 43,000 employees.