It’s been a while since I felt giddy reading a specification. The release of Google’s Agent Payments Protocol (AP2) brought back memories of my payments days—waiting eagerly for Visa and Mastercard to publish updates on network tokenization, or when Android introduced Host Card Emulation. Here’s what stood out. What Is AP2? The Agent Payments Protocol (AP2) is Google’s ambitious attempt to create a cryptographic foundation for AI agent–initiated commerce. Released on September 16, 2025 with backing from 60+ organizations, it tackles the central trust problem: ...

The Confidential Computing Consortium defines confidential computing as “the protection of data in use by performing computation in a hardware-based, attested Trusted Execution Environment.” It’s a powerful idea. Instead of trusting operators or cloud providers, you trust hardware itself to keep your data protected even while it’s being processed. When WhatsApp recently announced new AI features like summarizing messages or helping draft replies, most people saw just another set of productivity tools. What went unnoticed is that behind these features sits one of the most ambitious privacy technologies ever deployed: confidential computing at internet scale. ...

Back when mobile payments were still in their infancy—long before Apple Pay became a household name—I found myself captivated by the possibilities of NFC technology. As a grad student at Dalhousie University, I started tinkering with NFC and co-founded a small startup called Alfred NFC, where we experimented with peer-to-peer transactions. That was my first real taste of what would become a lifelong fascination with secure technology ecosystems. As graduation approached and the reality set in that Alfred NFC wouldn’t become the next big thing, I faced a crossroads. I applied to countless Canadian companies and banks for NFC-related work—anything that would let me stay in this space I was so passionate about. But the callbacks never came. So I pivoted: I interviewed for several Java developer roles and received multiple offers. It was a safe, predictable path forward. ...

LLM Council Run Generated: 2026-04-22 21:37:11 Provider: azure_foundry Algorithm: consensus_only Council models: Mistral-Large-3, DeepSeek-R1, grok-4-20-reasoning Chairman: gpt-4.1 Prompt title: “Follow the Data: The One Question That Makes Security Architecture Simple” slug: “follow-the-data” date: 2026-04-22T21:30:00.000Z draft: true tags: [“Security Architecture”, “InfoSec”, “Confidential Computing”, “AI Security”, “Payments”] Every security architecture problem I have ever worked on — payments, confidential computing, AI agents — has come down to the same question: Where is the data, and what happens to it? ...

LLM Council Run Generated: 2026-04-11 11:00:20 Provider: azure_foundry Algorithm: consensus_only Council models: Mistral-Large-3, DeepSeek-R1, gpt-4.1-mini Chairman: gpt-4.1 Prompt title: “What WhatsApp’s Trail of Bits Audit Teaches Us About Real TEE Security” slug: “what-whatsapps-trail-of-bits-audit-teaches-us-about-real-tee-security” date: 2026-04-10T19:45:00.000Z draft: true tags: [“Confidential Computing”, “TEE”, “Attestation”, “WhatsApp”, “Trail of Bits”, “AI Security”, “SEV-SNP”] In January, I wrote about WhatsApp’s Private Processing as a milestone for confidential computing: the first time TEEs were deployed at truly global scale to protect AI inference for billions of users. ...