I spent the last month intentionally poking at a selection of low-cost smart home hubs to answer a question that keeps me up at night: if a device’s supply chain is compromised — think counterfeit firmware shipped from a factory or an attacker flipping code before it reaches your home — which budget hubs can still protect you, and which fail catastrophically?This isn’t a lab thought...
Data poisoning is one of those threats that feels both abstract and terrifying: an attacker doesn't need access to your...
I’ve been in procurement meetings where the promise of “best-in-class” AI models meets the cold reality of legal...
I’ve been experimenting with on-device AI on phones for years, chasing a simple question: when does quantized...
Category: CybersecurityWhen I’m asked to audit an AI model for compliance and data leakage risk, I start with one...
I buy cheap smart displays to test what manufacturers cut to hit a low price. Over time I’ve learned that...
I’ve spent the last few years wrestling with the tension every ML team feels: how to move fast and still be secure....
I’ve been experimenting with on-device AI on phones for years, chasing a simple question: when does quantized inference actually make sense on a smartphone? It’s tempting to treat quantization as a silver bullet — smaller models, faster runtime, less battery drain — but reality is messier....
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Category: CybersecurityWhen I’m asked to audit an AI model for compliance and data leakage risk, I start with one guiding question: Where did the data come from, what happened to it, and who can access the model or its outputs? Tracing data lineage for AI models is rarely straightforward —...
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I buy cheap smart displays to test what manufacturers cut to hit a low price. Over time I’ve learned that “budget” often means compromises you can’t see at first glance—hidden microphones, weak privacy controls, and network behaviour that quietly farms data. If you’re thinking of buying...
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I’ve spent the last few years wrestling with the tension every ML team feels: how to move fast and still be secure. Machine learning pipelines are messy by design — they touch data, notebooks, scripts, containers, model artifacts, feature stores and serving infra. Add the need for...
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I want to walk you through a pattern I’ve used several times in the field: rolling out incremental on-device model updates using delta-only packages so users get improvements with minimal bandwidth, storage and downtime. I’ve shipped mobile and embedded updates for everything from speech models...
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I’ve been wrestling with the same question a lot lately: if you’re working with regulated data, is it really cheaper (and safer) to use an off-the-shelf model like Mistral via an API, or to bring a model in-house and fine-tune it locally? The short, honest answer I keep landing on is: it...
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I recently led a migration from password-based authentication to passkeys for a mid-size SaaS product using Okta as our identity provider. The project forced us to balance security wins with real-world usability: passkeys dramatically reduce phishing and password reuse risk, but they also introduce...
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Keeping an LLM on-device up to date in a field app is deceptively hard. Over-the-air model updates, prompt tuning patches, and delta compression can change user experience, storage, and — critically — battery life. I've worked through many update flows, from enterprise fleet deployments on...
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I’ve been benchmarking vector databases for months now, because keeping semantic search realtime at scale is one of those problems that sounds simple until you try to run it on real traffic. In this article I walk through the tests I ran comparing Qdrant, Milvus, and Pinecone focusing on three...
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I remember the first time my team relied on an AI system in production: a recommendation model that quietly drifted for weeks until business metrics diverged and users complained. We had alerts—sort of—but they told us symptoms, not causes. That experience shaped how I evaluate AI observability...
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