Written by practitioners, not theorists. Every piece is grounded in production outcomes from regulated environments — not vendor whitepapers or hypothetical frameworks.
Azure AI Foundry pricing at scale will surprise most organisations. We switched to a hybrid routing architecture — managed APIs for latency-sensitive workloads, self-hosted GPU clusters for high-volume batch processing. Here is the exact methodology.
Most AI governance frameworks are written for technical teams. Boards have five specific concerns — and they are not the ones you think. This article maps each concern to a concrete control framework.
The UK AI Act creates new obligations for high-risk AI systems. For financial services, insurance, and healthcare organisations, this is not optional. A plain-language breakdown of what changes, what doesn't, and the 12 actions to take now.
Every enterprise AI deployment has hallucination risk. Most mitigation strategies are insufficient. This is the framework we built — grounded in token-level evidence tracing — that achieves 97% detection with under 2% false positives.
The binary choice between managed APIs and self-hosted models is a false one. The right architecture routes intelligently based on cost, latency, data residency, and compliance requirements. The economics are more favourable than most engineers assume.
Employees at regulated firms are using consumer AI tools with production data. Most compliance teams are aware of it. Almost none have quantified the exposure. Here is the assessment methodology and the governance controls that actually work.
Follow Dr. Sam Arora on LinkedIn for regular insights on enterprise AI governance, platform architecture, and cost optimisation for regulated industries.
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