White Papers
Deep guides written by the people who build this
Detailed papers on cloud, security, compliance, and AI — written by the engineers and security experts who actually implement these programs.
Featured paper
How to implement Zero Trust security in regulated industries — a practical guide
Most Zero Trust security programs fail — not because the approach is wrong, but because the implementation doesn't account for how regulated industries actually operate. This guide covers the design patterns, control sequencing, and compliance mapping that make Zero Trust work in financial services, healthcare, and government, where generic guidance falls short.
Paper details
Authors
Amara Osei, Principal Security Architect
James Whitfield, CTO
All white papers
What it really takes to avoid being locked into one cloud provider
Many multi-cloud strategies end up creating vendor lock-in in a different form. This paper defines what genuinely flexible cloud architecture requires — technically and organizationally.
Why most enterprise AI projects fail before the AI is ever built
Most AI projects stall before a model is trained. The root cause is almost always the data infrastructure, not the AI itself. This paper defines what your data foundation needs to look like for production AI to work.
How to meet industrial security standards without halting operations
The IEC 62443 standard provides the right framework for industrial cybersecurity. What it doesn't tell you is how to apply it to a factory or facility that cannot stop running while you implement it. This paper fills that gap.
What FedRAMP High authorization actually involves and how to get ready for it
FedRAMP High authorization takes 12–24 months and requires documentation most commercial providers have never produced. This paper walks through the process, common failure points, and how to start preparing.
How to consolidate your technology without the project taking twice as long as planned
Most technology consolidation projects run over time because the sequencing is wrong. This paper covers the dependency-mapping approach and phased migration method that keeps risk manageable and timelines realistic.
How to keep AI models working reliably after they go live
Getting an AI model to production is only half the challenge. Keeping it working correctly over time requires a specific set of operational systems — model versioning, data storage, deployment infrastructure, and drift detection. This paper covers all of them.
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