Practical, standards-based thinking on applied AI, security, modernization, data and the workforce, written for the leaders in companies and agencies who have to make the call. No product pitches.
Waiting for an approved cloud often leaves the most sensitive data with the least help. This paper lays out a practical architecture for running capable AI (transcription, translation and analysis) entirely inside your own boundary, including controlled and disconnected environments, with every answer tied to its source and a tamper-evident audit trail.
Federal agencies are required to adopt both Zero Trust and AI, and when the two run as separate programs they collide at the model. This paper shows how to treat AI as a governed resource inside a NIST SP 800-207 architecture, mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS. The same approach fits any company running models on its own network.
A fluent summary you cannot check is a confident guess. This paper describes evidence-grounded AI that turns messy, multilingual inputs into analysis a leader can act on and defend, and explains how grounding each claim in its source makes the result auditable.
Big-bang rewrites of critical systems tend to fail. This paper makes the case for incremental modernization: deliver value early, retire legacy systems one workload at a time, move to the cloud where it fits, and keep the off-ramps open.
Most analytics ends at a dashboard nobody acts on. This paper shows how to build analytics that change what people do: start from the decision, give it a named owner, tie it to a defined action, and measure the outcome instead of the chart.
On safety-critical work, onboarding proves that people attended. It does not prove they understood, and that gap falls hardest on new workers and on people who do not read English as a first language. This paper treats comprehension as a measurable control, checked in the worker's own language.
Buying AI is different from buying software. This paper shows program offices how to buy and govern AI they can defend, by turning the NIST AI Risk Management Framework into contract language, independent test criteria, drift monitoring and off-ramps.
Data readiness, more than the choice of model, decides whether AI works in practice. This paper sets out the measurable dimensions of data that is ready for AI, the failure modes that hide in it, and a continuous loop for making your data trustworthy and keeping it that way.
A model that worked in the demo has not yet proven anything. This paper covers test and evaluation for AI you can put into service: functional, robustness and adversarial testing, including red-teaming against MITRE ATLAS and the OWASP Top 10 for LLM Applications, run as a continuous lifecycle rather than a one-time gate.
Each paper is aimed at program managers, security leaders and operators in companies and agencies. The reasoning rests on current federal guidance and published technical standards, not vendor marketing, so you can check it yourself.
Read our articles for shorter takes, or tell us what you are working on and we will say plainly whether we can help.