Perspectives
The methods I use and publish for governing AI and deciding which AI work to do, followed by my ongoing writing.
Frameworks
The Declared Behavior FrameworkAn AI governance audit methodology built on one premise: governance is the assurance that what happens is what was supposed to happen. Five layers, from declaration and conformance to containment, consequence, and evidence.
Continuously Enforceable AIHow to write AI policy so that every statement constraining a system can be checked by a machine, starting with a one-sentence enforceability test.
The Modality MethodChoosing AI use cases across machine learning, generative AI, and agentic AI: choose the work by value, the modality by fit, and the order by governance load.
AI Intake-to-Value Operating ModelA one-page decision standard for putting AI into production: six stages, weighted intake scoring, governance gates with required evidence, and a funding recommendation tied to measured results.
Break-in-Series Register, Release 1.3Adjudication rules for a public register of 202 events that changed long-running data series, so model owners and auditors can see how far back the data supports a claim.
The Break-in-Series Register itself is an interactive tool: open the register on auspica.ai.
Writing
My newsletter on technology adoption. Success isn’t about the tech, it’s about the approach, and the customer’s desired business outcomes come first.
Read the newsletter →The official Auspica blog on AI governance, risk, and compliance.
Read on Substack →Cloud strategy and architecture, and finding success while adapting to a mobile, data-driven world.
Visit the blog →Guest contributor on cloud topics.
Read the columns →Earlier talks
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