Turning AI governance principles into executable controls
A principle becomes operational only when it is translated into requirements, control owners, enforcement points, evidence and responses to failure.
Softlander blog perspectives published in March 2026.
A principle becomes operational only when it is translated into requirements, control owners, enforcement points, evidence and responses to failure.
A device in the field may be unreachable for months and in service for a decade. That constraint should change the architecture, not just the update policy.
"The system shall be secure against unauthorised access" cannot be implemented, reviewed or verified. Security requirements become useful when each one names an actor, a condition and an observable result.
An agent is not reliable if people avoid it, over-trust it or route around its controls. Adoption evidence belongs beside technical performance evidence.
Prompt injection becomes consequential when untrusted content can steer a model towards privileged tools. Reduce the blast radius by redesigning the workflow around trust and authority.
Model selection matters, but production behaviour emerges from an entire dependency graph. Review the system that turns model output into business effects.
Financial agents combine models, data, tools, people and third parties, so assurance has to follow the complete regulated workflow.
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Read the method, assumptions and questions to ask.