How boards should be measuring AI risk
A practical reporting model boards can use to govern AI without pretending to be technical.
Insights
Editorial perspective on enterprise AI, AI security, identity, governance, and the operating practices required to scale AI safely.
Now publishing
We are migrating selected MXP analysis, frameworks, and field notes from internal advisory work to this hub. New insights will be published here in the cadence they deserve — not on a content calendar.
A practical reporting model boards can use to govern AI without pretending to be technical.
Working through realistic enterprise threat scenarios — and the design patterns that actually defend against them.
A deep look at the model gateway, retrieval, agent runtime, identity, and audit as a single control surface.
Why traditional IAM falls short for AI agents, and what to put in place instead.
A pragmatic 12–24 month investment sequence that boards and operators both believe in.
The patterns that produce real value from AI agents, and the patterns that quietly produce risk.
In the meantime
Twenty chapters on how to scale advanced AI inside the enterprise — securely. Written for executive teams, not for marketers.