Thoughts and insights on technology, leadership, and software engineering.
Operationalizing governance at scale: moving from pilot purgatory to production deployment while maintaining control. Part 4 transforms NIST frameworks into sustainable operations that deliver consistent business value without collapsing under operational complexity.
Beyond trust theater: implementing metrics that actually matter for AI trustworthiness. Part 3 transforms measurement from technical performance dashboards to systematic evaluation of the seven NIST characteristics that determine whether AI systems are safe to deploy.
The real AI governance crisis isn't the models you've formally approved, it's the ones you don't know exist. Part 2 tackles the visibility gap that's creating compliance exposure and security risks.
Transforming the NIST AI Risk Management Framework from compliance theater to strategic enablement. Part 1 focuses on governance structures that accelerate AI adoption while managing real risks.
Exploring how AI integration is reshaping software development team structures and the evolution from traditional PRD workflows to AI-augmented collaboration patterns.
McKinsey's latest research reveals a striking gap between GRC aspiration and implementation reality, further making the case for top-down approaches to risk management.
A multi-dimensional framework for maintaining disaster recovery and business continuity plans through incremental reviews, addressing the gap between documentation and actual recovery capabilities.
Examining how traditional risk frameworks apply to emerging AI technologies, with a focus on agent-to-agent communication systems and multi-context planning.