Over the past weeks, I’ve been sharing a series of reflections on AI governance, decision accountability, and the role of human judgment in AI-enabled and cyber-physical systems.
These posts were never meant to present a complete solution.
They were intended to frame the problem space, clarify distinctions, and surface the governance challenges that emerge when AI systems operate in high-risk and security-critical environments.
This work is now progressing into a more structured form.
The concepts discussed so far are being consolidated into a coherent body of work, where governance is treated as an operational capability grounded in accountability, risk traceability, and evidence-based assurance — rather than as static documentation or post-hoc compliance.
Over the past weeks, I’ve been sharing a series of reflections on AI governance, decision accountability, and the role of human judgment in AI-enabled and cyber-physical systems.
These posts were never meant to present a complete solution.
They were intended to frame the problem space, clarify distinctions, and surface the governance challenges that emerge when AI systems operate in high-risk and security-critical environments.
This work is now progressing into a more structured form.
The concepts discussed so far are being consolidated into a coherent body of work, where governance is treated as an operational capability grounded in accountability, risk traceability, and evidence-based assurance — rather than as static documentation or post-hoc compliance.
Further details will be shared in due course, through a dedicated written work, with care taken to preserve clarity, rigor, and context.