AI Governance
Control AI behavior while it’s being used — where failure actually occurs.
Most AI governance efforts focus on intent, policy, or alignment.
Most AI failures occur later during use, under ambiguity,
and when systems
should stop, refuse, or escalate.
The material linked on this page explains where and why AI systems fail,
how those failures emerge during use, and
what operational controls prevent them.
If a system cannot stop when information is insufficient, surface uncertainty
explicitly, and preserve human decision authority, it is not governed.
A Practical Framework for Governing AI in Real Use
Why AI Behaves the Way It Does
Explains how probabilistic, nondeterministic systems generate outputs, why drift and overconfidence emerge, and why longer generations increase risk.
Conversational Posture as Inference-Time Control
Introduces a structural intervention: minimal-first responses that shorten generation trajectories, surface ambiguity early, and keep authority human-held.
Conversational Posture Kit
Turns the posture into something deployable, including system rules, operator guidance,
telemetry, and an evaluation protocol that works across AI system providers
What Governance Enables
- Early detection of drift and assumption-stacking
- Explicit uncertainty instead of fluent guesswork
- Faster correction cycles with less downstream rework
- Human judgment preserved by design, not policy
How to Judge AI and What Good Looks Like
How Leaders Should Evaluate AI
A 3-page governance checklist for industry leaders evaluating AI tools. Nine domains, plain language, no acquisition background required. Start here for a quick read; use the Acquisition Framework for defense and government depth.
Behavioral Governance Criteria for AI Acquisition
A comprehensive evaluation framework for defense and high-consequence AI systems. Twelve governance domains, acquisition-ready tools including RFP language, evaluation overlays, and vendor self-assessments. Aligned with DoD Responsible AI principles and NIST AI RMF.
Gonvernance Under Adversarial Pressure
A naturalistic case study: two governed AI systems, coordinated adversarial pressure, and an ontologically underdetermined question. Documents what happens when governance holds and what failure modes it prevents.
What Well-Governed AI Looks Like in Practice
A real-world example showing sustained, high-cognition AI collaboration with zero unresolved drift and near-zero rework.
Design Boundaries
Designed For
- Human-in-the-loop decision support
- Enterprise, regulated, and high-assurance environments
- Work where stopping and escalation matter
Not Designed For
- Fully autonomous agents
- Unattended batch workflows
- Systems that cannot stop or refuse
Governance is not overhead.
Constraint is what makes dependable AI use possible.