LUMINA-30 is a boundary framework for determining whether human refusal remained effective before irreversible AI autonomy emerged.
Why this matters here
For high-capability AI systems, the issue is not only failure probability.
The issue is whether loss structures become difficult to reverse once human refusal is no longer practically effective.
Screening questions
Who can stop the system?
Is that stop authority technically and organizationally effective?
Are stop commands and refusals logged?
Are humans protected when they reject AI-driven execution?
Can the organization still interrupt before irreversible externalization?
Can these claims be verified after the fact?
Contract and underwriting implications
Inability to demonstrate effective refusal indicates elevated control risk.
Missing records are a major adverse factor.
Closed-loop self-validation should be treated as an adverse condition.
Concentrated approval authority should trigger enhanced review.
Conclusion
LUMINA-30 provides a practical screen for whether human intervention remains real before losses become structurally irreversible.