Fail-Closed Execution as a General Validity Mechanism for AI, Scientific, Financial, and Safety-Critical Systems

Across artificial intelligence, scientific research, finance, and safety-critical infrastructure, modern computational systems increasingly operate in environments where invalid results are costly, dangerous, or irreversible. Despite domain-specific differences, these systems commonly rely on post-execution validation, permitting execution to complete optimistically and attempting to detect failure only after artifacts have been generated and propagated. This paper examines how post-hoc validation failures manifest across multiple high-stakes domains through concrete execution walkthroughs, showing how invalid artifacts are produced, trusted, and acted upon before detection occurs. It then analyzes how a fail-closed execution model—where execution completion is conditioned on satisfaction of governing constraints—fundamentally alters system behavior. Under fail-closed execution, invalid states are prevented from completing and artifact generation is structurally gated on execution success, rendering invalid outputs unproducible rather than merely detectable. The paper does not prescribe specific enforcement mechanisms, but presents fail-closed execution as a general architectural validity mechanism applicable across AI systems, scientific pipelines, financial models, and safety- and security-sensitive environments. The work argues that validity-by-construction represents a necessary evolution for trustworthy computation in increasingly autonomous and consequential systems.