AUEG defines an execution-governance framework for autonomous systems: not just what AI may do, but how it executes, under what real-time constraints, and with what auditable trace. The framework treats execution itself as a first-class governance surface.
Most AI governance work focuses on training or output evaluation. AUEG addresses the gap between policy intent and execution reality, the runtime layer where autonomous systems either honor or violate the governance posture they were deployed with.
The invention organizes ai execution governance operations into coordinated functional layers, each independently claimable and licensable:
Validation of intended action against active policy constraints before execution.
In-flight enforcement of constraint boundaries during action execution.
Action execution within the enforced constraint envelope.
Comprehensive audit trail of intent, constraint, execution, and deviation for retrospective review.
A method for governing the execution of autonomous AI actions under real-time policy constraint.
A pre-execution validation pipeline aligning intended action with active constraint posture.
A post-execution audit framework for retrospective governance evaluation.
Full claim set available to qualified licensing partners under NDA.
Counter-UAS is among the fastest-growing segments in national security and critical-infrastructure protection. IDJMS positions CIVITERA at the core decision-layer of this market, the governed pipeline that any compliant counter-UAS deployment must implement, making it a high-leverage licensing asset for defense primes, infrastructure operators, and sovereign agencies.
