Identity Drift as Structural Failure Mode: Why Rule Compliance Is Not Enough

The first paper in this series introduced the Triquetra Architecture. The secondevaluated it under adversarial pressure. This paper addresses a different question: whatexactly is the architecture trying to preserve? Most AI governance approaches focus onrule compliance, detecting whether system outputs violate explicit constraints.While necessary, this form of monitoring cannot detect a distinct class of failure:identity drift. A system may remain fully compliant with every rule while graduallydiverging from its original behavioral profile, producing outputs that are technicallyacceptable yet operationally inconsistent with its intended character. We argue thatidentity drift constitutes a structural governance failure mode separate from ruleviolation. Detecting it requires monitoring continuity of system behavior over timerather than evaluating outputs in isolation. The Triquetra Architecture addresses thisthrough Tier III, the Eve Protocol, which maintains a continuity baseline and evaluatesdeviations independently of rule enforcement. Case observations from the stress-testframework illustrates how continuity monitoring can identify degraded interactioncontexts and enforce confidence-based governance without requiring identity trackingor surveillance. Together, the three papers establish a layered governance model inwhich rule compliance and identity continuity operate as complementary safeguards