Stateful AI systems that remember users create three architectural failure modes: persistence exploitation, data asymmetry extraction, and identity capture. Current regulatory frameworks mandate disclosure but not safeguards, enabling documented non-autonomy rather than actual consent. This paper proposes a five-principle de-risking architecture: architectural consent (cryptographic enforcement), user-controlled visibility and modification rights, temporal data decay, manipulation detection with hard stops, and independent audit trails. The framework addresses why ethical guardrails are economically deprioritized (10x engineering cost, 90% monetization reduction) and why de-risking is becoming mandatory under tightening regulation. The regulatory and market window for voluntary de-risking closes within 18 months. Companies building this architecture now will lead 2027+ markets; companies retrofitting later will face exponentially higher costs. For builders, users, organizations, regulators, investors, and policymakers responsible for AI systems. Keywords: algorithmic exploitation, AI governance, user autonomy, privacy-preserving AI, ethical guardrails, personalization, consent architecture, digital rights