Safety Anxiety in the Age of Artificial Intelligence: A Framework for Mitigation Using the SCAB and PRIS Protocols

As artificial intelligence (AI) systems move from novelty to infrastructure, many users report “safety anxiety”: a persistent, situation-specific apprehension that AI may harm, deceive, surveil, or destabilize one’s life. This article defines safety anxiety as a construct adjacent to technostress, algorithm aversion, and trust in automation, then proposes two practical mitigation frameworks: the SCAB Protocol (Sovereignty, Coherence, Alignment, Boundaries, Agency, Probity) for behavioral and governance guardrails, and the PRIS Protocol (Psychosis Risk Interaction Score) for early detection of risky human–AI interaction patterns. Drawing on research in human-automation trust, algorithm aversion, transparency/explainability, and health-behavior theory (Protection Motivation Theory, Health Belief Model, UTAUT), we articulate a conceptual model linking uncertainty, loss of control, and threat appraisals to maladaptive responses (avoidance, overreliance, compulsive checking). We then map SCAB and PRIS interventions onto product design, user experience, and organizational policy, and illustrate the approach across five real-world scenarios (AI companions, consumer scams, workplace monitoring, symptom checking, and creative work). Finally, we propose an evaluation plan (validated anxiety/trust scales, technostress measures, A/B tests) and discuss ethical and cultural considerations. The central claim is that safety anxiety is not merely a user-education problem but a design-and-governance problem: when users have clear guardrails (SCAB) and risk-sensing feedback loops (PRIS), uncertainty decreases, perceived control increases, and trust calibrates toward appropriate reliance. Evidence from adjacent literatures—trust in automation, explainability/transparency, and technostress—supports the core mechanisms and informs the intervention design.