Cybersecurity professionals operate under chronic strain, facing high workloads, continuous threat exposure, and with a rapid skill shortage. These conditions contribute to increasing rates of burnout and quiet quitting; two psychosocial phenomena that impair well-being, increase error risk, and exacerbate talent shortages. While artificial intelligence (AI) has been widely adopted to optimize threat detection and automate routine tasks, its potential to support human sustainability in cybersecurity remains under-theorized. This paper proposes a conceptual model that positions AI as a psychosocial ally capable of buffering the emotional and cognitive demands placed on cybersecurity professionals. Drawing on the Job Demands–Resources (JD-R) and Effort–Recovery frameworks, the model maps five organizational stressors namely work overload, poor workplace dynamics, skills gap, high-risk high-pressure job, and continuous upskilling to targeted AI-supported interventions. These include intelligent task triaging, sentiment analysis, adaptive microlearning, decision support systems, and embedded recovery prompts. Each intervention is framed as either a moderator (reducing stressor intensity) or a mediator (reshaping psychological processing), with theoretical grounding provided throughout. This interdisciplinary framework contributes to human-centered cybersecurity research by integrating occupational health theory, AI ethics, and operational realities. While conceptual in scope, the model lays the groundwork for empirical validation through simulation studies, field pilots, and trust calibration experiments. Ultimately, it calls for a shift in how AI is designed and evaluated, not just as a tool for system defense, but as a resource for sustaining the people who defend them.