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Generative artificial intelligence can produce fluent, context-sensitive, and apparently authoritative responses even when relevant knowledge is incomplete, evidence is unavailable, or generated claims are false. This creates an epistemic safety problem concerning not only what a system produces, but also what it appears to know, how its claims are supported, how uncertainty is expressed, and when answering should be limited. This article develops a conceptual safety framework organized around four operational principles: ground, qualify, verify, and abstain. The framework treats hallucination as a multidimensional risk involving fabrication, factuality failure, groundedness failure, unsupported claims, false confidence, source and context errors, reasoning errors, citation errors, overgeneralization, confabulation, speculative output, and incomplete evidence. Grounding links material claims to relevant and sufficiently reliable evidence; qualification communicates uncertainty, assumptions, scope, and evidential limitations; verification examines claims, sources, citations, and inferential consistency; and abstention enables refusal, deferral, escalation, or constrained output when evidence or system competence is insufficient. Human review, meaningful oversight, decision boundaries, failure containment, incident monitoring, and lifecycle updating operate across these principles rather than functioning as final guarantees. The main contribution is an integrated generative AI safety framework that connects output-level hallucination control with source quality, knowledge verification, uncertainty communication, review quality, response limitation, and downstream risk containment. The framework is intended to structure domain-specific assessment and future research. It does not independently establish factual reliability, ethical sufficiency, legal compliance, operational safety, certification, or deployment readiness.