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Intelligent systems increasingly influence decisions, allocate attention, generate recommendations, and mediate interactions across technical, organizational, and social environments. Yet trustworthiness is often approached through isolated interventions, such as improving model accuracy, adding post-hoc explanations, documenting datasets, or assigning nominal human oversight. This article proposes a trustworthy-by-design conceptual framework that treats trustworthiness as a system-level property requiring coordinated design and evaluation across the full intelligent-system lifecycle. The framework begins with system-purpose and risk-context definition, followed by stakeholder and responsibility mapping, data-integrity assessment, provenance and lineage review, data documentation and governance, model-reliability evaluation, robustness and uncertainty assessment, and bias and fairness review where relevant. It then connects explanation quality with human interpretability and decision usefulness, rather than assuming that technically generated explanations will necessarily support understanding. Human oversight, meaningful control, contestability, recourse, institutional accountability, traceability, auditability, monitoring, and feedback updating form additional interdependent layers. The framework’s principal contribution is to clarify how technical evidence must be connected to human decision conditions and institutional responsibility without treating any individual safeguard as sufficient. Data provenance does not establish data appropriateness, robustness does not prove safety, explanations do not guarantee interpretability, human presence does not ensure meaningful control, and governance arrangements do not independently establish accountability. The framework may support conceptual analysis, assurance-oriented evaluation, and lifecycle governance planning, but requires empirical validation, human-centered assessment, domain-specific interpretation, and continuing institutional review.

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.