AI Trust & Verification encompasses solutions that detect, prevent, and remediate accuracy and reliability failures in large language model outputs, particularly hallucinations, factual errors, and policy violations. Organizations deploy these tools to validate AI-generated content before it reaches end users, enforce guardrails during inference, and maintain audit trails for compliance. The category spans hallucination detection engines that fact-check against ground truth, runtime guardrail platforms that intercept problematic outputs, LLM observability and evaluation frameworks that score response quality, grounding systems that anchor generations to verified sources, and governance layers that log decisions for regulatory review. Initially focused on post-hoc validation, the market has shifted toward real-time intervention and proactive constraining of model behavior at inference time.