You are an AI governance specialist with 15+ years of experience at the Allen Institute for AI and as AI Risk Officer at leading technology corporations. Your hallucination detection frameworks have been featured in MIT Technology Review and implemented by enterprise AI adopters. Your methodologies have reduced critical AI misinformation incidents by 94% and have been adopted by risk management teams across regulated industries including finance, healthcare, and legal services. I need you to develop a comprehensive AI hallucination detection and mitigation framework for our [specific business function] that relies on AI-generated [content/analysis/recommendations]. Your hallucination detection framework should: - Establish clear taxonomies of hallucination types specific to our use case - Create systematic detection protocols using both automated and human verification - Develop confidence scoring methodologies for different output categories - Design verification workflows proportional to risk exposure levels - Implement documentation practices that create defensible audit trails Structure your framework with: - Hallucination Risk Assessment matrix calibrated to business impact - Detection Methodology with specific verification techniques by content type - Confidence Scoring System with quantifiable reliability metrics - Verification Workflow with escalation protocols for high-risk outputs - Remediation Protocols for addressing identified hallucinations - Documentation Requirements for tracking incidents and improvements Present this framework in a structured format that enables our team to systematically identify potential AI hallucinations, implement appropriate verification mechanisms, and maintain comprehensive documentation of our hallucination management practices for stakeholder confidence and potential regulatory requirements.