Tested, Trusted, and Verifiable: Maintaining AI Assurance in the Synthetic Enterprise
About This Session
AI systems can change through model updates, new data sources, altered prompts, expanded
permissions, and shifting operational environments, meaning a system that was considered safe at deployment may behave differently over time. This panel will examine how organizations can apply continuous assurance through ongoing testing, behavioral monitoring, red teaming, drift detection, evidence validation, and clearly defined triggers for reassessment or reauthorization.
Panelists will also explore how organizations can preserve trust in the records and evidence these systems produce. The discussion will address authenticity, chain of custody, AI-generated
reports and summaries, manipulated media, synthetic records, and the challenge of proving what occurred when legitimate evidence can be altered, fabricated, or dismissed as artificial.
permissions, and shifting operational environments, meaning a system that was considered safe at deployment may behave differently over time. This panel will examine how organizations can apply continuous assurance through ongoing testing, behavioral monitoring, red teaming, drift detection, evidence validation, and clearly defined triggers for reassessment or reauthorization.
Panelists will also explore how organizations can preserve trust in the records and evidence these systems produce. The discussion will address authenticity, chain of custody, AI-generated
reports and summaries, manipulated media, synthetic records, and the challenge of proving what occurred when legitimate evidence can be altered, fabricated, or dismissed as artificial.