Applying IAMM to Artificial Intelligence

Artificial intelligence is becoming embedded in customer engagement, financial systems, logistics, infrastructure, decision-making, and automation. The same qualities that make AI useful also create new forms of exposure. Models can be opaque, data pipelines can be fragile, autonomous systems can exceed intended boundaries, and adversaries can exploit weaknesses faster than periodic controls can adapt. The consequence is larger than technical disruption. A compromised chatbot, manipulated dataset, deepfake campaign, or uncontrolled AI agent can undermine trust, create regulatory exposure, and leave leaders accountable for outcomes they cannot adequately explain. The Integrated Assurance Maturity Model (IAMM) provides a structured way to address that problem. It does not replace established standards. It connects governance, architecture and engineering, IT and operations, risk and compliance, metrics and reporting, and culture and collaboration so that assurance can operate across the full AI lifecycle.