Access our White Paper on AI Testing.

The rapid adoption of artificial intelligence (AI), particularly generative and agentic systems, presents both significant opportunities and substantial risks. Many organisations are deploying AI solutions without adequate testing, governance, or validation, increasing exposure to operational, security, compliance, and reputational challenges.

This paper proposes a risk-centred framework that treats AI as a probabilistic system requiring continuous assessment rather than traditional software testing alone. Integrating behavioural validation, adversarial testing, governance controls, and lifecycle monitoring, the framework supports trustworthy and scalable AI deployment. Drawing on Higher Education experience, the paper highlights content governance, data leakage prevention, and usage monitoring as critical assurance priorities.