AI testing is critical as enterprises deploy models into business-critical processes. Failures in accuracy, ethics, or security can cause financial and reputational damage. Current supervised models rely heavily on human oversight, making testing essential across performance, safety, and security. Challenges include concept drift, data bias, and rapid model aging. TCS proposes an AI assurance framework combining pre-deployment testing, bias checks, non-functional testing, and post-deployment monitoring with human-in-the-loop oversight. Standardized practices, continuous retraining, and collaborative intelligence between humans and machines are key to building reliable, future-proof AI systems.

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