AI applications are developed to operate reliably over long periods using the right models and datasets, to behave consistently and handle errors (robustness), and to stay secure against cyber attack as legal and industry frameworks require. Reliability covers service availability and serviceability, with disciplined software engineering and close management of data quality and integrity (consistency, accuracy, validity, timeliness, uniqueness, completeness). Robustness requires validation and error handling at every step of the pipeline and consistent performance on new, independent data and corner cases. Security applies conventional practice plus defences against adversarial perturbation, reverse engineering of models exposed as services, and malicious input, with data cleansed, secured and encrypted and access to trained models controlled. Continuous monitoring, validation, verification of accuracy and maintenance keep applications robust and secure.
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