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This paper presents experiments using Artificial Intelligence (AI) algorithms for online monitoring of integrated computer systems, including System-on-Chip based embedded systems. This new framework introduces an AI-lead infrastructure that is intended to operate in parallel with conventional monitoring and diagnosis techniques. Specifically, an initial application is presented, where each of the systempsilas software tasks are characterised online during their execution by a combination of novel hardware monitoring circuits and background software. These characteristics then stimulate a Self-Organising Map based classifier which is used to detect abnormal system behaviour, as caused by failure and malicious tampering including viruses. The approach provides a system-level perspective and is shown to detect subtle anomalies.