AI risk is not just about the model. It starts with the data feeding it. If training data, labels, or feedback can be influenced, the system may learn the wrong thing and keep repeating it. For UK teams, the practical response is to map the pipeline, validate incoming data, quarantine suspicious records, and keep clear lineage for every dataset. Full guide in the article. Full article: https://clearpathsecurity.co.uk/securing-ai-pipelines-against-data-poisoning-a-practical-guide-for-technical-teams/?utm_source=tumblr&utm_medium=social









