Data poisoning is an attack that corrupts the data used to train, fine-tune or ground an AI model. The model then learns hidden flaws, biases or backdoors chosen by the attacker.
Prompt injection is an attack where text a model reads, such as a web page, email or document, carries instructions that override what the application intended. It is the main security risk for any model that can call tools.
Retrieval-augmented generation (RAG) is a pattern that hands a language model relevant passages from your own documents at question time. Its answers then rest on that material, not only on what it learnt in training.
Training data is the set of examples a machine learning model learns from. Its quality, coverage and labelling largely determine how well the model performs.
AI security is protecting AI systems, their models, data and connected tools from attack and misuse. It also covers the new risks that AI adoption brings to the rest of the business.
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