Feature attribution estimates which parts of an input—such as words, image regions, or measured signals—contributed to a model’s output. Depending on the method, it may assign each feature an importance score or highlight influential parts of the input. These results are estimates, not necessarily a complete or exact record of the model’s decision process.
In cybersecurity, attribution can help analysts inspect why a system flagged a message, classified activity, or prioritized an alert, making errors and suspicious patterns easier to challenge. It can also support audits of automated decisions. Treat attribution as evidence for review, not proof that a model reasoned as the explanation suggests: preserve the input, model version, output, and attribution results, and verify important findings independently.