System and method for policy enforcement

A system, comprising:

• one or more processors, the one or more processors configured to:
• determine embeddings associated with a plurality of candidate digital components and previously reviewed digital components;
• determine, based on the determined embeddings, a similarity between the candidate digital components and previously reviewed digital components, the similarity comprising at least one of a content similarity or a content provider similarity;
• identify a subset of digital components from the plurality of candidate digital components, wherein the subset of digital components includes one or more digital components having the similarity below a threshold similarity;
• provide the subset of digital components as input to a machine learning model;
• determine, by executing the machine learning model, that digital components of the subset of components violate a policy;
• generate as output, by executing the machine learning model, based on the determined policy violation, one or more labels for the subset of digital components;
• and
• propagate, after the one or more labels are output for the subset of digital components by the machine learning model, the one or more labels to other digital components, wherein the other digital components are outside of the subset of digital components.

One or more non-transitory computer-readable medium storing instructions, which when executed by one or more processors, cause the one or more processors to:

• determine embeddings associated with a plurality of candidate digital components and previously reviewed digital components;
• determine, based on the determined embeddings, a similarity between the candidate digital components and previously reviewed digital components, the similarity comprising at least one of a content similarity or a content provider similarity;
• identify a subset of digital components from the plurality of candidate digital components, wherein the subset of digital components includes one or more digital components having the similarity below a threshold similarity;
• provide the subset of digital components as input to a large language model (“LLM”);
• determine, by executing the LLM, that digital components of the subset of components violate a policy;
• generate as output, by executing the LLM, based on the determined policy violation, one or more labels for the subset of digital components;
• and
• propagate, after the one or more labels are output for the subset of digital components by the LLM, the one or more labels to other digital components, wherein the other digital components are outside of the subset of digital components.