Systems and methods implementing a parallel search architecture for machine learning-based acceleration of data security, data security architectures, and data security compliance activities

A computer-implemented method for machine learning-informed augmentation of incomplete query-embedded digital artifacts, the method comprising:

• at a remote security response generation service implemented by a distributed network of computers:
• receiving, via a user interface, a target security query;
• executing, by one or more processors, a security query search based on the target security query, wherein executing the security query search includes retrieving a first corpus of candidate digital artifacts from a first search engine based on an input of the target security query;
• constructing an unprocessed queue of candidate digital artifacts, the unprocessed queue including at least a subset of the first corpus of candidate digital artifacts;
• providing, as input, each candidate digital artifact of the unprocessed queue of candidate digital artifacts to a sentence transformer machine learning model to generate a relevance score for each candidate digital artifact in the unprocessed queue;
• generating one or more candidate responses to the target security query based on a subset of prioritized candidate digital artifacts, the subset of prioritized candidate digital artifacts including one or more candidate digital artifacts from the unprocessed queue that each have a corresponding relevance score that satisfies a relevance threshold;
• and
• surfacing, via the user interface, the one or more candidate responses to the target security query.

A computer-implemented method for machine learning-informed augmentation of an incomplete query-embedded digital artifact, the method comprising:

• at a remote security response generation service implemented by a distributed network of computers:
• receiving, via a user interface, the incomplete query-embedded digital artifact;
• identifying one or more incomplete governance, risk, and compliance (GRC) query-answer sets in the incomplete query-embedded digital artifact, wherein each incomplete GRC query-answer set includes a GRC query and at least one empty answer value;
• computing, by one or more processors, one or more candidate response values for each of the at least one empty answer values for each incomplete GRC query-answer set, wherein computing the one or more candidate response values for each incomplete GRC query-answer set includes:
• (i) retrieving a first corpus of candidate historical query-answer sets based on executing a first search algorithm with an input of the GRC query of the incomplete GRC query-answer set and a second corpus of candidate historical query-answer sets based on executing a second search algorithm with an input of the GRC query of the incomplete GRC query-answer set, wherein the second search algorithm is distinct from the first search algorithm,
• (ii) constructing a merged corpus of candidate historical query- answer sets, the merged corpus comprising at least a subset of the first corpus of candidate historical query-answer sets and at least a subset of the second corpus of candidate historical query-answer sets,
• (iii) providing, as input, each candidate historical query-answer set of the merged corpus of candidate historical query-answer sets to a sentence transformer machine learning model to compute a relevance score for each candidate historical query-answer set in the merged corpus, and
• (iv) generating the one or more candidate response values based on a subset of prioritized candidate historical query-answer sets from the merged corpus that each have a corresponding relevance score that satisfies a relevance threshold;
• and
• surfacing the one or more candidate response values for each of the at least one empty answer values for each incomplete GRC query-answer set via the user interface.