A system for assessing and remedying software vulnerability, comprising: a memory to store executable instructions; and a processor adapted to access the memory, the processor further adapted to execute the executable instructions stored in the memory to: access an automated triage rule library comprising a plurality of pre-defined automated triage policies corresponding to a plurality of predetermined vulnerability types, wherein each automated triage policy comprises a decision tree for determining whether one of the plurality of predetermined vulnerability types is exploitable; access a machine learning model library for probabilistic determination of whether one of the predetermined plurality of predetermined vulnerability types is exploitable; obtain an electronic document listing potential vulnerability issues of a software product based on source code of the software product; determine whether the potential vulnerability issues are associated with one of the plurality of predetermined vulnerability types; when it is determined that the potential vulnerability issues are associated with the one of the plurality of predetermined vulnerability types, determine whether the software product is exploitable based on processing the electronic document using an automated triage policy retrieved from the automated triage rule library associated with the one of the plurality of predetermined vulnerability types and a corresponding decision tree, otherwise determine probabilistically whether the software product is exploitable based on processing the electronic document using a machine learning model from the machine learning model library, wherein the decision tree comprises a set of progressively ordered automated triage methods, wherein each automated triage method in the automated triage policy is configured to generate a triage output when processing the electronic document; and determine a remediation of the vulnerability issues based on the triage output. a memory to store executable instructions; and a processor adapted to access the memory, the processor further adapted to execute the executable instructions stored in the memory to: access an automated triage rule library comprising a plurality of pre-defined automated triage policies corresponding to a plurality of predetermined vulnerability types, wherein each automated triage policy comprises a decision tree for determining whether one of the plurality of predetermined vulnerability types is exploitable; access a machine learning model library for probabilistic determination of whether one of the predetermined plurality of predetermined vulnerability types is exploitable; obtain an electronic document listing potential vulnerability issues of a software product based on source code of the software product; determine whether the potential vulnerability issues are associated with one of the plurality of predetermined vulnerability types; when it is determined that the potential vulnerability issues are associated with the one of the plurality of predetermined vulnerability types, determine whether the software product is exploitable based on processing the electronic document using an automated triage policy retrieved from the automated triage rule library associated with the one of the plurality of predetermined vulnerability types and a corresponding decision tree, otherwise determine probabilistically whether the software product is exploitable based on processing the electronic document using a machine learning model from the machine learning model library, wherein the decision tree comprises a set of progressively ordered automated triage methods, wherein each automated triage method in the automated triage policy is configured to generate a triage output when processing the electronic document; and determine a remediation of the vulnerability issues based on the triage output. access an automated triage rule library comprising a plurality of pre-defined automated triage policies corresponding to a plurality of predetermined vulnerability types, wherein each automated triage policy comprises a decision tree for determining whether one of the plurality of predetermined vulnerability types is exploitable; access a machine learning model library for probabilistic determination of whether one of the predetermined plurality of predetermined vulnerability types is exploitable; obtain an electronic document listing potential vulnerability issues of a software product based on source code of the software product; determine whether the potential vulnerability issues are associated with one of the plurality of predetermined vulnerability types; when it is determined that the potential vulnerability issues are associated with the one of the plurality of predetermined vulnerability types, determine whether the software product is exploitable based on processing the electronic document using an automated triage policy retrieved from the automated triage rule library associated with the one of the plurality of predetermined vulnerability types and a corresponding decision tree, otherwise determine probabilistically whether the software product is exploitable based on processing the electronic document using a machine learning model from the machine learning model library, wherein the decision tree comprises a set of progressively ordered automated triage methods, wherein each automated triage method in the automated triage policy is configured to generate a triage output when processing the electronic document; and determine a remediation of the vulnerability issues based on the triage output.