Accenture Global Solutions Limited

Karel Kohout

Richard Levine

Ganesh Devarajan

Mário Lauande Lacroix

Finbarr S Tarrant

Johnny Obando

Navdeep Sharma

Sidath Handurukande

Gopal Kavanadala Sridhar

John Donovan Delmare, Jr

Jee Hyub Kim

Owen McGauley

David Glyn Darkin

Mehdi Sanoh

Eanna Mulrooney

Dalton Harris

Jason Widen

Anton Plotnikov

Steven Nicholas Strupp

Matt Renzi Quillosa

Vidya Govindan

Finbarr Tarrant

Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Systems and methods for triaging software vulnerabilities

Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their automated triage based on machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and extract features from the report for each potential vulnerability issue. The system may receive policy data and business rules, and compare the extracted features relative to such data and rules. The system may determine a token based on the source code of a potential vulnerability issue, and a vector based on the extracted features of a potential vulnerability issue and based on the token. The system may select a machine learning modelling method and/or an automated triaging method based on the vector, and determine a vulnerability accuracy score based on the vector using the selected method.

A device may receive contextual data, computational data, experiential data, and industry data associated with software code, and may receive detected vulnerabilities data identified by a scanning model based on the software code and software code metadata of the computational data. The device may process the contextual data, the computational data, and the experiential data, with a contextual identification model, to determine a set of rules and a set of actions, and may enrich, via an enrichment model, the industry data with the experiential data to generate enriched industry data. The device may process the software code metadata, the detected vulnerabilities data, and the enriched industry data, with a correlation model, to generate analysis data, and may process the analysis data and the set of rules, with a security model, to confirm security issues associated with the software code. The device may perform one of the set of actions based on the security issues.

Utilizing orchestration and augmented vulnerability triage for software security testing

Growing community of inventors

Prague, Czechia

Karel Kohout