Abstract: A plurality of communicatively coupled, networked assets may be threatened or attacked by a cybersecurity attack. The operational resiliency of the computer network determines whether the cybersecurity attack leads to a shutdown of one or more assets, or even the entire computer network. Machines and processes are disclosed to improve operational cybersecurity resiliency of software on the computer network. Machine learning is used to identify potential vulnerabilities from a vulnerability database. Chaos stress testing using a machine learning algorithm can be performed on software to exploit the vulnerabilities. A blast radius can be set to minimize any potential negative side effects of the testing. Software can be remediated to account for responses to the testing by reconfiguring to prevent exploitation of the vulnerabilities. A financial impact of the exploited vulnerabilities can be calculated and reports can be generated.

Abstract: A plurality of communicatively coupled, networked assets may be threatened or attacked by a cybersecurity attack. The operational resiliency of the computer network determines whether the cybersecurity attack leads to a shutdown of one or more assets, or even the entire computer network. Machines and processes are disclosed to improve operational cybersecurity resiliency of software on the computer network. Machine learning is used to identify potential vulnerabilities from a vulnerability database. Chaos stress testing using a machine learning algorithm can be performed on software to exploit the vulnerabilities. A blast radius can be set to minimize any potential negative side effects of the testing. Software can be remediated to account for responses to the testing by reconfiguring to prevent exploitation of the vulnerabilities. A financial impact of the exploited vulnerabilities can be calculated and reports can be generated.