Accenture Global Solutions Limited

Dan Klein

Eitan Hadar

Gal Engelberg

Benjamin Glen McCarty

Moshe Hadad

Tomer Ram

Aolin Ding

Hodaya Binyamini

Nimrod Busany

Louis William Divalentin

Roei Levi

Daniel Nahmias

Victor Daniel Corvalan

Rafi Shalom

Lior Bass

Adrian Kuboszek

Elad Segev

Amin Hass

Daniel Voicu

Yossef Tahar

Implementations include a computer-implemented method comprising: obtaining data representing observed conditions in an enterprise network, each observed condition being associated with at least one cybersecurity issue, a cybersecurity issue comprising one of (i) a vulnerability comprising an instance of a vulnerable condition or (ii) a weakness that is likely to cause a vulnerability to occur; using a plurality of exploitation prediction models to determine probabilities of exploitation of the cybersecurity issues associated with the observed conditions in the enterprise network, wherein the plurality of exploitation prediction models are trained using a knowledge mesh generated using data from cybersecurity repositories; assigning a priority ranking to each of the observed conditions in the enterprise network based on the respective probabilities of exploitation for the cybersecurity issues associated with the observed conditions; and performing one or more actions to mitigate the observed conditions in the enterprise network based on the priority rankings.

Automated cybersecurity vulnerability prioritization

Implementations are directed to methods, systems, and apparatus for automated prioritization of cyber risk to digital identities. Actions include obtaining graph data defining a knowledge graph including nodes and edges, the nodes representing respective objects of the enterprise network including digital identities and resources, each node being associated with an explicit risk score and properties of the represented object, each edge representing a relation between objects; determining priority scores for the objects, including, for a first object represented by a first node: determining an implicit risk score for the first node; determining a total risk score for the first node; and determining a priority score for the first node based on the total risk score and properties associated with the first node; generating a ranking of the objects according to the priority scores; and providing, for presentation on a display, cyber security risk data indicating the ranking of the objects.

Automated prioritization of cyber risk to digital identities

Implementations include methods, systems, computer-readable storage medium for mitigating cyber security risk of an enterprise network. A method includes: receiving an initial analytic attack graph (AAG) that is representative of paths within the enterprise network with respect to at least one target asset, the initial AAG comprising nodes and edges between the nodes; identifying, from the nodes of the initial AAG, a plurality of node groups, each node group including two or more nodes having at least one common attribute; generating an abstract AAG from the initial AAG, the abstract AAG including at least one abstract node, wherein each node group of the initial AAG is represented by a respective abstract node of the abstract AAG; determining a set of remedial actions at least partially based on the abstract AAG; and executing remedial actions in the set of remedial actions to reduce a cyber security risk to the enterprise network.

Analytical attack graph abstraction for resource-efficiencies

Growing community of inventors

Rosh Ha'ayin, Israel

Dan Klein