BitSight Technologies, Inc.

Systems and methods are disclosed for providing a cyber resilience rating. A method can include obtaining a plurality of entity indicators. The method can include determining a peer group of entities for the entity based on the entity indicators. The method can include obtaining a plurality of loss event records for the peer group. The method can include executing, based on the loss event records, a plurality of Monte Carlo simulations to generate loss simulation data. The method can include identifying, based on the loss simulation data, an expected probability value. The method can include providing a risk factor score indicative of a cyber security risk of the entity based on the identified expected probability value. The method can include providing a cyber resilience rating for the entity based on a combination of the risk factor score, a fortitude factor score, and a governance factor score.

A computer-implemented method is provided for statistical modeling of entities of a particular type. The method can include obtaining entity data including a plurality of entity data sets, each entity data set associated with a respective entity and including values for one or more static parameters indicative of a type of the entity. Each entity data set can include (i) values for input parameter(s) indicative of a security profile of the entity and (ii) a value of a security class parameter indicative of a security class of the entity based on the values of the input parameters. The method can include training a statistical classifier to infer a value of the security class parameter indicative of the security class of a particular entity of the particular type based on values of one or more of the input parameters indicative of a security profile of the particular entity.

Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

A computer-implemented method is provided for statistical modeling of entities of a particular type. The method can include obtaining entity data including a plurality of entity data sets, each entity data set associated with a respective entity and including values for one or more static parameters indicative of a type of the entity. Each entity data set can include (i) values for input parameter(s) indicative of a security profile of the entity and (ii) a value of a security class parameter indicative of a security class of the entity based on the values of the input parameters. The method can include training a statistical classifier to infer a value of the security class parameter indicative of the security class of a particular entity of the particular type based on values of one or more of the input parameters indicative of a security profile of the particular entity.

A method and system for creating a composite security rating from security characterization data of a third party computer system. The security characterization data is derived from externally observable characteristics of the third party computer system. Advantageously, the composite security score has a relatively high likelihood of corresponding to an internal audit score despite use of externally observable security characteristics. Also, the method and system may include use of multiple security characterizations all solely derived from externally observable characteristics of the third party computer system.

A cybersecurity risk management method may include recommending, for each of a plurality of affiliates of an entity, a respective cybersecurity criticality tier selected from a set of cybersecurity criticality tiers; receiving user input adjusting and/or adopting the recommended cybersecurity criticality tier for each of the affiliates; assigning each of the affiliates to the respective adjusted or adopted cybersecurity criticality tier; obtaining respective security scores for the affiliates; and displaying a user interface component configured to show a visualization of a cybersecurity risk management plan of the entity with respect to the plurality of affiliates, wherein the risk management plan partitions the affiliates into a plurality of affiliate sets based on the security scores and the assigned cybersecurity criticality tiers of the affiliates and specifies, for each of the affiliate sets, an action to be taken by the entity with respect to the affiliates in the affiliate set.

Methods and systems are provided for assessing the cybersecurity state of entities based on extended-computer network characteristics. A method can include obtaining, for a plurality of computer networks associated with an entity and not associated with the entity, a first and second network dataset. The first and second network datasets can be combined. A plurality of Internet Protocol (IP) addresses associated with the entity and associated with a plurality of entities can be obtained, where the entity and the plurality of entities each associated with a unique identifier (UID). The method can include determining whether each of the plurality of computer networks not associated with the entity comprises a remote office network. A cybersecurity state of the entity can be determined based on an evaluation of security characteristics of the IP addresses associated with the entity and of one or more IP addresses attributed to the remote office networks.

A system and method for setting alert thresholds related to cybersecurity ratings of one or more affiliate entities. An example method includes: obtaining entity data including cybersecurity event data for an affiliate entity; calculating a time-series cybersecurity rating for the affiliate entity based on the entity data; associating an alert reporting threshold with the time-series cybersecurity rating, wherein a comparison of the alert reporting threshold to the time-series cybersecurity rating determines a number of alerts reported for the affiliate entity; applying an alternative alert reporting threshold against the time-series cybersecurity rating to determine an alternative number of alerts reported for the affiliate entity; and updating the alert reporting threshold for the time-series cybersecurity rating to the alternative alert reporting threshold.

Computer-implemented methods and systems are provided for the detection of software presence remotely through the web browser by detecting the presence of webinjects in a web browser that visits a detection webpage. The methods can include delivering a detection webpage to a web browser, in which the detection webpage has detection code configured to detect a presence of the webinject in the detection webpage; and inspecting, by the detection code, rendering of content of the detection webpage in the browser to detect webinject content in the detection webpage by the webinject, the webinject content including one or more Hypertext Markup Language (HTML) components. The method can further include, if webinject content is detected, generating a fingerprint for each of the one or more HTML components; transmitting the one or more fingerprints to an external server; and classifying, by the external server, the webinject based on the one or more fingerprints.