Abstract: A system for determining an entity's security rating may include a ratings engine and a security database. The security database may include a manifest and a distributed index containing security records. Each of the security records may have a key (e.g., a network identifier of a network asset) and a value (e.g., security information associated with the network asset identified by the key). The keyspace may be partitioned into multiple key ranges. The manifest may contain references to segments of the distributed index. Each segment may be associated with a key range and may index a group of security records having keys within the key range. The manifest and the segments may be stored in an object storage system. The ratings engine may determine the security rating of an entity based on security records of the entity's network assets, which may be retrieved from the database.

Abstract: 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.

Abstract: A system for determining an entity's security rating may include a ratings engine and a security database. The security database may include a manifest and a distributed index containing security records. Each of the security records may have a key (e.g., a network identifier of a network asset) and a value (e.g., security information associated with the network asset identified by the key). The keyspace may be partitioned into multiple key ranges. The manifest may contain references to segments of the distributed index. Each segment may be associated with a key range and may index a group of security records having keys within the key range. The manifest and the segments may be stored in an object storage system. The ratings engine may determine the security rating of an entity based on security records of the entity's network assets, which may be retrieved from the database.

Abstract: 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.

Abstract: A system for determining an entity's security rating may include a ratings engine and a security database. The security database may include a manifest and a distributed index containing security records. Each of the security records may have a key (e.g., a network identifier of a network asset) and a value (e.g., security information associated with the network asset identified by the key). The keyspace may be partitioned into multiple key ranges. The manifest may contain references to segments of the distributed index. Each segment may be associated with a key range and may index a group of security records having keys within the key range. The manifest and the segments may be stored in an object storage system. The ratings engine may determine the security rating of an entity based on security records of the entity's network assets, which may be retrieved from the database.

Abstract: A system for determining an entity's security rating may include a ratings engine and a security database. The security database may include a manifest and a distributed index containing security records. Each of the security records may have a key (e.g., a network identifier of a network asset) and a value (e.g., security information associated with the network asset identified by the key). The keyspace may be partitioned into multiple key ranges. The manifest may contain references to segments of the distributed index. Each segment may be associated with a key range and may index a group of security records having keys within the key range. The manifest and the segments may be stored in an object storage system. The ratings engine may determine the security rating of an entity based on security records of the entity's network assets, which may be retrieved from the database.

Abstract: 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.

Abstract: 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.

Abstract: A system for determining an entity's security rating may include a ratings engine and a security database. The security database may include a manifest and a distributed index containing security records. Each of the security records may have a key (e.g., a network identifier of a network asset) and a value (e.g., security information associated with the network asset identified by the key). The keyspace may be partitioned into multiple key ranges. The manifest may contain references to segments of the distributed index. Each segment may be associated with a key range and may index a group of security records having keys within the key range. The manifest and the segments may be stored in an object storage system. The ratings engine may determine the security rating of an entity based on security records of the entity's network assets, which may be retrieved from the database.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.