An AI-based system and method for facilitating management of threats for an organization is disclosed. The method includes receiving one or more inputs captured by a plurality of electronic devices of an organization, determining an AI model based on a type of the one or more inputs, and determining if the one or more inputs correspond to a predefined data range. The method includes detecting one or more threats associated with the organization by using the determined AI model and generating one or more real-time alerts corresponding to the detected one or more threats. Furthermore, the method includes generating one or more recommendations for responding to the detected one or more threats and outputting the detected one or more threats, the generated one or more real-time alerts and the generated one or more recommendations on user interface screen of one or more user devices.

A device may receive security data identifying assets of an entity, security issues associated with the assets, and objectives associated with the assets and may utilize a data model to generate, based on the security data, asset related data identifying mapped sets of security data. The device may process a first portion of the asset related data, with a first model, to calculate an asset risk likelihood score for an asset of the assets and may process a second portion of the asset related data, with a second model, to calculate an asset criticality score for the asset. The device may process a third portion of the asset related data, with a third model, to calculate an asset control effectiveness score for the asset and may combine the scores to generate a security risk score for the asset. The device may provide the security risk score for display.

Techniques for providing network traffic security in a virtualized environment are described. A threat aware controller uses a threat feed provided by a threat intelligence service to establish a threat detection engine on virtual switches. The threat aware controller and threat detection engine work together to detect any anomalous or malicious behavior of network traffic on the virtual switch and established virtual network functions to quickly detect, verify, and isolate network threats.

A computerized system for complying with critical infrastructure protection (“CIP”) standards concerning system configuration changes. The system can be used to automatically identify and track changes to computers on the network, improving system security and CIP compliance reporting. In certain embodiments, the system collects system information on servers and workstations using built-in commands. The configuration profiles of these computers/devices can be archived for audit purposes.

Computer systems and methods are provided for receiving a first authentication request that includes an image of an identification document. A risk value is determined using one or more information factors that correspond to the authentication request. A validation user interface that includes the image of the identification document is displayed. A risk category that corresponds to the risk value is determined using at least a first risk threshold. In accordance with a determination that the risk value corresponds to a first risk category, a visual indication that corresponds to the first risk category is displayed. In accordance with a determination that the risk value corresponds to a second risk category, a visual indication that corresponds to the second risk category is displayed.

Embodiments are disclosed for a method for a security model. The method includes generating a Bloch sphere based on a system information and event management (SIEM) of a security domain and a structured threat information expression trusted automated exchange of indicator information. The method also includes generating a quantum state probabilities matrix based on the Bloch sphere. Further, the method includes training a security threat model to perform security threat classifications based on the quantum state probabilities matrix. Additionally, the method includes performing a machine learning classification of the security domain based on the quantum state probabilities matrix.

This disclosure is related to methods and apparatus used to for preventing malicious content from reaching a destination via a dynamic analysis engine may operate in real-time when packetized data is received. Data packets sent from a source computer may be received and be forwarded to an analysis computer that may monitor actions performed by executable program code included within the set of data packets when making determinations regarding whether the data packet set should be classified as malware. In certain instances all but a last data packet of the data packet set may also be sent to the destination computer while the analysis computer executes and monitors the program code included in the data packet set. In instances when the analysis computer identifies that the data packet set does include malware, the malware may be blocked from reaching the destination computer by not sending the last data packet to the destination computer.

Security related anomalies in the data related to network entities are identified, and a risk score is assigned to each entity based on the anomalies. Visualization data is generated for a color-coded interactive visualization. Generating the visualization data includes assigning each entity to a separate polygon to be displayed concurrently on a display screen; selecting a size of each polygon to indicate one of: a number of security related anomalies associated with the entity, or a risk level assigned to the entity, where the risk level is based on the risk score of the entity, and selecting a color of each polygon to indicate the other one of: the number of security related anomalies associated with the entity, or the risk level assigned to the entity; and causing, the color-coded interactive visualization to be displayed on a display device based on the visualization data.

The systems and methods disclosed herein comprise computer-based platforms configured for automated early-stage application security monitoring for allowing users (e.g., application developers) to make decisions at the early stage of the application development.

A computer implemented method of identifying anomalous behavior of a computer system in a set of intercommunicating computer systems, each computer system in the set being uniquely identifiable, the method including monitoring communication between computer systems in the set for a predetermined baseline time period to generate a baseline vector representation of each of the systems; monitoring communication between computer systems in the set for a subsequent predetermined time period to generate a subsequent vector representation of each of the systems; comparing baseline and subsequent vector representations corresponding to a target computer system using a vector similarity function to identify anomalous behavior of the target system in the subsequent time period compared to the baseline time period, wherein a vector representation of the target system for a time period is generated based on a deterministic walk of a graph representation of communications between the computer systems in which nodes of the graph correspond to computer systems in the set and weighted directed edges between nodes of the graph correspond to a characteristic of communication between pairs of computer systems in the set.