48 results about "Threat mitigation" patented technology

A method of mitigating the theft of a portable electronic device communicatively connected to a second portable electronic device via a communications subsystem comprising a short range wireless network. Upon detecting that predetermined theft mitigation criteria have been met activating threat mitigation measures including at least locking the screen of the potentially stolen device and prompting the potentially stolen device to emit an alarm tone to alert bystanders.

A computer-implemented method, computer program product and computing system for accessing interface rules for a remote resource; generating a connector interface based, at least in part, upon the interface rules; and accessing the remote resource via the connector interface

A computer-implemented method, computer program product and computing system for receiving a result set; providing the result set to a first prompt / generative AI model pair to generate a first output; providing the result set to at least a second prompt / generative AI model pair to generate at least a second output; and providing the first output and the at least a second output to a large language model to define a superior output chosen from the first output and the at least a second output.

A computer-implemented method, computer program product and computing system for defining a target result set size; executing an initial search on a data set to generate an initial result set; comparing the size of the initial result set to the target result set size; if the size of the initial result set is compatible with the target result set size, providing the initial result set to a requesting entity; and if the size of the initial result set is not compatible with the target result set size, revising the initial search to generate a revised search that is executed on the data set to generate a revised result set.

A computer-implemented method, computer program product and computing system for receiving a plurality of detection events concerning a plurality of security events occurring on a security-relevant subsystem within a computing platform; identifying two or more associated detection events included within the plurality of detection events; and grouping the two or more associated detection events to define a security incident.

Systems and methods for mitigating DNS amplification attacks are provided. In one example, a threat intelligence system collects data about the requests received by a DNS server, and / or responses generated by the DNS server. The threat intelligence system triggers a threat mitigation action upon detecting evidence (in one or more forms) of a DNS amplification attack. The threat mitigation action may include filtering DNS responses generated by the DNS server. The filtering rule may indicate that a DNS response in which the payload size is above a threshold payload size is to be dropped. In examples, the payload threshold size is dynamically set by the threat intelligence system using a machine learning model to minimize the filtering of DNS responses for valid DNS queries, while maximizing filtering of DNS responses for malicious DNS queries.

Various techniques for providing a DNS Early Threat Executive Response System (DETERS) are disclosed. In some embodiments, DETERS is a comprehensive DNS threat detection, response, and reporting system with a modular analytics architecture that allows for early detection of suspicious activity in near real-time. DETERS can identify threats before they are able to spread or compromise systems. DETERS uses a combination of streaming and batch processing, as well as historical DNS information. The DNS-centric design allows a DNS resolver to quickly mitigate threats and for the reporting system to alert users allowing them to take further actions that are reflected in the DNS resolver response policy.

A computer-implemented method, computer program product and computing system for receiving a result set; providing the result set to a first prompt / generative AI model pair to generate a first output; providing the result set to at least a second prompt / generative AI model pair to generate at least a second output; and providing the first output and the at least a second output to a large language model to define a superior output chosen from the first output and the at least a second output.

Disclosed herein are methods and systems for improving endpoint device security. An agent deployed to the endpoint device may inject monitoring code into a program during runtime of the program. The monitoring code instructs the program to generate runtime telemetry data. The agent may detect behavior indicative of a security risk based on the runtime telemetry data. The agent may automatically perform at least one remedial action of a plurality of remedial actions, wherein the plurality of remedial actions comprises: notifying a user of the endpoint device of the behavior indicative of the security risk, terminating the program, blocking one or more functions of the program, and reporting the behavior indicative of the security risk to a server system.

Various technologies for providing a DNS Early Threat Executive Response System (DETERS) are disclosed. In some embodiments, DETERS is a comprehensive DNS threat detection, response, and reporting system with a modular analytical architecture that enables near real-time early detection of suspicious activity. DETERS can identify threats before they can spread to the system or gain unauthorized access to it. DETERS utilizes a combination of streaming and batch processing along with historical DNS information. The DNS-centric design allows DNS resolvers to quickly mitigate threats and reporting systems to alert users, enabling users to take further action reflected in DNS resolver policies.

A computer-implemented method, computer program product and computing system for defining a target result set size; executing an initial search on a data set to generate an initial result set; comparing the size of the initial result set to the target result set size; if the size of the initial result set is compatible with the target result set size, providing the initial result set to a requesting entity; and if the size of the initial result set is not compatible with the target result set size, revising the initial search to generate a revised search that is executed on the data set to generate a revised result set

A method for controlling a vehicle is disclosed. The method comprises determining whether one or more surrounding vehicles exhibit a threatening behavior, and controlling the vehicle to perform one or more threat-avoidance actions when it is determined that one or more surrounding vehicles exhibit a threatening behavior. Example threatening behaviors include a first surrounding vehicle directly in front of the vehicle decelerating without a traffic-related reason for deceleration, and, in combination with the former, a second surrounding vehicle in a lane adjacent to the vehicle decelerating in association with the first surrounding vehicle. Corresponding computer programs, computer program products, control units, devices, systems, and vehicles are also disclosed.

A system, method, and computer-program product includes detecting an electronic communication transmitted to a message storage repository monitored by a threat detection and response service, retrieving unstructured message data of the electronic communication in response to detecting the transmission of the electronic communication to the message storage repository, transforming the unstructured message data of the electronic communication into a structured message data object interpretable by the threat detection and response service, assessing the structured message data object that corresponds to the electronic communication against a set of subscriber-agnostic threat detection instructions provided by the threat detection and response service and a set of subscriber-composable enrichments that include subscriber-composed threat detection instructions, automatically detecting the electronic communication as malicious based on the assessment, and executing a threat mitigation action that mitigates a security threat associated with the electronic communication.

Systems and methods that create, use, enhance, maintain, and otherwise optimize a threat model—generally used for risk evaluation and threat mitigation—comprising artificial intelligence inherent in an entity is described. Certain embodiments describe, in countering a threat event, a need for an artificial intelligence entity to cooperate with non-expert users to give the users abilities to act on the domain in the users' self-interest. In countering a threat event, certain other embodiments describe that no single actor, in a heterogeneous collection of actors with varying abilities, may act in isolation to efficiently and effectively counter the threat to the collection; a minimum inevitable loss for the threat event may be achieved by an active cooperation of the heterogeneous actors of type comprising at least one of: expert users, non-expert users, and artificial intelligence entities that are sufficiently trained and knowledgeable on the threat event.

Processes and systems for identifying and mitigating threats to critical assets leverage interactive 3D modeling systems, such as gaming engines, to generate customized interactive 3D critical asset site models that provide interactive visual displays of multiple potential threats, multiple potential mitigation measures, and the effectiveness of the each of the potential threat mitigation features with respect to protecting existing and future critical assets and / or critical asset sites.

A computer-implemented method, computer program product and computing system for identifying an event that concerns a network entity on a computer platform; obtaining entity data for the network entity from a plurality of data sources, thus defining a plurality of network entity data portions; and combining the plurality of network entity data portions to form consolidated network entity data for the network entity.

A comprehensive non-lethal defense system using a 5-pound CO2 fire extinguisher and detailed training protocols is provided. The defense system includes a standard fire extinguisher that is repurposed for threat mitigation. The defense system also includes a frost-free horn that Prevents frostbite on the hands of the user during operation the standard fire extinguisher. The defense system also includes a trigger mechanism with a safety lock that allows for controlled bursts and prevents accidental discharge from the standard fire extinguisher. The defense system also includes an ergonomic handle that ensures secure grip and stability under stress corresponding the standard fire extinguisher.

The disclosure relates to a method and system for dynamically mitigating threats of generative Artificial Intelligence (AI) models. Conventional systems often suffer from inefficiencies due to sequentially applying threat detection checks leading to unnecessary preprocessing and increased computational demands. Additionally, such systems typically focus only on input data, neglecting potential threats in outputs. The disclosed system and method addresses these drawbacks by employing a hierarchical structure of macro and nano classifiers. The system utilizes macro classifiers for broad initial threat categorization followed by specialized nano classifiers for detailed analysis of specific threat subtypes, thereby optimizing processing time and computational resources. The system operates in real time, applying predefined moderation rules to both input and output data to ensure comprehensive threat mitigation. Additionally, continuous telemetry data updates refine nano classifiers and threat identification mechanisms, maintaining high accuracy and adaptability. The disclosed method enhances safety efficiency and reliability of generative AI models.

This disclosure describes techniques for performing fingerprinting of network devices, where the fingerprinting is capable of providing a high definition and clear picture of the network device and / or the identity of the operator of the network device. In one example, this disclosure describes a method that includes receiving, over a network from a user device, a first set of fingerprint data; generating, based on the first set of fingerprint data, a first threat assessment associated with the user; receiving, over the network from the user device, a second set of fingerprint data; generating, by the computing system and based on the first set of fingerprint data and the second set of fingerprint data, a second threat assessment; and sending, by the computing system and based on the second threat assessment, control signals to a system on the network to cause the system to implement a policy threat mitigation policy.

A computer-implemented method, computer program product and computing system for deploying an agent to proactively monitor activity within a computing platform and generate an initial notification if a security event is detected; receiving the initial notification of the security event from the agent, wherein the initial notification includes a computer-readable language portion that defines one or more specifics of the security event; and iteratively processing the initial notification using a generative AI model and a formatting script to produce a summarized human-readable report for the initial notification.

A computer-implemented method, computer program product and computing system for deploying an agent to proactively monitor activity within a computing platform and generate an initial notification if a security event is detected; receiving the initial notification of the security event from the agent, wherein the initial notification includes a computer-readable language portion that defines one or more specifics of the security event; and iteratively processing the initial notification using a generative AI model and a formatting script to produce a summarized human-readable report for the initial notification.

Disclosed are methods and systems for assessing system risks associated with one or more assets coupled to a network and mitigating against said system risks. According to one implementation, a method for assessing a system risk comprises receiving network data associated with a first asset communicatively coupled to a network, quantifying the network data, and generating a risk parameter using the quantified network data and vulnerability data associated with the network. The method further comprises determining, based on the risk parameter that a security risk of a first asset of the network is higher than the security risk of a second asset of the network. Upon this determining, the method initiates remediation operations that first secure the first asset on the network against a security breach to the network.

A system and method for generating event-specific handling instructions for accelerating a threat mitigation of a cybersecurity event includes identifying a cybersecurity event; generating a cybersecurity event digest based on the cybersecurity event, computing a cybersecurity hashing-based signature of the cybersecurity event based on the cybersecurity event digest; searching, based on the distinct cybersecurity hashing-based signature of the cybersecurity event, an n-dimensional space comprising a plurality of historical cybersecurity event hashing-based signatures; returning one or more historical cybersecurity events or historical cybersecurity alerts homogeneous to the cybersecurity event based on the search; deriving one or more cybersecurity event-specific handling actions for the cybersecurity event based on identifying a threat handling action corresponding to each of the one or more historical cybersecurity events or historical cybersecurity alerts homogeneous to the cybersecurity event; and executing one or more cybersecurity threat mitigation actions to resolve or mitigate the cybersecurity event.

Processes and systems for identifying and mitigating threats to existing critical assets leverage interactive 3D modeling systems, such as gaming engines, to generate customized interactive 3D critical asset site models that provide interactive visual displays of multiple potential threats, multiple potential mitigation measures, and the effectiveness of the each of the potential threat mitigation features with respect to protecting existing and future critical assets and / or critical asset sites.

A computer-implemented method, computer program product and computing system for selecting two or more generative AI nodes from a plurality of generative AI nodes; and arranging the two or more generative AI nodes to form a generative AI workflow.

A computer-implemented method, computer program product and computing system for receiving an alert concerning an event within a computer platform; autonomously defining an investigation / remediation plan for addressing the event within the computer platform based upon one or more available resources; autonomously executing the investigation / remediation plan to address the event within the computer platform; autonomously determining an efficacy level for the investigation / remediation plan; and autonomously effectuating a remedial action based, at least in part upon the determined efficacy level.

A system and methods for enhanced cybersecurity using an automated planning service is provided, comprising an automated planning service that receives a network analysis job, constructs a simulation using known network and threat information, assigns individual actions to worker nodes for processing, analyzes the results and records new threat mitigation plans as they are produced to expedite future operations, and produces an action plan as output.

The present disclosure provides methods, systems and storage media for conducting a security review of a system. Certain examples relate to the use of trained generative AI to generating a root security query using a machine learning (ML) generator, based on a system description. A security requirement associated with the root security query is extracted, and an indication of the root security query is output at a user interface. A user input is received in response, and the ML generator generates a follow-up request that is output via the user interface. A second user input is received in response to the follow-up request, and the ML generator then determines that the security requirement is not satisfied by the target system.

The disclosure relates to a method and system for dynamically mitigating threats of generative Artificial Intelligence (AI) models. Conventional systems often suffer from inefficiencies due to sequentially applying threat detection checks leading to unnecessary preprocessing and increased computational demands. Additionally, such systems typically focus only on input data, neglecting potential threats in outputs. The disclosed system and method addresses these drawbacks by employing a hierarchical structure of macro and nano classifiers. The system utilizes macro classifiers for broad initial threat categorization followed by specialized nano classifiers for detailed analysis of specific threat subtypes, thereby optimizing processing time and computational resources. The system operates in real time, applying predefined moderation rules to both input and output data to ensure comprehensive threat mitigation. Additionally, continuous telemetry data updates refine nano classifiers and threat identification mechanisms, maintaining high accuracy and adaptability. The disclosed method enhances safety efficiency and reliability of generative AI models.

To provide a threat handling system and a threat handling method handling a coming flying threat by laser radiation effectively.SOLUTION: A threat handling system (1) comprises a handling device (5), a block device (6) and a determination device (50). The handling device (5) detects a coming flying threat (4) and handles the threat (4) by laser radiation. The block device (6) is installed to prevent the threat (4) from passing over a block region (20) where the power of the laser radiation is estimated to be lower than a prescribed threshold value and coming close to an object (3) to be protected. The determination device (50) determines a position of an upper end of the block device (6) on the basis of the upper end of the block region (20).SELECTED DRAWING: Figure 2A