4608results about "Computer security arrangements" patented technology

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.

The disclosed embodiments provide systems and methods for continuous exposure and attack surface management using a data fabric. Data from multiple heterogeneous cybersecurity sources, including vulnerability scanners, threat intelligence, cloud security tools, and endpoint monitoring systems, is ingested and integrated into a semantically harmonized representation, such as a security knowledge graph. This unified data model normalizes, correlates, and contextualizes diverse cybersecurity information, enabling comprehensive and real-time assessment of an organization's cybersecurity risk posture. Automated workflows trigger proactive remediation actions based on dynamically calculated exposure metrics. Additional embodiments leverage the same data fabric architecture to support specialized cybersecurity use cases, including unified vulnerability management (UVM), cyber asset attack surface management (CAASM), continuous threat exposure management (CTEM), and asset exposure management (AEM).

A computer-implemented method for jailbreaking Large Language Models (LLMs) requiring only black-box access-Branch-and-Prune. LLMs are powerful tools displaying many capabilities. However, despite safety training, LLMs can generate harmful, biased, and toxic content-demonstrated by the prevalence of human-designed “jailbreaks” that override LLM's safety “guardrails.” At the core, the method engages an “attacker” LLM into conversations where it generates variations of the original prompt that may jailbreak the target LLM. Compared to prior methods, the disclosed Branch-and-Prune adaptively decides which conversations to engage in (pursuing conversations with a high-likelihood of success while abandoning less-promising ones), and “mixes” the different conversations (increasing the success rate of the method). This enables jailbreaks for state-of-the-art LLMs for over 80% of the existing harmful prompts while requiring fewer queries to the target LLM.

The disclosed embodiments provide systems and methods for automated mapping of raw data into a data fabric. An innovative approach leveraging Artificial Intelligence (AI)-powered tools and a data fabric to automate the ingestion, transformation, and integration of raw data into a unified model is introduced. By automating the data mapping process, organizations can reduce reliance on manual methods and accelerate their ability to utilize robust insights for exposure management and attack surface reduction. The disclosed solution provides a scalable architecture for unifying cybersecurity signals across cloud and hybrid environments, enabling real-time decision-making and improved organizational resilience against cyber threats

Data is received which includes multimodal input for ingestion by a first generative AI (GenAI) model is received. This received data is input into the first GenAI model to result in a first output. The first output along with the received data is input into a second GenAI model to result in a second output. The first GenAI model is a modified (e.g., fine-tuned, etc.) version of the second GenAI model. When the second output indicates that guardrails associated with the second GenAI model have been triggered, one or more remediation actions are initiated. Otherwise, the first output is returned to the requestor. Related apparatus, systems, techniques and articles are also described.

Systems and methods include discovering and classifying any of data discovered by inline cloud inspection, data stored across one or more cloud services, and data stored across one or more endpoints; continuously monitoring access to and usage of classified data, wherein the monitoring is performed in real-time and includes analyzing data access patterns, user behaviors, and application interactions; evaluating a security posture of the classified data by identifying misconfigurations, compliance violations, excessive permissions, and vulnerabilities; and enforcing one or more security policies based on the evaluated security posture.

An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.

An identity management system may obtain a set of data signals via a system log application programming interface (API). The set of data signals may be associated with interactions between a user of a client device and one or more applications associated with the identity management system. The identity management system may then output a set of text strings that include parsed data from the set of data signals to a large language model (LLM) that is affiliated with a multi-modal machine learning model configured to generate risk metrics based on data signals. A first risk metric may then be obtained from the LLM. Further, the identity management system may generate a second risk metric by using a risk combinator of the multi-modal machine learning model. As such, the identity management system may generate a unified risk metric based on the first risk metric and the second risk metric.

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

Systems, methods, and computer program products are disclosed for running applications in a serverless environment. Running applications in a serverless environment includes configuring a serverle96ss environment by integrating a set of rules into the serverless environment, the set of rules including at least one trigger and at least one trigger-responsive action for use in conjunction with a SaaS platform; obtaining a general address for the serverless environment; using the general address for conveying code to the configured serverless environment, for combination in an immutable manner with configuration information, thereby enabling formation of an immutable image, wherein the configuration information is adapted to invoke the at least one trigger, thereby initiating the at least one trigger-responsive action; and receiving a specific address in the serverless environment for reaching an actuated instance of the immutable image for incorporation with the SaaS platform.

A cyber security system is adapted to contextualize and visualize cloud architectures featuring ephemeral cloud assets with generation of a cloud asset remediation plan to group alerts for handling based on security team responsibilities.

Systems and methods for detailed cloud posture remediation recommendations utilizing custom Large Language Models (LLMs). The present systems and methods are configured to perform the steps of scanning a cloud environment for posture control data; generating one or more alerts related to any of risky configurations and risky activities associated with the cloud environment; generating one or more remediation recommendations based on the one or more alerts; and providing the one or more alerts and the one or more remediation recommendations to administrators of the cloud environment.

A computer implemented method for governing risk actions by an artificial intelligence (AI) browser, by classifying a proposed action by the AI browser based on a large language model (LLM) as safe or risky based on AI weights or based on policy rules; initiating a step up authentication flow for a risk action; presenting an action summary and required capabilities to the user for approval; and enforcing user configured spend or scope limits on the risk action.

The present application relates to a federated learning method and a first device, and a third device. The method includes: sending, by a first device, a first key to a second device, wherein the first key is configured to encrypt inference information of a second model in the second device to obtain first encrypted inference information; and obtaining, by the first device, target information based on inference information of a first model in the first device and second encrypted inference information corresponding to the first encrypted inference information, in response to receiving the second encrypted inference information.

A method for packaging digital evidence for long term validation comprises forming a package of a digital document (10), an electronic signature (12) for the document (10), together with evidence (16) of the authority of the signature in the document and a time stamp (20) indicating when the document was digitally signed. All of the pieces form parts of the packaged evidence.

The present disclosure provides a binary code similarity detection system based on hard sample-aware momentum contrastive learning, comprising: a preprocessing device for transforming the binary code into tokens for neural networks; a feature extracting device for using the tokens to generate final representation embeddings for the binary code; and a similarity detection device for using the representation embeddings to detect the binary code similarity.

The present disclosure describes systems and methods for detecting and preventing data misconfigurations within a security-focused data fabric platform. The system integrates an advanced script migration engine designed to streamline the translation of security rules and scripts across different scripting languages while ensuring alignment with the fabric's unified schema. The method involves receiving inputs from data sources, mapping these inputs to entities of a target schema, monitoring real-time data changes, and simulating impacts on operational dependencies to detect misconfigurations proactively. Leveraging AI-driven mechanisms, including Large Language Models (LLMs), the system dynamically identifies breaking changes in third-party data streams, issues alerts, and provides suggested fixes. The script migration engine further enhances the platform's functionality by automating cross-platform script translations and enabling faster onboarding of security tools. Together, these innovations ensure scalable, accurate, and resilient integration and management of security data across heterogeneous sources, strengthening operational integrity and minimizing security risks.

Various embodiments include systems and methods for generating a prompt for a large generative AI model (LXM). A computing device may be configured to receive a user prompt, obtain user context information from one or more sources of physical context information and user background information, use the received user prompt and the obtained user context information to generate a contextualized prompt for submission to an LXM, and submit the generated contextualized prompt to the LXM

Embodiments are directed to methods and systems that can be used to perform efficient, parallel, privacy-preserving graph analysis. One particular application of embodiments is performing private cycle detection in order to detect anomalous behavior in directional electronic communications. Two (or more) parties can each possess private electronic communication data, which can be used to construct a private directed union graph corresponding to the union of the parties' electronic communication data. This private union graph can be analyzed by a multi-party computation network in order to detect cycles of defined length (e.g., comprising between four and eight communicating participants). These cycles can be used as evidence of anomalous or illicit use of such electronic communications systems.

A method of building a privacy-preserving neural network model is provided. The method includes: performing first neural network operations using a non-private neural network of the privacy-preserving neural network model based on first input data in plaintext to produce first output data in plaintext, whereby the non-private neural network is pre-trained and learnable parameters of the non-private neural network are fixed while performing the first neural network operations; encrypting the first output data from the non-private neural network using a homomorphic encryption method to produce first encrypted data; and performing second neural network operations homomorphically using a private neural network of the privacy-preserving neural network model based on the first encrypted data to produce second encrypted data, whereby learnable parameters of the private neural network are trained while performing the second neural network operations. There is also provided a corresponding system for building a privacy-preserving neural network model, as well as method and system for performing privacy-preserving prediction using the privacy-preserving neural network model built.

Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.

Methods, systems, and computer storage media for providing attack path discovery management using an attack path discovery engine of a security management system. Attack path discovery management supports automatic attack path discovery that involves identifying and mapping potential pathways that attackers could use to infiltrate computing environments. In operation, an attack path discovery computation model comprising an entry point element, an advancement step element, and a target element, is accessed. A computing environment graph comprising computing components of a computing environment is accessed. Based on the entry point element, an entry point is identified in the computing graph; based on the advancement step element, an advancement step is identified in the computing environment graph; and based on the target element, a target is identified in the computing environment graph. An attack path is generated based on the entry point, the advancement steps, and the target. The attack path is communicated.

A method and system for protecting sensitive data in a generative AI system is disclosed. The method includes encrypting sensitive data fields at a field level using a first encryption proxy in a data store, labeling the encrypted data fields with metadata in the data store, interfacing the data store to the generative AI system, receiving a user prompt at a second proxy, sending the user prompt to the generative AI system, generating a response by the generative AI system, receiving the response from the generative AI system, and selectively decrypting, by the second proxy, sensitive data in the response based on user authorization. In some embodiments, the metadata comprises encryption keys information and access control policies.

The invention proposes to generate a new MAC address for a non-AP station while being associated with an AP station, for use when associating again with the same AP station. For privacy reason, the same new MAC address is generated locally at both the non-AP station and the AP station using a shared function. This is done in a synchronized manned, preferably at the end of the association, e.g. when disassociating. The non-AP station disassociates with the AP station, and at any time in the future, it may associate again with the same AP station using this new MAC address. The AP station is therefore able to recognize the non-AP station, while outside observers only see a new MAC address and thus see a new non-AP station associating with the AP station. User privacy is therefore reinforced over time as the non-AP station associates multiple times to the same AP.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

A method of secure communication is provided. The method can include receiving a message from a first endpoint according to a first communication protocol, translating the message from the first communication protocol to a second communication protocol, and transmitting the message via a communication mode according to the second communication protocol. The method can further include translating the message from the second communication protocol to a third communication protocol and sending the message to a second endpoint according to the third communication protocol.

The disclosed embodiments provide systems and methods for performing queries via an intelligent query engine. Various embodiments include receiving an input query and parsing the input query into a query representation object; generating an evaluation plan based on the query representation object, wherein the evaluation plan comprises a graph of computation nodes, each computation node specifying a granularity for grouping, associated filters, an aggregation schema, and join dependencies required for node computation, and wherein the evaluation plan is ordered to account for hierarchical dependencies among the computation nodes; translating the evaluation plan into an executable query optimized for a specific target data store, wherein the translation adapts query syntax and join structures to capabilities of a target data store; and executing the translated query on the target data store, resolving dependencies and aggregations according to the evaluation plan to generate query results satisfying the input query.

In particular embodiments, a consent notice configuration determination system may be configured to: (1) scan a particular website from a plurality of different locations; (2) identify one or more types of technologies available on the particular website for individuals accessing the particular website from each of the plurality of different locations; (3) determine, for each of the plurality of different locations, based on a global set of databases, legal / regulatory guidance, etc. and the one or more types of technologies, particular legal and industry requirements for each of the plurality of different locations; and (4) automatically configure, for each of the plurality of different locations, a consent interface for the particular website for each of the plurality of different locations based at least in part on the one or more types of technologies and the global set of databases.

Examples analyze effective permissions in a role-based access control system. A prompt is created for a large language model (LLM). The prompt includes a role definition for a role of a role-based access control system, and action definitions. The role definition for the role includes an action and effective permissions text describing a summary of permission limitations provided by the role. The action definitions are provided in a hierarchical format. Query text is added to the prompt. The query text includes a question about effective permissions associated with the role. The prompt is submitted to the LLM, thereby generating response text from the LLM. The response text is displayed to a user.