86 results about "Security framework" patented technology

Guiding query creation for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input comprising one or more entity identifiers; gathering information based on the one or more entity identifiers; providing, to a generative artificial intelligence (AI) model, a prompt based on the natural language input and comprising the gathered information; and receiving, from the generative AI model, a response to the prompt.

A decentralized artificial intelligence platform is disclosed that enables secure sharing and execution of AI modules in sensitive application domains. The platform comprises a knowledge module registry for discovering specialized AI modules, a security framework enforcing structural integrity of AI modules, a zero-knowledge proof validation service for verifying usage conditions without exposing private data, and a blockchain-based value chain for managing smart contracts and payments. In operation, an AI agent can fetch a required AI module from the knowledge module registry, prove the right to use it, execute the AI module on local data, and automatically pay the AI module's provider through a blockchain transaction.

Providing generative artificial intelligence (AI)-enabled notebook interfaces for a security framework, including: receiving a request to generate a notebook interface for a security framework monitoring a cloud deployment; generating, in response to the request, the notebook interface, wherein the notebook interface comprises one or more notebook cells for interacting with the security framework, wherein the one or more notebook cells comprise a natural language input cell for querying a generative artificial intelligence (AI) model; and presenting the notebook interface.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

A system and method for privacy-preserving communication validation and multi-attribute trust scoring is disclosed. The system analyzes communication metadata to determine pattern legitimacy by comparing current communication patterns against relationship fingerprints without accessing communication content. The system validates relationship context between communicating parties using interaction graph analysis and historical communication data. Cross-network intelligence correlation compares current patterns against aggregated patterns across voice, email, and messaging services, creating a self-strengthening security framework that recognizes emerging threat patterns while validating legitimate communication behaviors. The system generates comprehensive multi-attribute trust assessments comprising individual trust attribute scores including engagement rate, reliability index, channel preference, temporal pattern, and behavioral pattern, combined into overall trust levels. Trust context is displayed through a user interface presenting simplified, intuitive, and actionable information with progressive disclosure capabilities, enabling informed user decisions while preserving privacy. Communication processing actions provide users with appropriate engagement options tailored to specific trust assessment results.

The invention relates to the technical field of intelligent construction, and discloses an intelligent construction collaborative management platform and method based on digital twinning, and the method comprises the steps: firstly obtaining the practical operation data of a target engineering machine, analyzing the data safety level of the practical operation data to generate a distributed storage path, extracting storage node identifiers in the path, and deploying the storage node identifiers to a digital twinning platform; analyzing a node storage state index, querying a data backup demand and constructing a storage backup framework; then identifying an access request of the storage security framework, verifying a user identity, calculating an authority level index, analyzing an access range, and determining a platform multi-layer access regulation; then monitoring a permission change event, collecting a log, identifying a risk access behavior and calculating an access risk index; and finally, adjusting node encryption configuration according to the risk index, identifying an authority control key, and generating a self-adaptive security policy of the target engineering machinery on the platform. According to the invention, the integrity and safety of construction management can be improved.

A QR Code Verification Engine provides a multi-layered security framework for generating, validating, and authenticating QR codes while preventing tampering, fraud, and unauthorized access. The system embeds a hidden security layer within the QR code using steganographic encoding or invisible watermarking techniques, ensuring detection of any modifications. The hidden layer is encrypted using asymmetric cryptography, allowing only an authorized verification system to extract and validate it. An AI-powered tamper detection module analyzes QR codes for anomalies, while cryptographic hash verification ensures integrity. The system employs biometric authentication, push notification approvals, and contextual security measures to enhance user verification. Dynamic QR codes with expiration rules prevent replay attacks. Secure offline verification allows authentication without network connectivity. The system integrates with financial platforms, web security tools, and real-time fraud detection mechanisms, ensuring a highly secure and scalable QR code validation framework for transactions, identity verification, and access control applications.

The invention relates to the technical field of Internet of Things communication, and discloses a gateway management system and method based on multiple Internet of Things protocols, and the system comprises a processor architecture, a protocol conversion module, a collaborative management module, a unified security framework module and an edge computing module. The method is applied to the system. According to the application, efficient interconnection and dynamic collaborative management of multi-protocol equipment are realized through FPGA module hardware acceleration protocol conversion, a genetic algorithm and a Petr network hybrid task scheduling framework and a unified security mechanism irrelevant to a protocol, and various different mainstream Internet of Things protocols are compatible through a modular processor architecture and a dynamic expansion interface, so that the implementation is convenient, and the implementation is easy. According to the method, the hardware cost is reduced, rapid integration of a new protocol is supported, local and cloud resource allocation is further optimized through an intelligent switching mechanism of edge computing and cloud collaboration, the system stability and the data processing efficiency are improved, and the method is widely applied to scenes such as smart home, industrial Internet of Things and smart cities.

The invention provides a power transmission intelligent inspection image defect identification and intelligent marking system. The system comprises a data layer, an application layer, a system layer and a hardware layer. The data layer is used for managing storage data; the application layer is used for realizing a service logic and user interaction function; the system layer is used for managing an operation environment and a security framework; the hardware layer is used for providing underlying computing power, storage and communication support; according to the invention, multi-modal inspection data and multi-round time sequence analysis are fused, and an integrated intelligent inspection system with high-precision identification, trend perception and visual early warning capabilities is constructed.

The invention discloses a WebRTC-based vehicle-mounted video stream secure transmission management method, and relates to the technical field of real-time communication, and the method comprises the steps: 1, constructing a hierarchical security system, and building a basic security framework; the layered security system comprises an application layer, a transmission layer, a network layer and a physical layer; 2, according to the basic security framework, in the WebRTC interactive connection establishment process, zero-knowledge proof is introduced, access strategy dynamic updating is achieved through a block chain, an authentication access control strategy set is output, and the authentication access control strategy set comprises device access, a bidirectional identity authentication rule and a block chain-based fine-grained access control strategy; 3, on the basis of the authentication access control strategy set, data security is guaranteed through hybrid encryption, the integrity of the Hash tree is verified, and an improved encryption transmission scheme is output; wherein the Hash tree is verified, and anti-jitter and code rate self-adaption are carried out in combination with the LDPC and the DRL.

The present invention introduces an artificial intelligence (AI) system that includes a model, a first model protection system, and a wrapper. The system is designed to enhance security by processing inputs and outputs through a protective layer. Specifically, inputs from users are initially directed by the wrapper to the first model protection system, which then hardens these inputs before they are fed into the model to produce outputs. Subsequently, the outputs are received by the wrapper and passed through the first model protection system again to harden the outputs before they are delivered to users. Additionally, the invention introduces a security assessment system for evaluating the potential risks associated with a target model before its deployment, comprising an assessment system, a red teaming system, and a second model protection system, collectively working to thoroughly assess and mitigate potential security threats. The present invention offers a comprehensive security framework for AI models, covering both the development and deployment phases, and addresses the escalating security challenges facing AI models.

Systems and methods are disclosed to provide a security framework for the authentication of digital content and the prevention of unauthorized modifications, especially targeting the vulnerabilities introduced by deepfake technologies. It innovates by merging reverse engineering with expression manipulation detection to discern genuine from altered digital media. The process involves comparing historical data against synthetic or real-time content, generating a “video mesh” that enables precise manipulation identification. Enhanced by Smart Contracts for each content piece to record and verify changes, this system advances digital identity and transaction security significantly beyond current methodologies. Furthermore, it employs Generative AI within the Identity Intelligent Clip Reviewer to scrutinize blockchain-secured data for manipulation signs, issuing a Proof of Digital Manipulation (PODM) for verified authenticity. This comprehensive method ensures the integrity and trustworthiness of digital interactions across various platforms, marking a significant step forward in the protection against sophisticated cyber threats and unauthorized data alterations.

Techniques are described for generating visualizations of security framework information (such as, e.g., MITRE ATT&CK® information) displayed in connection with notable events detected by a data intake and query system. Data intake and query systems, SIEM systems, and other applications often provide user interfaces that display detected occurrences of incidents, sometimes referred to as “notable events,” within users' information technology (IT) environments. A data intake and query system is described herein that is capable of identifying notable events, assigning tactic and technique information to notable events (and optionally to associated risk objects), and causing display of tactic and technique visualizations which are intuitive and customizable, provide ready access to relevant documentation, and follow a format with which security analysts are familiar.

A system and method for security validation of incoming communications through relationship fingerprints is disclosed. The system analyzes metadata from incoming communications to identify communication patterns without accessing content. Using these patterns, the system validates relationship context between communicating parties through an interaction graph and relationship fingerprints stored in a database. The relationship fingerprints contain temporal patterns, channel preferences, and behavioral patterns that evolve as relationships develop. The system validates incoming communications by matching current patterns against stored relationship fingerprints. Additionally, the system enables cross-service pattern sharing through aggregated patterns that can be normalized and shared across networks while maintaining privacy. The system continuously updates both relationship fingerprints and aggregated patterns as new communications occur, creating an evolving security framework based on relationship patterns.

A system and method for encoding and decoding optical codes with context-aware, multi-level security. Input data is classified into security levels with associated context sensitivity requirements. The system compresses data using public and private codebooks based on security classifications, then generates optical codes incorporating both compressed data and context requirements. When scanned, the system collects environmental contextual data (location, network environment, device security, user behavior), analyzes it against embedded context requirements, and dynamically determines which security levels are accessible in the current environment. Only authorized security levels are decoded using appropriate codebooks based on both user credentials and current contextual factors. This approach enables fine-grained, context-sensitive access control that adapts to changing environments while maintaining the compression benefits and capacity advantages of the multi-level security framework.

Systems and processes are disclosed to monitor and control toxic configuration in software container deployment. AI-based monitor in each node provides end-to-end fault management system that can detect, diagnose, classify, and suggest remediation actions for non-virtualized cloud-based misconfiguration vulnerabilities. Anomalies are diagnosed using pre-computed fault signatures and automated remediation is integrated with a cloud management stack. Multiple monitoring layers within nodes secure the container-based virtualization environment. Container-based “CSTC security framework” virtualization technology provides security and identifies potential toxic configuration threats. CSTC security locates container-based systems at severe risk for DDOS attacks to kernel vulnerability / container breakout and ensures appropriate privilege configuration for user processes. Resource access limited based on policies and CSTC security profiles as well as provide AI-based monitoring of container runtime behavior to provide additional security layer that ensures safety and protection in container-based virtualization.

Systems and methods are disclosed to provide a security framework for the authentication of digital content and the prevention of unauthorized modifications, especially targeting the vulnerabilities introduced by deepfake technologies. It innovates by merging reverse engineering with expression manipulation detection to discern genuine from altered digital media. The process involves comparing historical data against synthetic or real-time content, generating a “video mesh” that enables precise manipulation identification. Enhanced by Smart Contracts for each content piece to record and verify changes, this system advances digital identity and transaction security significantly beyond current methodologies. Furthermore, it employs Generative AI within the Identity Intelligent Clip Reviewer to scrutinize blockchain-secured data for manipulation signs, issuing a Proof of Digital Manipulation (PODM) for verified authenticity. This comprehensive method ensures the integrity and trustworthiness of digital interactions across various platforms, marking a significant step forward in the protection against sophisticated cyber threats and unauthorized data alterations.

Methods and systems for securing distributed systems are disclosed. The distributed systems may include data processing systems subject to compromise by malicious entities. If compromised, the data processing systems may impair the services provided by the distributed system. To secure the distributed systems, the data processing systems may implement a security framework. The security framework may utilize a hierarchy that defines authority for validating trusted entities. The hierarchy may vest authority across the distributed system, and may be based on a reputation (e.g., weighted reputation) of each of the data processing systems within the distributed system. If the reputation of a data processing system meets criteria, the data processing system may be treated as being compromised and a local refresh of security data may be performed. Consequently, the impact of compromise of the data processing system may be limited by the distributed authority.

A method includes receiving multiple security framework requirements, mapping the security framework requirements to a predicate, mapping the predicate to a system-specific implementation, evaluating, using a runtime system, the target system by analyzing a multitude of build files using the system-specific implementation, and presenting a report indicating whether the security framework requirements are satisfied.

Methods and systems for verifying authority in distributed systems are disclosed. Authority may be delegated to various entities within a distributed system to invoke performance of operations within the distributed systems. To verify that authority has been delegated, a security framework may be used that limits computing resource consumption for the verifications. The computing resource consumption may be limited by using cryptographically verifiable data structures that establish chains of delegation of authority. The cryptographically verifiable data structures may be limited in size and content to reduce the computational cost for implementing the security framework.

This invention provides a power transmission intelligent inspection image defect recognition and intelligent annotation system. The system includes a data layer, an application layer, a system layer, and a hardware layer. The data layer is used to manage and store data. The application layer is used to implement business logic and user interaction functions. The system layer is used to manage the operating environment and security framework. The hardware layer is used to provide underlying computing power, storage, and communication support. This invention integrates multimodal inspection data with multi-round time series analysis to construct an integrated intelligent inspection system with high-precision recognition, trend perception, and visual early warning capabilities.

A method for implementing a slice security zone (SSZ) in a 5G network. The method comprises storing by an SSZ function executing on a first network server an SSZ security profile of the SSZ in a secure storage function, receiving by the SSZ function from a slice management function a slice registration request comprising information relating to a slice security profile of a slice managed by the slice management function, if the slice security profile complies with the SSZ security profile, storing by the SSZ function a slice registration association between the slice and the SSZ in the secure storage function, and sending by the SSZ function to the slice management function a slice registration response comprising information relating to whether the slice was registered in the SSZ.

Methods and systems can secure distributed systems. The distributed systems may include data processing systems subject to compromise by malicious entities. If compromised, the data processing systems may impair the services provided by the distributed system. To secure the distributed systems, the data processing systems may implement a security framework. The security framework may utilize a hierarchy that defines authority for validating trusted entities. The hierarchy may vest authority across the distributed system, and may be based on a reputation (e.g., weighted reputation) of each of the data processing systems within the distributed system. If the reputation of a data processing system meets criteria based on active and passive monitoring, the data processing system may be treated as being compromised and a local refresh of security data may be performed. Consequently, the impact of compromise of the data processing system may be limited by the distributed authority.

A security framework for redundancy configuration management ensures that multiplexer control data and redundancy maps distributed to compute modules are authenticated, verified, and securely applied. Configuration packets are cryptographically signed, versioned, and transmitted through an isolated sideband channel. A coordination processor verifies signatures, checks integrity hashes, prevents replay, and applies updates only during redundancy-safe intervals. Logs of verified configurations are stored in secure memory and may be audited through a hierarchical management structure. The invention prevents unauthorized or corrupted redundancy configurations in multi-module compute systems.

Methods and systems for securing distributed systems are disclosed. The distributed systems may include data processing systems subject to compromise by malicious entities. If compromised, the data processing systems may impair the services provided by the distributed system. To secure the distributed systems, the data processing systems may implement a security framework. The security framework may utilize a hierarchy that defines authority for validating trusted entities. The hierarchy may vest authority across the distributed system, and may be based on a reputation (e.g., weighted reputation) of each of the data processing systems within the distributed system. The hierarchy may be dynamically updated over time as new information regarding data processing systems is discovered. Consequently, the impact of compromise of a data processing system may be limited by the distributed authority.

The invention relates to an intelligent network automobile information security multi-source dynamic intrusion protection method, which relates to the field of intelligent network automobile information security, and comprises the following steps: deploying a multi-source dynamic protection model by a vehicle-mounted terminal, a user mobile terminal and a field end edge cloud sub-platform, and constructing an intrusion detection mechanism based on the vehicle-mounted terminal, the user mobile terminal and the field end edge cloud sub-platform, and an intrusion adversarial network defense mechanism is generated. According to the method, the multi-source dynamic protection models are deployed at different terminals, a detection mechanism is established to realize dynamic detection of multi-source intrusion, an adversarial network defense mechanism is generated aiming at abnormal features, detection and defense of multi-source intrusion data can be effectively realized, a security framework model is established at a server, and the security of the multi-source intrusion data is improved. The security of intelligent network automobile information interaction is improved, the potential security risk is reduced, and the security of the parking lot cloud fusion intelligent network connection automobile information system is ensured.

Methods and systems for securing distributed systems are disclosed. The distributed systems may include data processing systems subject to compromise by malicious entities. If compromised, the data processing systems may impair the services provided by the distributed system. To secure the distributed systems, the data processing systems may implement a security framework. The security framework may utilize a hierarchy that defines authority for validating trusted entities. The hierarchy may vest authority across the distributed system, and may be based on a reputation (e.g., weighted reputation) of each of the data processing systems within the distributed system. The hierarchy may be dynamically updated over time as new information regarding data processing systems is discovered. If the reputation of a data processing system meets criteria, that data processing system may be treated as being compromised. Consequently, the impact of compromise of the data processing system may be limited by the distributed authority.

Adding guardrails to generative artificial intelligence (AI)-created workflows, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input; providing the natural language input to a generative artificial intelligence (AI) model; receiving, from the generative AI model, a response to the natural language input, wherein the response comprises data encoding a user interface (UI) widget comprising natural language description of one or more actions determined by the generative AI model based on the natural language input and a selectable element that, when selected by a user, causes the one or more actions to be performed by an entity other than the generative AI model; and presenting the UI widget via the natural language interface.

A system to handle cybersecurity threats is disclosed. The system may include a transceiver and a processor. The transceiver may be configured to receive a request from a user via a user interface rendered on a user device. The request may include a query associated with cyber security. The processor may be configured to render the user interface on the user device, and obtain the query from the transceiver. The processor may parse the query into structured data, and identify a sequence of templated actions based on the structured data and a security framework. The processor may map each templated action into one or more action blocks to perform the templated action, based on a user environment. The processor may prepare a workflow to resolve the query based on the sequence of templated actions and the action blocks for each templated action, and perform a predetermined action.