10398results about "Digital data authentication" patented technology

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

A computerized system receives an original prompt that a querying user sends to a Large Language Model (LLM) that is operably connected to organizational data sources of an organization. Instead of executing the original prompt by the LLM, the system obtains user-related organizational context that pertains to characteristics of the querying user, obtains data-related organizational context that pertains to data from which the LLM is expected to obtain information for responding to the original query, and obtains pre-defined organizational policy rules, that indicate which type of users are authorized to access which type of organizational data. Based on the obtained data, the system modifies the original prompt into an adapted prompt. The system sends the adapted prompt, and not the original prompt, to the LLM for processing. The system obtains LLM-generated output from the LLM in response to the adapted prompt, and provides that LLM-generated output to the querying user.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention provides an agent collaborative question-answering method and system based on context awareness and authority control, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring user query, and triggering a user context manager to generate a session state user context object containing a permission label and preference; the central scheduler performs task decomposition based on the query intention and the context, dynamically selects an intelligent agent through a multi-dimensional scoring model and binds a knowledge source; the authority-aware agent execution engine executes triple authority verification and intermediate result desensitization processing in the whole process, and supports agent fault tolerance and audit log recording. The method realizes unification of knowledge security and access flexibility, supports multi-scene adaptive question and answer, and has good expandability and fault-tolerant capability.

The invention provides a data privacy protection method for a data governance system, and belongs to the technical field of data governance, and the method comprises the steps: carrying out the cross verification of multi-source feature data and the unique identification information of an object collected on site, generating an original data set, carrying out the sensitive information recognition and grading, and generating the preprocessing data with a sensitive grade label; core identification information in the preprocessed data is disassembled to generate standardized desensitized data conforming to privacy protection, a bidirectional encryption mapping relation is established between object unique identification information collected on site and the standardized desensitized data, an encryption index is formed, the encryption index and preset multi-dimensional compliance data are fused, and a data fusion result is obtained; generating standardized fusion data; and based on the access token, generating a differential authorization data set divided according to permission granularity, performing privacy disclosure risk assessment, generating a risk level, performing privacy processing on the risk level, and outputting the risk level to a risk control system. And the data management efficiency is improved.

An electronic device can be validated at a circuit-level or device-level to provide supply chain verification of an integrated circuit (IC) product. A modern integrated circuit package can comprise multiple dies, systems and circuitry built from a variety of device-level structures. Device-level verification disclosed herein can confirm that a device-level (sub-) component of an integrated circuit product is sourced by a known or otherwise valid manufacturer. This serves to mitigate or avoid a hacking attempt involving illicit replacement of a component of an IC product by an intermediate handler of the IC product within a supply chain.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

The invention discloses an audit monitoring method and system based on multi-agent dynamic permission grading. The method comprises the following steps: constructing a trusted security basis; generating a dynamic token based on the authority control strategy, wherein the load of the dynamic token comprises an authority identifier of an atomic operation unit defined by the authority control strategy; micro-permission life cycle control is implemented, and when it is detected that any permission recovery triggering condition is met, a corresponding permission recovery operation is triggered; performing full-process auditing and anomaly detection, collecting full-link logs of the system, and performing four-layer collaborative detection; hierarchical response is implemented through a multi-level alarm and intervention mechanism, and optimization is carried out based on audit data. According to the method, a dynamic token, micro-permission atomization and dynamic recovery mechanism is introduced, the permission granularity is controlled in a minimum operation unit, and automatic real-time recovery of the permission is achieved. Through a four-layer cooperative detection model and in combination with a weighted voting decision-making mechanism, known threats, behavior abnormalities, complex attack chains and unknown threats are efficiently dealt with.

Computerized systems and methods for automated Access Permissions Review in an organization. A method includes: (a) receiving a textual description of an Access Review Policy; (b) constructing a trigger for automatically initiating an Access Review process; (c) automatically collecting organizational data that pertains to: (i) roles and positions of users, (ii) user-specific characteristics, (iii) email messages sent and received by users, (iv) description of events in which users had accessed elements of the Organizational Resource. Upon a triggering of the trigger, the method provides to a fine-tuned Large Language Model (LLM), the Access Review Policy and augmentation data from the organizational data, and a prompt instructing the LLM to generate a proposal to revoke, modify, add or maintain the Access Permissions of a particular user, and further instructing the LLM to generate output that describes reasoning and supporting evidence for the proposal.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The present disclosure provides for technology that addresses technological challenges arising in the field of identity verification. In particular, the present disclosure provides for out-of-band identity verification workflows that allow a first user to issue a verification challenge to a second user to confirm the identity of the second user. For example, the first user may receive a communication claiming to be from the second user. Rather than attempting to confirm the identity of the second user in-band, the first user initiates an out-of-band verification challenge to the second user.

Systems and methods are described for tool discovery and ingestion for artificial intelligence (“AI”) agents. An AI platform can discover a first tool specification that includes an action and a description. The first tool specification is ingested to create a first tool object. The first tool object includes the action and an endpoint. Tool labels are determined from the specification and applied to the first tool object. A user interface displays the tool object, and it is added to an AI agent. The AI agent includes a manifest file that is used to execute the AI agent. This includes determining whether the AI agent is authorized to perform the action, and providing the AI agent with access to a tool credential, wherein the tool credential is sent to the endpoint.

The invention relates to the technical field of computers, and discloses a data control method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a data control strategy defined by a data owner, and the data control strategy is constructed based on a quintuple framework and comprises a subject, an object, an environment condition, an operation behavior and an effect; compiling the data control strategy into an intelligent contract code, and generating a source code based on an intelligent contract template; deploying the smart contract code to a block chain network to form a decentralized strategy decision point; intercepting the data access request, triggering the smart contract to verify the access request, and generating an access credential when the verification is passed; and allowing data access based on the access credential, and monitoring a data use behavior to ensure consistency with the data control strategy. According to the method, the fine granularity and the whole-process coverage of data control can be improved, the auditing credibility can be improved, the automation level and the efficiency of a control system can be improved, and the end-to-end safety guarantee capability is enhanced.

The invention discloses a data privacy protection method for an industrial internet platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

A private identity system performs on-device embedding and homomorphic tokenization (HT) to map plaintext inputs to non-invertible HT tokens. During enrollment, HT tokens are stored and a centroid can be computed for each user. During prediction, a new HT token shortlists nearest centroids, followed by 1:1 distance verification against stored embeddings. On success, the system returns a UUID bound to the user. Because passkeys can be shared via device or account keychains, the UUID binds the ceremony to the same enrolled individual without exposing biometrics.

The invention relates to a method and system for encrypting and isolating storage data of a credential mobile terminal, and belongs to the technical field of information. The method comprises the following steps: encrypting and partitioning the whole storage area, and setting an access strategy according to user permission; when a user requests to access, executing multi-factor authentication containing a password and an external hardware certificate, and adding biological characteristic authentication; after the authentication is passed, an encryption key is taken from the domestic security chip; and encrypting and decrypting data by using the secret key, and opening corresponding partition access according to authority. The system comprises a storage partition module, an identity authentication module, a key management module, an encryption and decryption module and an authority control module and is used for executing the method. The method also comprises the steps of equipment startup trusted boot and firmware upgrade verification, is based on a custom curing system, and is compatible with Android 10 +. The problems of poor hardware controllability and positioning security risk of the creative mobile terminal are solved, and full-link data security protection is realized.

When AI agents perform various processes, different levels of authentication are required for each type of process. However, conventional methods only allowed for uniform authentication, making it difficult to balance security and convenience. [Solution] The AI ​​agent (10) identifies the type of process to be executed, and the authentication matching unit (30) applies matching conditions (expiration date, authority, time difference, rule version, user ID, etc.) according to the type of process to match the authentication status of the external authentication infrastructure (60). The execution control unit (40) determines whether or not to execute based on the matching result, and the audit record unit (50) generates an audit record in hash chain format that can detect tampering. This allows for the automatic application of the optimal level of authentication strictness for each type of process, achieving both security and convenience.

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and / or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Systems, methods, and non-transitory computer readable media including instructions for providing private answers to silent questions are described. Providing private answers to silent questions includes receiving signals indicative of particular facial micromovements in an absence of perceptible vocalization; accessing a data structure correlating facial micromovements with words; using the received signals to perform a lookup in the data structure of particular words associated with the particular facial micromovements; determining a query from the particular words; accessing at least one data structure to perform a look up for an answer to the query; and generating a discreet output that includes the answer to the query.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions level. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of resources that are ingested for use in the artificial intelligence (AI) agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

Systems and methods involving various registration and authentication workflows are disclosed herein. A user may be authenticated without the use of static usernames or passwords. In some embodiments, an authentication identifier may be generated that is associated with an authentication request for a user to access a protected resource (e.g., a web app). An authentication code may be generated based on the authentication identifier. The authentication code may be sent to a computing device to be provided to the user, who may provide the authentication code to an application on their mobile device. The mobile device may send a payload containing the authentication identifier, credentials saved on the user device from a previous registration step, and a digital signature. The digital signature may be authenticated using contents of the payload before validating the authentication identifier.

The invention provides a multi-modal data desensitization method, system and device and a medium, and belongs to the technical field of information security. The method comprises the following steps: firstly, acquiring multi-modal original data based on preset access information and acquisition frequency, and after standardized preprocessing, positioning sensitive information by using a classification recognition algorithm and generating an analysis report. Then, according to a report, matching a strategy from a desensitization strategy library bound with the sensitivity level, adjusting a dynamic confusion algorithm parameter to desensitize, integrating and generating desensitized multi-modal data, and recording and storing a mapping relationship between the original data and the desensitized data; and if a restoration request is received, after authorization verification is passed, reverse desensitization restoration data is carried out by using the mapping relation. Besides, full-process feedback information is collected, the desensitization effect is quantitatively evaluated according to a preset index, the desensitization strategy and algorithm are iteratively optimized accordingly, and effective desensitization and safety management of multi-modal data are achieved.

The invention provides a molecular identity authentication system and method based on DNA origami framework layered assembly. The molecular identity authentication system comprises a disclosed skeleton chain and a plurality of shared staple chains, the shared staple chain comprises staple chains shared by three groups of single bodies and staple chains shared in pairs, and the staple chains are used as secret keys to be distributed to three participants so as to prepare the three groups of single bodies respectively, and then the three groups of single bodies are combined and spliced into a tripolymer with a dot matrix pattern, so that multi-user collaborative layered chain type assembly is realized. According to the molecular identity authentication system based on DNA origami framework layered assembly, the system combination complexity is improved to the information theory security level, so that the biological information security protection capability is improved, and brute force cracking is prevented.

The invention provides an authority management method based on MCP and RBAC models, and relates to the technical field of authority management, comprising: acquiring a tool description set, a system cue word and a user task; and outputting a task response corresponding to the user task by using the MCP service layer. The MCP service layer comprises an MCP Host, a plurality of MCP Clients, a plurality of MCP Servers and a large language model; the MCP Host is used for acquiring user input and sending a response result corresponding to the user input to the user; the MCP Client runs in the MCP Host and is responsible for establishing connection with the MCP Server and communicating with the MCP Server; the MCP Server is used for generating an available tool description and selecting and executing a called tool; the large language model is used for generating tool calling description and generating task responses corresponding to user tasks.

The invention relates to an electric power mobile application security protection method based on a swan gap microkernel, which comprises the following steps of: S1, establishing a hardware trust root, and storing a device root key and a certificate chain by utilizing a non-tampering storage area built in a chip; s2, establishing a complete trusted boot chain based on a hardware root of trust, and verifying the integrity and credibility of the next layer by each layer from Bootloader to a swan microkernel and then to an application program; s3, constructing a triple authentication system based on user identity, equipment identity and biological characteristics, and performing user authentication; s4, according to a user authentication result, realizing fine-grained authority control based on an RBAC model and ABAC, and ensuring that authority check can be executed during resource access every time by utilizing an IPC security mechanism of a swan micro kernel; and S5, creating an independent execution environment for each power application according to the process isolation capability of the swan microkernel, and ensuring that memories among the applications cannot be mutually accessed through virtual address space isolation. The reliability of power mobile application safety protection is effectively improved.