AI-powered cloud compliance system for data and mapping services for autonomous vehicles

Abstract

A cloud-integrated, AI-based compliance control system for data and mapping services of autonomous vehicles (AVs), consisting of: a data acquisition unit configured to acquire, aggregate and preprocess sensor data from autonomous vehicle subsystems, including LiDAR scanners, radar arrays, camera systems and telemetry transmitters, wherein the data acquisition unit includes a synchronization circuit for aligning temporal sensor data streams and a metadata tagging circuit configured to add position coordinates, time identifiers and authentication data to each data record; a compliance processing unit that is operationally connected to the data acquisition unit and configured to execute artificial intelligence-based compliance techniques stored in a storage unit, wherein the compliance processing unit is further configured to interpret the relevant data protection, privacy and mapping laws and generate compliance control signals that define permissible data routing, encryption and storage parameters; a classification unit that is operationally connected to the compliance processing unit and is configured to analyze the data content using tensor computational hardware to identify sensitive, restricted, or public data and assign compliance classification identifiers to each data packet; a policy control unit that is operationally connected to the compliance processing unit and is configured to enforce compliance control signals by dynamically adjusting the activation of encryption keys, routing permissions, and cloud storage allocations according to the compliance classification identifiers; a data provenance tracking unit operationally connected to the policy control unit and comprising a cryptographic hash generation circuit configured to record compliance check events, data transformations, and access transactions in a chained, in-memory ledger; and A compliance check device that is structurally integrated between the data acquisition unit and a cloud communications controller. This compliance check device includes a compliance processor, a secure cryptographic enclave, and a transmission control circuit configured to authorize, block, or redirect cloud-bound data packets based on compliance control signals received from the compliance processing unit.

Description

Technical field of the invention

[0001] The present invention relates to systems and methods for ensuring compliance with regulatory, ethical, and data protection requirements in cloud-based environments for data management and mapping services of autonomous vehicles. In particular, the invention relates to an AI-supported cloud compliance system that integrates the automated interpretation of policies, real-time monitoring, data provenance tracking, and adaptive rule enforcement in distributed cloud architectures used for storing, processing, and distributing AV-generated data, including sensor telemetry, imagery, and map datasets. Background of the invention

[0002] Autonomous vehicles (AVs) require large amounts of highly accurate sensor data, including LiDAR scans, radar reflections, GPS telemetry, and high-resolution camera images. This data is continuously uploaded to cloud platforms for processing, analysis, and map updates, enabling real-time navigation, environmental perception, and decision-making. However, storing and using AV data in the cloud presents significant challenges regarding compliance with regional data privacy laws (such as GDPR, CCPA, and the Indian DPDP Act), national mapping restrictions, and cross-border data transfer regulations.

[0003] Existing cloud storage and data processing systems generally treat compliance as an add-on function, implemented through rule-based access control and manual audits. Such systems are inherently reactive and lack continuous and adaptive enforcement mechanisms capable of addressing dynamically changing regulatory environments. Furthermore, the complexity of mapping datasets—which often contain sensitive geospatial data, road infrastructure imagery, and personally identifiable information—demands a more nuanced compliance approach that differentiates between permissible and restricted data categories in real time.

[0004] Traditional methods for verifying compliance in autonomous vehicle data pipelines rely on static configurations, limited policy libraries, and manual audits. Therefore, they are unsuitable for large-scale autonomous vehicle ecosystems, where data volumes reach petabytes and regulations vary across jurisdictions. Furthermore, traditional systems fail to address the issue of data provenance transparency. It is difficult to trace how and where data has been transformed, replicated, or shared within cloud ecosystems. This lack of traceability increases the risk of violations and undermines accountability.

[0005] Therefore, there is an urgent need for an AI-powered, dynamic, and automated cloud compliance system capable of analyzing and controlling the movement, processing, and storage of AV-related data across distributed cloud infrastructures. Such a system should intelligently interpret regulatory texts, translate them into actionable compliance policies, and monitor cloud services for compliance in real time. Furthermore, it should be able to generate audit logs, adaptive rule updates, and intelligent decision logs that can withstand regulatory scrutiny while ensuring the operational efficiency of AV mapping services.

[0006] The development of autonomous vehicles (AVs) has triggered a profound transformation in transportation systems, urban infrastructure, and data-driven mobility ecosystems. Autonomous vehicles generate, process, and transmit vast amounts of heterogeneous data from high-precision sensors such as LiDAR, radar, GPS, inertial sensors, and multi-image cameras. This data, collectively referred to as AV operational data, plays a central role in perception, localization, decision-making, and mapping. The rapid proliferation of AV platforms, coupled with the emergence of cloud-based services for real-time data analytics, has made the cloud an indispensable backbone for autonomous driving ecosystems.

[0007] Mapping services, simulation systems, and remote fleet management all rely on continuous cloud connectivity. However, this paradigm shift brings with it a completely new dimension of complexity: cloud compliance management, particularly with regard to data protection, adherence to legal regulations, and secure cross-border data transfer.

[0008] Existing solutions for managing AV data in the cloud primarily focus on scalability, storage efficiency, and computing power, often neglecting the complex and dynamic regulatory frameworks that govern data collection and use. Conventional cloud data management systems, including those from major providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, offer compliance modules based on pre-configured templates and custom access policies. While such templates may meet basic compliance requirements, they fail to interpret or implement contextual compliance—that is, the legal and ethical obligations that depend on the nature of the data, its geographic origin, or the applicable jurisdiction.For example, visual data containing identifiable faces or vehicle license plates must be anonymized before being uploaded to the cloud, according to the European Union's General Data Protection Regulation (GDPR), while the same data may be permissible under different retention rules in other jurisdictions. Existing compliance modules lack the adaptive intelligence to make these distinctions automatically.

[0009] A typical example of this limitation can be found in data lake and data warehouse architectures for AV mapping services. These architectures process large volumes of sensor data and geospatial information for map creation and updates. While they include encryption of data at rest and access control mechanisms, they lack automated data classification and policy enforcement. Compliance officers must manually review metadata, annotate restricted datasets, and apply anonymization techniques, which becomes virtually impossible with terabytes of data processed per hour. The lack of automation leads to human error, delayed regulatory responses, and potential data breaches.

[0010] Furthermore, the lack of transparent data provenance tracking means that once data enters the cloud environment, it becomes virtually impossible to determine where it has been replicated or processed. This undermines accountability and legal traceability.

[0011] Several research-oriented solutions have attempted to introduce compliance automation through rule-based frameworks. These frameworks rely on hard-coded policies that define which operations are permitted or prohibited for specific data types. For example, a rule might state that “geodata within military restricted areas may not be exported out of the country.” While conceptually sound, such static rules are inflexible and cannot adapt to changing legal interpretations or regional policy changes. Rule-based systems lack semantic understanding and cannot interpret ambiguous or unstructured regulatory texts. Consequently, they often fail when applied to complex, cross-border contexts characteristic of AV ecosystems.The lack of interoperability between cloud environments further complicates ensuring compliance, as each cloud provider uses proprietary security mechanisms and policy formats, making cross-platform policy enforcement a manual and error-prone process.

[0012] Security compliance systems based on cryptographic controls and access logging also suffer from scalability and interpretability issues. While cryptographic integrity checks ensure data authenticity, they offer no semantic guarantee of regulatory compliance. Similarly, access logs are too detailed and extensive for manual review, making real-time compliance verification virtually impossible. As a result, organizations resort to retrospective sampling, which cannot guarantee continuous protection.

[0013] Existing cloud compliance mechanisms for AV data and map services are fundamentally limited by reactive rule enforcement, static policy interpretation, fragmented auditability, and a lack of AI-powered contextual analysis. The increasing complexity of AV ecosystems, combined with the dynamic nature of data protection laws and geospatial restrictions, is rendering traditional approaches obsolete. A significant technological gap exists for an intelligent, adaptive, and automated compliance infrastructure that understands legal obligations, analyzes AV data contexts, and seamlessly enforces compliance across distributed cloud environments. This gap forms the basis and motivation for the present invention—an AI-powered cloud compliance system for AV data and map services that overcomes the limitations of existing technologies through proactive intelligence, continuous adaptation, and traceable data governance. Summary of the invention

[0014] The invention provides an intelligent, AI-supported cloud compliance system for data and mapping services of autonomous vehicles. The system comprises a compliance interpretation engine, a cloud compliance monitor, a data origin tracker, a policy enforcement module, and a compliance testing device that acts as a physical interface between vehicle-side data sources and cloud storage.

[0015] The AI ​​models integrated into the system semantically analyze legal frameworks to extract key compliance elements and automatically map them to cloud policy parameters such as encryption levels, data retention periods, access rights, and geofencing restrictions. The compliance monitor continuously tracks data operations in the cloud and identifies non-compliant actions such as unauthorized transfers or unencrypted uploads. The data origin tracker stores cryptographic hashes for each stage of AV data processing, ensuring verifiable traceability.

[0016] The physical compliance device acts as a gatekeeper node, either integrated into an AV data aggregation station or as a standalone device in a data center. It features dedicated circuitry for encryption validation, metadata inspection, and AI-powered data sensitivity classification prior to cloud transmission. Together, these components form a self-contained compliance ecosystem that dynamically adapts to changing regulatory frameworks while ensuring transparency, accountability, and operational integrity.

[0017] The main objective of the present invention is to provide an AI-powered cloud compliance system that automates the enforcement of data protection, privacy and mapping regulations in heterogeneous cloud environments used in AV data and mapping services.

[0018] Another objective of the invention is to enable dynamic conformity derivations, whereby artificial intelligence models interpret legal and regulatory requirements and map them into executable policy logic that is applicable to certain AV data types such as image data, telemetry data or geodata.

[0019] Another objective of the invention is to ensure a traceable and verifiable data flow through continuous monitoring of the data origin, encryption states, access permissions and transmission paths between cloud nodes.

[0020] Another objective of the invention is to provide an integrated device or a hardware-embedded compliance module that can act as an intermediary between AV data acquisition systems and cloud-based mapping repositories to ensure compliance with regulations before data is uploaded or retrieved.

[0021] Furthermore, the invention aims to implement self-learning and adaptive rule refinements based on detected anomalies, audit results or new legal changes, thereby ensuring sustainable compliance management in dynamic regulatory environments. BRIEF DESCRIPTION OF THE IMAGE

[0022] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of an AI-driven cloud compliance system for data and mapping services for autonomous vehicles (AVs).

[0023] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0024] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0025] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0026] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0027] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent to the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0029] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0030] In Fig.Figure 1 is a block diagram of an AI-powered cloud compliance system for data and mapping services for autonomous vehicles (AVs). The system (100) comprises: a data acquisition unit (102) for acquiring, aggregating, and preprocessing sensor data from autonomous vehicle subsystems, including LiDAR scanners, radar arrays, camera systems, and telemetry transmitters. The data acquisition unit includes a synchronization circuit for aligning temporal sensor data streams and a metadata tagging circuit for adding position coordinates, time identifiers, and authentication data to each data record. A compliance processing unit (104) is connected to the data acquisition unit and executes AI-based compliance algorithms stored in memory. The compliance processing unit also interprets applicable data protection, privacy, and mapping laws and generates compliance control signals that determine permissible data transmission,Define encryption and storage parameters. A classification unit (106) is connected to the compliance processing unit and analyzes the data content using tensor computing hardware to identify sensitive, restricted, or public data and assign compliance classification identifiers to each data packet. A policy control unit (108), operationally connected to the compliance processing unit, is configured to enforce the compliance control signals by dynamically adjusting the activation of encryption keys, routing permissions, and cloud storage allocations according to the compliance classification identifiers. A data provenance tracking unit (110), operationally coupled to the policy control unit, includes a cryptographic hash generation circuit configured to track compliance verification events.records data transformations and access transactions in a chained ledger stored in the storage unit; and a compliance verification device (112) structurally integrated between the data acquisition unit and a cloud communications controller (112a), the compliance verification device comprising a compliance processor, a secure cryptographic enclave, and a transmission control circuit configured to authorize, block, or redirect cloud-bound data packets based on compliance control signals received from the compliance processing unit.

[0031] In one embodiment, the data acquisition unit (102) further comprises a preprocessing unit with a digital signal conditioning circuit configured to perform noise filtering, time smoothing and normalization of the raw sensor outputs prior to encryption; and an encryption controller configured to encrypt the preprocessed data with a symmetric key prior to transmission to the compliance processing unit.

[0032] In one embodiment, the compliance processing unit (104) comprises a neural inference processor array configured to perform matrix-based calculations for the compliance decision and a logic controller configured to map the inference results to predefined compliance parameters stored in the memory unit.

[0033] In one embodiment, the storage unit comprises a hierarchical storage architecture with a volatile cache for temporary compliance decision data, non-volatile flash storage for storing compliance policies, and an immutable blockchain ledger partition configured to store cryptographically verifiable origin records.

[0034] In one embodiment, the policy control unit (108) comprises an access control controller configured to manage the allocation and expiry of encryption keys for each data category, a routing controller configured to select permissible cloud endpoints in accordance with applicable legal regulations, and a transfer authorization circuit configured to execute or terminate data transfer operations based on compliance control signals.

[0035] In one embodiment, the data provenance tracking unit (110) further comprises a timestamp generator circuit configured to assign a microsecond-resolution timestamp to each recorded compliance event, and a verification controller configured to perform recursive hash validation during the audit retrieval to ensure provenance integrity.

[0036] In one embodiment, the conformity testing device (112) comprises a buffer memory configured to temporarily store data packets pending conformity validation, a comparison circuit configured to compare conformity identifiers embedded in metadata with permissible policy parameters, and a quarantine controller configured to store non-compliant data packets in an encrypted partition for later administrative review.

[0037] In one embodiment, the cloud communication controller (112a) comprises an asymmetric encryption circuit configured for cryptographic key exchange using elliptic curves for secure communication, a packet integrity checker configured for validating data signatures, and a responsibility routing selector configured to assign cloud transmissions to the specified data zones according to the parameters of the compliance policy.

[0038] In one embodiment, the classification unit (106) comprises a sensitivity detection circuit configured to detect restricted data patterns such as images of human faces, vehicle registration plates or protected infrastructures, and an output controller configured to add conformance sensitivity flags to the metadata of the corresponding data packets.

[0039] In one embodiment, the compliance processing unit (104) is operationally connected to a learning control unit comprising a matrix optimization processor and a gradient fitting circuit configured to update the weights of the neural model based on compliance audit feedback, regulatory changes, and operational performance metrics. Detailed description of the invention

[0040] The present invention relates to a cloud-integrated, AI-powered compliance control system for data and mapping services of autonomous vehicles. The system combines AI-powered compliance analytics with a distributed, hardware-based cloud architecture to ensure the lawful processing, storage, and transmission of AV-generated data across different jurisdictions. It addresses the need for automated, auditable, and adaptive compliance in large-scale AV data ecosystems where sensor data, telemetry data, and map information are continuously streamed to the cloud for analysis and model updates.

[0041] The system comprises a data acquisition unit that receives raw data from AV subsystems such as LiDAR, radar, high-resolution cameras, and telemetry sensors. The data acquisition unit is equipped with a synchronization circuit that coordinates the temporal data streams of multiple sensors, as well as a metadata tagging circuit that assigns positional coordinates, timestamps, and sensor identifiers to each data record. A preprocessing unit performs noise reduction, normalization, and compression using digital signal conditioning. This unit also includes an encryption controller that applies symmetric encryption directly at the hardware level before data transmission, ensuring that no unencrypted AV data is transmitted to external cloud infrastructures.

[0042] The compliance processing unit acts as the system's computing core, executing trained AI procedures stored in memory. These procedures are based on a two-stage hybrid deep learning architecture: regulatory interpretation and compliance parameter derivation. The interpretation stage uses transformer-based natural language models trained on international and national data protection laws, mapping regulations, and regional guidelines. This model transforms textual regulatory data into structured compliance vectors that define obligations such as encryption requirements, permissible data transfer zones, and retention periods. The derivation stage uses these vectors as constraints in a neural network decision model, which calculates the compliance parameters for each data set based on its origin, sensitivity, and type.

[0043] Mathematically, the compliance processing unit executes a function f(D, R) → C, where D represents the metadata and features of the dataset and R represents the regulatory features extracted from the legal interpretation model. The function outputs a compliance decision variable C, which can correspond to one of several categories: compliant, restricted, or non-compliant. This decision variable is transmitted to the policy control unit, which then applies the appropriate enforcement measures. The compliance inference process utilizes multi-layered tensor calculations, matrix-based feature extraction, and recurrent validation feedback loops to ensure high interpretive accuracy. It is optimized through reinforcement learning based on historical compliance results and audit feedback.

[0044] The classification unit performs real-time sensitivity checks using tensor computing hardware integrated into its circuitry. It detects and categorizes sensitive data elements, such as facial features, vehicle registration numbers, or geocoordinates, from protected mapping areas. These detections are converted into compliance classification identifiers, which are embedded in the metadata of each data record. The classification process compares feature vectors from the data against reference templates representing protected data categories and uses cosine similarity calculations to determine compliance risk levels.

[0045] The policy control unit enforces policy compliance through hardware-based routing, encryption management, and data access control. It dynamically adjusts routing tables and storage allocations in distributed cloud environments based on compliance classification identifiers. If the compliance processing unit determines that a data record may only be stored in specific jurisdictions, the policy control unit activates routing restrictions that prevent data transfers outside those regions. The policy control unit also manages the lifecycles of encryption keys using a hardware-based key activation controller, ensuring that encryption keys are created, distributed, and revoked in accordance with the compliance policies stored in the storage unit.

[0046] The data provenance tracking unit securely stores all compliance events, transformations, and data transfers within the system. It is equipped with a hash generation circuit that creates cryptographic fingerprints for each transaction and sequentially links them in a blockchain-based, in-memory ledger. Each entry contains a timestamp, a data identifier, and a hash reference to the previous entry, thus forming an immutable chain of proof. Data provenance tracking ensures traceability and non-repudiation, enabling regulatory authorities to verify compliance with applicable laws during data processing at every stage of the data lifecycle.

[0047] The compliance check device acts as an intermediary between the data acquisition unit and the cloud communication controller. It contains a compliance processor that executes firmware-level checks to analyze incoming data packets before they are uploaded to the cloud. The device's secure hardware enclave stores cryptographic keys and compliance metadata, while the transmission control circuit authorizes or blocks transmissions based on the compliance parameters received from the compliance processing unit. The device also includes a buffer that temporarily stores data until classification, and a comparator circuit that validates the compliance identifier embedded in each packet's metadata.

[0048] The cloud communication controller manages encrypted communication between the compliance verification device and the distributed cloud storage nodes. It uses asymmetric encryption circuits to establish secure communication channels and performs key exchange using elliptic curve cryptography. The controller includes a packet integrity check to verify data authenticity and a routing selector that assigns data packets to region-specific cloud partitions based on compliance policies.

[0049] The storage unit serves as a data repository for compliance rules, AI models, and audit logs. It consists of a multi-layered structure: a volatile cache for the temporary storage of decisions, non-volatile flash storage for persistent compliance policies, and a blockchain ledger partition for the immutable storage of provenance data. An access controller manages the data flow between the units and encrypts the stored information in real time using hardware-based integrity checks.

[0050] The learning control unit continuously optimizes the parameters of the AI ​​model. It receives feedback from audit reports, regulatory updates, and detected anomalies, and recalibrates the weight matrices of the compliance inference model using gradient descent and amplification-based adjustment methods. This ensures that the system remains adaptable to changing regulatory frameworks and operating conditions without manual reprogramming.

[0051] The rule synchronization unit communicates with external legal databases via a secure interface and retrieves regularly updated legal definitions and compliance requirements. A checksum verification authenticates the received rules before integration into the compliance knowledge base. This ensures that the system's compliance logic always adheres to global and regional regulations for data processing and mapping activities.

[0052] A visualization control unit is connected to the data provenance tracking unit and provides administrators with a user interface. It visualizes compliance events, provenance paths, and enforcement status in a real-time dashboard. The visualization processor also generates auditable compliance summaries that can be submitted directly to regulators for verification.

[0053] The compliance management control system coordinates communication between all operational units via a central control bus, a clock oscillator, and a command scheduler. It optimizes signal propagation times and prioritizes tasks to ensure the synchronous operation of all compliance-related activities. An integrated voltage regulator guarantees the uninterrupted power supply to critical compliance circuits, and a fault tolerance controller protects against data loss or corruption during temporary failures through redundancy buffers and automatic recovery routines.

[0054] The components described above can be implemented using off-the-shelf hardware and custom firmware. The compliance processing unit can be implemented using a GPU- or FPGA-based neural inference accelerator. The data acquisition and classification units can be built with embedded processors featuring high-speed ADC interfaces for real-time sensor data acquisition. The encryption and cryptographic functions can utilize dedicated AES and ECC cores built on secure microcontrollers. The blockchain-based data provenance tracer can use existing distributed ledger frameworks with secure hardware hashing circuits. The system can be built on a high-speed control bus (e.g., PCIe or a custom SoC architecture) with shared memory access.The firmware for synchronization, policy control, and routing can be developed in C or Verilog to ensure deterministic timing and hardware-level compliance checking.

[0055] The technological advancement of this invention lies in its ability to combine AI-based regulatory interpretation with hardware-driven, real-time compliance enforcement for AV data ecosystems. Unlike existing cloud data systems that rely on static rule templates or post-audits, this system performs compliance inference dynamically and autonomously at the data entry layer. The integration of neural circuits with encryption and routing controllers enables the real-time implementation of compliance decisions, thus preventing violations before the data leaves the AV network. The use of blockchain-based provenance tracking ensures immutable auditability, while the reinforcement learning loop allows for the self-evolving of compliance models as new laws come into effect.

[0056] The technical benefit of this system lies in a significant improvement in the accuracy of regulatory compliance, data security, and legal accountability for mapping and telemetry data management of autonomous vehicles. It eliminates manual policy mapping, reduces latency in compliance testing, and ensures data localization in legally permissible cloud regions. The system thus improves both the operational reliability of autonomous vehicle data pipelines and the regulatory accountability of cloud-based autonomous vehicle ecosystems, representing a significant advancement over traditional compliance testing infrastructures.

[0057] The AI-powered cloud compliance system for AV data and mapping services includes both software and hardware components designed for collaborative operation in a distributed cloud environment.

[0058] The Compliance Interpretation Engine (CIE) uses a transformer-based natural language processing model trained on legal datasets from various jurisdictions. The CIE processes regulatory documents in natural language and transforms them into structured compliance representations that include parameters such as categories of restricted data, required anonymization levels, and retention periods. These representations are then converted into machine-readable policy objects and stored in a Policy Knowledge Base (PKB).

[0059] The Cloud Compliance Monitor (CCM) acts as a continuous monitoring module integrated with cloud APIs and orchestration frameworks such as Kubernetes or AWS Lambda. It uses AI-based anomaly detection to identify potential compliance violations, such as unusual access patterns, cross-border data flows, or unauthorized replication events. The CCM communicates directly with the Policy Enforcement Module (PEM), which dynamically adjusts permissions, suspends transactions, or triggers encryption processes in the event of violations.

[0060] The Data Lineage Tracker (DLT) forms the backbone of the system's traceability. It utilizes a blockchain-based ledger mechanism that records cryptographically signed events corresponding to every data transformation, replication, or deletion in the cloud. Each block in the ledger is time-stamped and linked to the preceding operation. This creates a tamper-proof audit trail suitable for legal and regulatory purposes.

[0061] A custom-designed, AI-powered adaptive learning system (ALS) continuously trains CIE and PEM based on new regulatory publications, audit reports, and feedback from compliance officers. The ALS uses reinforcement learning to optimize policy enforcement strategies that minimize business disruptions while ensuring full regulatory compliance.

[0062] The CVD begins operating as soon as AV data streams enter the system via the input port. The integrated classifier analyzes metadata and user data attributes to determine whether the data contains personally identifiable information (PII) or location-based content. The AI ​​inference module accesses the current PKB guidelines and decides on the permissible data path – either direct upload to compliant cloud areas or forwarding to anonymization modules before transmission.

[0063] The entire system architecture supports multi-cloud environments and integrates seamlessly with leading cloud providers via standardized compliance APIs. Data audit trails generated by the DLT are accessible through a compliance visualization dashboard (CVDash), providing regulators and administrators with a unified overview of compliance status, data origin diagrams, and incident analyses.

[0064] Through this integrated, AI-powered framework, the invention ensures consistent compliance with regulations for AV data and mapping ecosystems. This significantly reduces manual effort, minimizes data privacy risks, and promotes lawful data exchange across international legal systems. The invention thus represents a technological advancement in the field of data management for autonomous vehicles and secure cloud-based mapping processes.

[0065] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0066] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 An AI-driven cloud compliance system for data and mapping services for autonomous vehicles (AVs). 102 Data acquisition unit 104 Processing Unit for Compliance Matters 106 Classification unit 108 Policy Control Unit 110 Data Origin Tracking Unit 112 Conformity testing device 112a Cloud Communication Controller

Claims

[1] A cloud-integrated AI-based compliance control system for data and mapping services of autonomous vehicles (AVs), consisting of: a data acquisition unit configured to acquire, aggregate and preprocess sensor data from autonomous vehicle subsystems, including LiDAR scanners, radar arrays, camera systems and telemetry transmitters, wherein the data acquisition unit includes a synchronization circuit for aligning temporal sensor data streams and a metadata tagging circuit configured to add position coordinates, time identifiers and authentication data to each data record; a compliance processing unit that is operationally connected to the data acquisition unit and configured to execute artificial intelligence-based compliance techniques stored in a storage unit, wherein the compliance processing unit is further configured to interpret the relevant data protection, privacy and mapping laws and generate compliance control signals that define permissible data routing, encryption and storage parameters; a classification unit that is operationally connected to the compliance processing unit and is configured to analyze the data content using tensor computational hardware to identify sensitive, restricted, or public data and assign compliance classification identifiers to each data packet; a policy control unit that is operationally connected to the compliance processing unit and is configured to enforce compliance control signals by dynamically adjusting the activation of encryption keys, routing permissions, and cloud storage allocations according to the compliance classification identifiers; a data provenance tracking unit operationally connected to the policy control unit and comprising a cryptographic hash generation circuit configured to record compliance check events, data transformations, and access transactions in a chained, in-memory ledger; and A compliance check device that is structurally integrated between the data acquisition unit and a cloud communications controller. This compliance check device includes a compliance processor, a secure cryptographic enclave, and a transmission control circuit configured to authorize, block, or redirect cloud-bound data packets based on compliance control signals received from the compliance processing unit. [2] System according to claim 1, wherein the data acquisition unit further comprises a preprocessing unit which includes a digital signal conditioning circuit for noise filtering, time smoothing and normalization of the raw sensor outputs prior to encryption, and an encryption controller for applying symmetric key encryption to the preprocessed data prior to transmission to the conformity processing unit. [3] System according to claim 1, wherein the compliance processing unit comprises a neural inference processor array configured to perform matrix-based computations for compliance decision inference and a logic controller configured to map inference results to predefined compliance parameters stored in the memory unit. [4] System according to claim 1, wherein the storage unit comprises a hierarchical storage architecture comprising a volatile cache for temporary compliance decision data, a non-volatile flash memory for storing compliance policies, and an immutable blockchain ledger partition configured to store cryptographically verifiable origin records. [5] System according to claim 1, wherein the policy control unit comprises an access control controller configured to manage the allocation and expiry of encryption keys for each data category, a routing controller configured to select permissible cloud endpoints in accordance with applicable legislation, and a transmission authorization circuit configured to execute or terminate data transmission operations based on compliance control signals. [6] System according to claim 1, wherein the data provenance tracking unit further comprises a timestamp generator circuit configured to assign a microsecond-resolution timestamp to each recorded compliance event, and a verification controller configured to perform recursive hash validation during the audit retrieval to ensure provenance integrity. [7] System according to claim 1, wherein the conformity testing device comprises a buffer memory for temporarily storing data packets until conformity validation, a comparison circuit for comparing conformity identifiers embedded in metadata with permissible policy parameters and a quarantine controller for storing non-conforming data packets in an encrypted partition for later administrative review. [8] System according to claim 1, wherein the cloud communication controller comprises an asymmetric encryption circuit for performing a cryptographic key exchange using elliptic curves for secure communication, a packet integrity check for validating data signatures and a jurisdiction routing selector for assigning cloud transfers to specified data zones according to the parameters of the compliance policy. [9] System according to claim 1, wherein the classification unit comprises a sensitivity detection circuit configured to detect restricted data patterns including human facial images, vehicle registration plates or protected infrastructure, and an output controller configured to add conformance sensitivity flags to the metadata of the corresponding data packets. [10] System according to claim 1, wherein the compliance processing unit is operationally connected to a learning control unit comprising a matrix optimization processor and a gradient fitting circuit configured to update the weights of the neural model based on compliance audit feedback, regulatory changes and operational performance indicators.

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