Cloud-native system for monitoring identity security with autonomous troubleshooting

Abstract

A cloud-native identity security monitoring system implemented as a computer device, consisting of: a data collection interface configured to receive identity-related telemetry data from a variety of distributed cloud resources, identity providers, and access management services over network communication channels; a preprocessing unit that is operationally coupled with the data acquisition interface and is configured to normalize the received telemetry data into a uniform data schema through timestamp matching, identity correlation, attribute extraction and structure transformation; a storage unit that is connected to the preprocessing unit and configured to store normalized identity data and historical records of identity activity; a graph construction processor that is operationally connected to the storage unit and configured to generate and continuously update an identity relationship structure that includes nodes representing identities, devices, services, and resources, as well as edges representing authentication events, authorization actions, and access interactions; an analytical processor coupled with the graph construction processor and configured to calculate behavioral profiles for the respective identities by analyzing temporal sequences, access frequencies, interaction patterns and context attributes, and furthermore configured to identify anomaly values ​​that indicate deviations from established behavioral profiles; a decision processor that communicates with the analysis processor and is configured to compare the anomaly values ​​with dynamically adjustable thresholds to identify anomalous identity activity; an interface for executing corrective actions, which is operationally connected to the decision processor and configured to send control instructions to external cloud systems to perform corrective actions, including invalidating credentials, terminating sessions, changing access rights, and enforcing additional authentication requirements; and a feedback processing unit configured to receive status data after the fix and update the analytical processor to refine behavioral models based on the results of the corrective actions taken.

Description

Technical field of the invention

[0001] The present invention relates generally to the field of cybersecurity and in particular to a cloud-native computing device with associated system architecture configured for identity-centric security monitoring, anomaly detection, and autonomous remediation in distributed cloud environments. The invention further relates to a machine-implemented structure for the continuous analysis of identity behavior, access patterns, and authentication events in multi-cloud infrastructures, as well as for the dynamic execution of corrective security measures without human intervention. BACKGROUND OF THE INVENTION

[0002] With the increasing prevalence of cloud-native architectures, microservices, and distributed identity providers, the attack surface for identity attacks in enterprise environments has significantly expanded. Traditional, perimeter-based security mechanisms are insufficient to detect and mitigate threats arising from compromised credentials, privilege escalation, lateral movement, and unauthorized access to cloud workloads. Identity-based attacks often exploit legitimate authentication channels and are therefore difficult to detect with conventional rule-based monitoring systems.

[0003] Existing security monitoring systems rely heavily on static policies, predefined rules, and delayed human responses. This prolongs the dwell time of attackers and increases risk. Furthermore, the dynamic and ephemeral nature of cloud resources poses a challenge to correlating identity events across different services, containers, and orchestration layers. There is a need for a cloud-native machine architecture capable of capturing identity telemetry in real time, performing contextual analysis, building behavioral models, and autonomously taking countermeasures when anomalies are detected.

[0004] The rapid shift of enterprise IT infrastructures toward cloud-native architectures has fundamentally changed the security landscape, particularly with regard to identity and access management. In traditional on-premises environments, security controls were primarily network perimeter-based, using firewalls and network segmentation to prevent unauthorized access. However, with the increasing prevalence of Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), and containerized microservices, identity has established itself as the central control plane for accessing distributed resources. Cloud identity management systems are designed to authenticate users, authorize access, and manage identity lifecycles across platforms.However, the dynamic and decentralized nature of cloud environments presents existing solutions with significant technical challenges that they struggle to overcome.

[0005] Conventional identity and access management (IAM) solutions in cloud environments typically rely on centralized authentication services, role-based access control (RBAC), and policy enforcement mechanisms. These systems often integrate directory services and federated identity providers to enable single sign-on (SSO) for multiple applications. While such approaches improve usability and simplify access control, they increase the complexity of maintaining consistent identity management across heterogeneous cloud ecosystems. Organizations operating in multi-cloud or hybrid environments frequently encounter difficulties synchronizing identity policies, resulting in fragmented identity stores and inconsistent enforcement of security rules.This fragmentation creates transparency gaps and makes it difficult for security teams to gain a unified view of identity activity and potential threats.

[0006] Existing cloud security monitoring solutions attempt to address these issues by aggregating logs, metrics, and events from various cloud services, including compute instances, containers, firewalls, and identity systems. These systems typically employ rule-based correlation algorithms and, in some cases, machine learning to detect anomalous behavior. For example, unusual login patterns, excessive data access, or anomalous API activity can trigger alerts indicating a potential compromise. However, such solutions are often limited by their reliance on predefined rules and static thresholds, which are insufficient to detect complex, constantly evolving threats that mimic legitimate user behavior.Attackers using stolen credentials can operate within normal system boundaries, thus bypassing detection mechanisms that do not include context or behavioral analysis.

[0007] A significant drawback of existing IAM and monitoring systems is their inability to continuously and contextually detect identity theft threats. Many systems still rely on periodic audits or static access policies instead of real-time behavioral monitoring. This limitation is particularly problematic because credential-based attacks, such as phishing, credential stuffing, and session hijacking, have become the dominant attack vectors in cloud environments. Once an attacker gains access with valid credentials, they can move laterally, escalate privileges, and exfiltrate data without triggering traditional security alerts. The lack of continuous monitoring and adaptive analytics significantly extends the dwell time of attackers in compromised systems.

[0008] Another critical limitation of existing solutions is their over-reliance on manual intervention in security incident response. While many cloud security tools generate alerts when they detect suspicious activity, the responsibility for investigating and responding to these alerts typically rests with human operators. This leads to operational bottlenecks, particularly in large environments where the number of alerts can be overwhelming. Security teams often suffer from alert fatigue, as critical alerts get lost among numerous low-priority notifications, resulting in delayed or missed responses. Furthermore, the processes for investigating and resolving security incidents are time-consuming, often requiring several hours to analyze a single security event, thus increasing the risk of damage spreading.

[0009] Another significant drawback lies in the limited integration and interoperability of existing security tools. Companies typically use multiple isolated solutions for identity management, threat detection, compliance monitoring, and incident response. These tools often operate in isolation, creating data silos that hinder comprehensive threat analysis. The lack of seamless data exchange and insufficient correlation between systems prevents the development of a holistic security concept and thus reduces the effectiveness of detection and response mechanisms. Fragmented security architectures not only increase operational complexity but also create vulnerabilities that attackers can exploit.

[0010] Machine learning relies on detection systems designed to improve cloud security monitoring. However, these systems face several challenges. They require large amounts of high-quality training data and often struggle with issues such as model drift, false positives, and poor interpretability. The dynamic nature of cloud environments, characterized by rapidly changing workloads and user behavior, further complicates the maintenance of accurate and reliable models. Traditional anomaly detection methods may fail to capture subtle, context-dependent threats, resulting in missed detections or excessive false positives. The lack of standardized datasets and evaluation frameworks further limits the effectiveness and adoption of such approaches in real-world environments.

[0011] Existing cloud-based identity management and security monitoring solutions offer basic authentication, authorization, and threat detection capabilities, but suffer from significant technical limitations. These include a lack of real-time and contextual monitoring, reliance on static rules and manual responses, fragmented visibility in multi-cloud environments, an inability to effectively manage non-human identities, and limited automation of remediation processes. Given the ongoing evolution of cloud infrastructures, there is a pressing need for advanced systems that integrate identity telemetry, perform continuous behavioral analysis, and can respond to threats autonomously, scalably, and adaptively. SUMMARY OF THE INVENTION

[0012] The present invention describes a cloud-native identity security monitoring system implemented as a specialized computing device for operation in distributed cloud environments. The device comprises interconnected processing units, memory, network interfaces, and programmable logic. It is configured to collect identity-related telemetry data from various cloud services, normalize this data, encode it into structured identity graphs, and continuously evaluate behavioral anomalies using adaptive analysis models.

[0013] The system also includes a vulnerability remediation control structure that automatically initiates corrective actions such as credential revocation, session termination, privilege restriction, and policy reconfiguration upon detection of anomalous or malicious identity activity. The device is architecturally designed to support containerized deployment, horizontal scalability, and integration with cloud orchestration systems, thus enabling automated, real-time identity threat mitigation across heterogeneous cloud infrastructures.

[0014] The present invention aims to provide a cloud-native system for monitoring identity security. This system is implemented as a specialized computing device capable of continuously and in real time acquiring, processing, and analyzing identity-related telemetry data in distributed cloud environments. The invention overcomes the limitations of conventional identity and access management systems through a machine architecture that enables unified visibility of identity activities across multi-cloud, hybrid, and container infrastructures, thereby ensuring comprehensive monitoring of authentication events, authorization decisions, and access behavior.

[0015] A further objective of the invention is to provide a technically sophisticated system for creating dynamic identity relationship representations that capture interactions between users, services, devices, and resources, thus enabling context-related analysis of identity behavior. By generating and maintaining continuously updated identity graphs, the invention aims to identify complex behavioral patterns, correlations, and anomalies that are not detectable with conventional rule-based monitoring approaches.

[0016] A further objective of the invention is to provide an analytical processing mechanism that enables adaptive behavioral modeling and anomaly detection using computer-aided methods. These methods establish baseline identity patterns and dynamically adjust detection thresholds. The invention aims to detect subtle variations in identity usage, including attempts at privilege escalation, lateral movement within cloud resources, unusual access times, and geographic inconsistencies. This improves the accuracy and reliability of threat detection in highly dynamic environments.

[0017] A further objective of the invention is to provide an autonomous defense function integrated into the computer device that wards off detected identity threats without manual intervention. The invention enables the automated execution of corrective actions such as invalidating login credentials, terminating sessions, restricting privileges, enforcing additional authentication factors, and modifying access control policies. This significantly reduces response time and limits potential damage from compromised identities.

[0018] A further objective of the invention is to provide a scalable and cloud-native machine architecture that supports deployment in container orchestration environments and enables horizontal scaling for processing large volumes of identity telemetry data. The invention aims to ensure efficient system operation in large enterprise environments with dynamic workloads, high transaction rates, and constantly evolving identity interactions.

[0019] A further objective of the invention is to provide a secure and fault-tolerant computer architecture with encrypted communication channels, secure data storage mechanisms, and hardware-based security functions to protect sensitive identity data during processing and transmission. The invention further aims to ensure fault tolerance and high availability through redundant processing paths and distributed data replication.

[0020] A further objective of the invention is to provide feedback-driven learning capability within the system. The results of corrective actions and detected anomalies are continuously analyzed to refine behavioral models and improve detection accuracy over time. This ensures that the system adapts to changing threat patterns and evolving user behavior without requiring frequent manual reconfiguration.

[0021] A further objective of the invention is interoperability with various cloud service providers and identity management frameworks through standardized communication interfaces and programmable integration mechanisms. The invention enables seamless interaction with external systems for telemetry acquisition and troubleshooting, thus ensuring compatibility with heterogeneous cloud ecosystems.

[0022] A further objective of the invention is to reduce the operational overhead and alarm fatigue of conventional security monitoring systems by minimizing false alarms and automating workflows for responding to security incidents. The invention aims to improve the overall security posture through continuous, intelligent, and automated identity threat management in cloud-native environments.

[0023] Overall, the invention aims to provide a comprehensive, adaptive and autonomous identity security monitoring system that overcomes the technical shortcomings of existing solutions by integrating real-time data processing, behavioral analysis and automated troubleshooting into a unified cloud-native computing device. BRIEF DESCRIPTION OF THE IMAGE

[0024] 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 a cloud-native identity security monitoring system implemented as a computer device.

[0025] 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

[0026] 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.

[0027] 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 of it.

[0028] 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.

[0029] 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 in 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.

[0030] 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.

[0031] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0032] Fig.Figure 1 shows a block diagram of a cloud-native identity security monitoring system implemented as a computer. The system 100 comprises: a data acquisition interface (102) for receiving identity-related telemetry data from a variety of distributed cloud resources, identity providers, and access management services via network communication channels; a preprocessing unit (104) connected to the data acquisition interface that normalizes the received telemetry data into a unified data schema through timestamp matching, identity correlation, attribute extraction, and structure transformation; and a storage unit (106) that communicates with the preprocessing unit and stores normalized identity data as well as historical identity activity records.a graph construction processor (108) connected to memory that generates and continuously updates an identity relationship structure comprising nodes for identities, devices, services, and resources, as well as edges for authentication events, authorization actions, and access interactions; an analytical processor (110) coupled to the graph construction processor and configured to compute behavioral profiles for the respective identities by analyzing temporal sequences, access frequencies, interaction patterns, and context attributes, and furthermore, to identify anomaly values ​​indicating deviations from established behavioral profiles; a decision processor (112) that communicates with the analytical processor and is configured to compare the anomaly values ​​with dynamically adjustable thresholds to identify anomalous identity activity;an interface for executing corrective actions (114) that is operationally connected to the decision processor and configured to send control instructions to external cloud systems to execute corrective actions, including invalidating credentials, terminating sessions, changing access rights, and enforcing additional authentication requirements; and a feedback processing unit (116) configured to receive status data after the correction and update the analytical processor to refine behavioral models based on the results of the executed corrective actions.

[0033] In one embodiment, the preprocessing unit (104) is further configured to perform identity event deduplication by identifying recurring authentication records through hash-based comparison of event attributes. The preprocessing unit encodes identity attributes, including geographic indicators, device identifiers, and network parameters, into structured representations for subsequent processing.

[0034] In one embodiment, the graph construction processor (108) is configured to maintain the temporal order of identity interactions by assigning time-indexed attributes to edges, and to update the identity relationship structure almost in real time upon receiving new telemetry data without reconstructing the entire graph.

[0035] In one embodiment, the analytical processor (110) is configured to generate behavioral models based on the statistical aggregation of identity activity over a configurable observation window and is furthermore configured to include sequence-based pattern recognition to identify abnormal transitions between resources and services.

[0036] In one embodiment, the analytical processor (110) is further configured to calculate anomaly values ​​based on a weighted combination of parameters, including, among others, the variance in access frequency, geographical inconsistencies, privilege escalation attempts, and variations in resource interaction sequences.

[0037] In one embodiment, the decision processor (112) is configured to dynamically adjust the thresholds for anomaly detection based on historical false-positive rates and feedback from the feedback processing unit. This enables adaptive sensitivity to deviations in identity behavior.

[0038] In one embodiment, the interface for executing the corrective actions (114) is configured to communicate with the control systems of the cloud service provider via authenticated application programming interface calls secured by cryptographic credentials, and the corrective actions are executed without manual authorization.

[0039] In one embodiment, the interface for executing remedial actions (114) is further configured to selectively apply remedial actions based on the severity classification of detected anomalies, with low-severity anomalies triggering additional authentication requests and high-severity anomalies triggering an immediate revocation of the login credentials and termination of the session.

[0040] In one embodiment, the feedback processing unit (116) is configured to record the results of the corrective actions, including success status, execution latency, and subsequent identity behavior, and furthermore updates behavioral models by incorporating reinforcement signals derived from the results.

[0041] In one embodiment, the storage unit (106) comprises a volatile high-speed memory segment for real-time processing and a persistent non-volatile memory segment for long-term storage of identity activity data and audit logs.

[0042] The present invention relates to a cloud-native system for monitoring identity security. This system is implemented as a specialized computing device configured for the continuous collection, transformation, analysis, and remediation of identity-related activities in distributed computing environments. System operation is controlled by a sequence of coordinated computing processes executed on interconnected processing units. Each unit contributes to the formation of an adaptive and autonomous identity protection mechanism. The workflow begins with the collection of identity telemetry data via a data acquisition interface. This interface establishes secure communication channels to various external systems, including identity providers, access control services, container orchestration environments, and application interfaces.The incoming data includes heterogeneous event data sets such as authentication attempts, token issuances, role assignments, session activities, and resource access logs, each of which may be in different formats and structures.

[0043] Upon receiving the telemetry data, the preprocessing unit performs a series of transformation operations to standardize the data into a uniform representation. First, timestamp normalization is performed by converting all event timestamps into a consistent temporal reference. This enables the precise sequencing of identity activities. Next, identity correlation is performed, resolving multiple identifiers belonging to a single entity using attribute matching and mapping. The preprocessing unit also performs deduplication by generating hash representations of event attributes and removing redundant records with identical hash values. Finally, attributes are extracted to isolate relevant parameters such as user IDs, device signatures, geographic indicators, network addresses, and permission levels.These parameters are encoded into structured data objects that conform to a predefined schema, thus enabling efficient further processing.

[0044] The normalized data is stored in a storage medium that manages both temporary and persistent representations of identity activities. The process utilizes high-speed storage for immediate task processing while simultaneously persistently storing selected data for historical analysis and auditing purposes. The stored data forms the basis for constructing an identity relationship structure using a graph construction process. The graph construction technique iteratively processes incoming data objects and maps each entity, including users, devices, services, and resources, to corresponding nodes within a graph structure. Interactions such as authentication events, authorization actions, and access operations are represented as edges between the nodes.Each edge is assigned temporal attributes that specify the time of occurrence of the interaction, thus enabling a chronological arrangement of the events.

[0045] The graph construction processor continuously and incrementally updates the identity relationship structure by integrating new nodes and edges without rebuilding the entire graph. The process also performs multi-level traversal operations to identify indirect relationships between entities, thereby revealing complex interaction patterns such as lateral movement between resources. Connectivity metrics are calculated based on traversal depth, interaction frequency, and path density, and serve as indicators of anomalous behavior when compared to the baseline patterns.

[0046] The analysis processor performs a behavioral modeling procedure that creates baseline profiles for each identity based on historical activities stored in memory. The modeling process involves the statistical aggregation of identity interactions over a defined observation period. This includes calculating parameters such as average access frequency, typical resource usage patterns, geographic distribution, and temporal activity cycles. In addition to statistical aggregation, the procedure performs sequence analysis by encoding ordered interaction sequences and identifying recurring patterns that characterize normal behavior. These sequences are then compared with newly observed activity sequences to detect anomalies that might indicate potential threats.

[0047] Subsequently, an anomaly assessment procedure is applied to quantify deviations from established behavioral profiles. This procedure calculates individual deviation metrics based on various parameters, including frequency deviation (difference between observed and expected access numbers), geographic deviation (distance between typical and current access locations), privilege escalation deviation (changes in access levels), and sequence deviation (comparison of observed interaction sequences with reference patterns). Each deviation metric is weighted according to its relative importance, and an overall anomaly score is calculated as a weighted aggregation of these metrics. The assessment mechanism continuously processes incoming data, thus enabling real-time detection of abnormal identity behavior.

[0048] The decision processor receives the calculated anomaly scores and compares them to dynamically adjustable thresholds. Threshold adjustment incorporates feedback from previous detection results, including false positive and false negative rates, to optimize sensitivity. The decision processor categorizes anomalies into different severity levels based on the anomaly score and contextual factors such as the criticality of the accessed resources and the identity's authorization levels. After classifying an anomaly, the decision processor generates a sequence of corrective actions tailored to the severity and nature of the detected threat.

[0049] The vulnerability remediation interface translates the remediation instruction into executable control instructions and transmits them to external cloud systems via authenticated communication protocols. The remediation process ensures secure interaction with identity management services and cloud control interfaces through the use of cryptographic credentials and verification mechanisms. Depending on the severity, the actions performed can include invalidating authentication tokens, terminating active sessions, restricting access rights, enforcing additional authentication requirements, or modifying access control policies. The execution process is autonomous, requiring no manual intervention, thus minimizing response time and reducing the risk of threat propagation.

[0050] After corrective actions are implemented, the feedback processing unit collects outcome data such as execution success, latency, and subsequent identity behavior. The feedback technique analyzes this data to determine the effectiveness of the applied corrective actions and generate reinforcement signals used to update behavioral models in the analysis processor. This adaptive learning mechanism allows the system to improve recognition accuracy over time by incorporating real-world results into model refinement. The feedback processing unit also updates the threshold parameters in the decision processor to ensure optimal sensitivity in response to changing identity behaviors.

[0051] The system also features a secure communication mechanism that ensures encrypted data transmission between internal components and external systems. Cryptographic operations are accelerated by hardware-based processing capabilities, achieving high throughput without compromising security. The computing device is designed for use in a container environment, enabling the distributed execution of processing tasks across multiple instances. A load balancing mechanism assigns tasks to processing instances based on resource availability and processing requirements, thus achieving horizontal scalability and efficient utilization of computing resources.

[0052] The system also maintains an immutable log of all processed identity events, detected anomalies, and implemented corrective actions. Log generation captures each event with associated metadata such as timestamps, identity attributes, anomaly assessments, and action results, and stores the logs in a tamper-proof manner. This ensures traceability and supports compliance verification as well as forensic analysis.

[0053] By integrating data acquisition, preprocessing, graph construction, behavioral modeling, anomaly detection, decision-making, remediation, and feedback learning, the present invention provides a comprehensive technical framework for autonomous identity security monitoring. The continuous and adaptive nature of the method enables the system to detect complex identity-based threats, respond in real time, and adapt to changing operating conditions. This significantly enhances the security of cloud-native environments.

[0054] The present invention relates to a machine-implemented system in the form of a cloud-native computing device for monitoring identity security and autonomously remediating security vulnerabilities. The device comprises a data acquisition interface that is electrically connected to a distributed network communication system. This interface receives identity-related telemetry data streams from various cloud-based identity providers, access management services, container orchestration environments, and application programming interfaces. These telemetry data streams include authentication logs, authorization decisions, token issuance logs, session metadata, and user behavior data.

[0055] The received telemetry data is transferred to a preprocessing and normalization unit implemented in the device. This unit is configured to transform heterogeneous data formats into a uniform schema. Preprocessing includes timestamp synchronization, identity resolution, deduplication, and encoding of identity attributes such as user IDs, device fingerprints, geolocation indicators, and access permissions. The normalized data is then stored in a high-throughput storage structure that includes both volatile and non-volatile memory segments for real-time access and historical storage.

[0056] A graph construction processor is operationally connected to the storage structure and configured to generate dynamic identity relationship graphs. The processor encodes entities such as users, roles, devices, services, and resources as nodes and represents interactions such as login events, permission assignments, and API accesses as edges. The graph structure is continuously updated based on incoming telemetry data, thus enabling the temporal and contextual representation of identity activities in the cloud environment.

[0057] An analytical processing unit is integrated into the device and configured for behavioral modeling and anomaly detection of the generated identity graphs. The unit utilizes machine learning-based statistical inference techniques, including probabilistic modeling, clustering, and sequence analysis, to identify fundamental behavioral patterns for individual identities and groups. Deviations from these patterns are quantified using anomaly scoring mechanisms that consider parameters such as access frequency, geographic dispersion, privilege escalation attempts, and anomalies in resource interactions.

[0058] The device also includes a decision processor that evaluates anomaly values ​​based on dynamically adaptive thresholds. Once it is determined that a specific identity behavior exceeds a predefined or learned risk threshold, the processor generates a remediation action. This action is then transmitted to an interface for remediation execution, which is operationally connected to external cloud control systems and identity management services.

[0059] The vulnerability remediation interface is configured to autonomously enforce corrective actions without manual intervention. These actions include revoking authentication tokens, disabling user accounts, terminating active sessions, reducing access rights, enforcing multi-factor authentication, and adjusting access control policies. The interface uses secure communication protocols and authenticated API calls to interact with cloud service providers and perform real-time vulnerability remediation.

[0060] The computer system also includes a feedback processing unit that monitors the results of implemented corrective actions. This feedback is used to update behavioral models and refine anomaly detection parameters, thereby enabling adaptive learning and continuous improvement in detection accuracy. The system is also configured to maintain an audit log of all detected anomalies, corrective actions, and system decisions for compliance and forensic analysis.

[0061] The device is designed as a containerized machine instance that can be deployed in cloud orchestration environments such as Kubernetes clusters. The architecture supports horizontal scaling through the replication of processing units and distributed load balancing. The system also features fault tolerance mechanisms that ensure uninterrupted operation in the event of node failures through redundant processing paths and data replication.

[0062] The invention further comprises a secure communication bus that connects all internal components of the device, thereby ensuring encrypted data transmission and data integrity verification between the processing units. The device is also equipped with hardware-based security features, including Trusted Execution Environments and cryptographic accelerators, to improve data protection and processing efficiency.

[0063] The cloud-native identity security monitoring system continuously collects identity telemetry data, creates contextual identity graphs, detects anomalies using adaptive analytics models, and autonomously initiates countermeasures to mitigate security threats. The integration of real-time monitoring, graph-based analysis, and automated response enables the system to significantly reduce response time, minimize security gaps, and increase the overall resilience of cloud-native infrastructures.

[0064] The system components are implemented as tangible, hardware-based elements within a computer, thus fulfilling the system capability requirements by ensuring a clear physical realization rather than abstract functionality. The data acquisition interface includes, in particular, network interface circuits with physical communication ports, transceivers, and signal conditioning circuits configured to receive identity telemetry over wired or wireless channels. The preprocessing unit, graph construction processor, analysis processor, and decision processor are each implemented as one or more microprocessors, digital signal processors, or application-specific integrated circuits, interconnected via system buses and supported by clock and control circuits.Each processor performs low-level operations such as arithmetic calculations, bit transformations, and memory accesses to physically stored data. The memory unit comprises semiconductor memory elements such as volatile RAM cells and non-volatile memory arrays implemented with flash memory or similar persistent storage technologies, which physically store identity data and intermediate processing results. The problem-solving interface also includes hardware communication controllers and input / output circuits that generate and send electrical signals to external systems according to control instructions. The feedback processing unit is also integrated into a dedicated processing circuit that receives and processes electrical input signals along with data after the problem has been resolved.All connections between these components are implemented via conductive traces, buses and interface controllers to ensure that data transmission, processing and storage take place via specific electrical and electronic mechanisms. REFERENCES 100 A cloud-native identity security monitoring system implemented as a computer. 102 Data acquisition interface 104 Pre-processing unit 106 storage units 108 Graph construction processor 110 analytical processor 112 Decision processor 114 Interface for the execution of remediation measures 116 Feedback processing unit

Claims

[1] A cloud-native identity security monitoring system implemented as a computer device, consisting of: a data collection interface configured to receive identity-related telemetry data from a variety of distributed cloud resources, identity providers, and access management services over network communication channels; a preprocessing unit that is operationally coupled with the data acquisition interface and is configured to normalize the received telemetry data into a uniform data schema through timestamp matching, identity correlation, attribute extraction and structure transformation; a storage unit that is connected to the preprocessing unit and configured to store normalized identity data and historical records of identity activity; a graph construction processor that is operationally connected to the storage unit and configured to generate and continuously update an identity relationship structure that includes nodes representing identities, devices, services, and resources, as well as edges representing authentication events, authorization actions, and access interactions; an analytical processor coupled with the graph construction processor and configured to calculate behavioral profiles for the respective identities by analyzing temporal sequences, access frequencies, interaction patterns and context attributes, and furthermore configured to identify anomaly values ​​that indicate deviations from established behavioral profiles; a decision processor that communicates with the analysis processor and is configured to compare the anomaly values ​​with dynamically adjustable thresholds to identify anomalous identity activity; an interface for executing corrective actions, which is operationally connected to the decision processor and configured to send control instructions to external cloud systems to perform corrective actions, including invalidating credentials, terminating sessions, changing access rights, and enforcing additional authentication requirements; and a feedback processing unit configured to receive status data after the fix and update the analytical processor to refine behavioral models based on the results of the corrective actions taken. [2] System according to claim 1, wherein the preprocessing unit is further configured to perform identity event deduplication by identifying recurring authentication records by hash-based comparison of event attributes, and wherein the preprocessing unit encodes identity attributes including geographic indicators, device identifiers and network parameters into structured representations for subsequent processing. [3] System according to claim 1, wherein the graph construction processor is configured to maintain the temporal order of identity interactions by assigning time-indexed attributes to edges, and is further configured to update the identity relationship structure upon receiving new telemetry data in near real time without reconstructing the entire graph. [4] System according to claim 1, wherein the analytical processor is configured to generate behavioral models based on the statistical aggregation of identity activity over a configurable observation window and is further configured to include sequence-based pattern recognition to identify abnormal transitions between resources and services. [5] System according to claim 1, wherein the analytical processor is further configured to calculate anomaly values ​​based on a weighted combination of parameters, including, among others, the variation in access frequency, geographical inconsistencies, attempts at privilege escalation and variations in resource interaction sequences. [6] System according to claim 1, wherein the decision processor is configured to dynamically adjust the thresholds for anomaly detection based on historical false positive rates and feedback from the feedback processing unit, thereby enabling adaptive sensitivity to deviations in identity behavior. [7] System according to claim 1, wherein the interface for executing the remediation measures is further configured to selectively apply remediation measures based on the severity classification of detected anomalies, wherein low-severity anomalies trigger additional authentication requests and high-severity anomalies trigger an immediate revocation of the login credentials and termination of the session. [8] System according to claim 1, wherein the storage unit comprises a volatile high-speed memory segment for real-time processing and a persistent non-volatile memory segment for long-term storage of identity activity data and audit logs.

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