Multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning to defend against cyber threats

Abstract

A multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning for orchestrating cyber threats, comprising: a computing device comprising at least one central processing unit, a storage unit, a memory unit, and a network interface unit interconnected via a communication bus; a multi-cloud connectivity processor operationally connected to the network interface unit and configured to establish authenticated communication channels with a variety of cloud computing infrastructures, including public cloud servers, private cloud servers, and enterprise data centers, wherein the multi-cloud connectivity processor manages address mappings, endpoint identifiers, and routing configurations to enable file transfer communication across heterogeneous cloud environments;a file transfer coordination processor that is operationally connected to the storage unit and configured to receive file transfer requests from external computer systems, verify the integrity of the files, generate encryption parameters, segment the received file into multiple transfer packets, and schedule the transmission of the packets via the network interface unit to one or more target cloud infrastructures; an encryption processing unit configured to apply cryptographic encryption procedures to each transfer packet prior to transmission, the encryption processing unit also generating digital signatures and cryptographic hash values ​​associated with the packets to ensure the confidentiality and integrity of the transferred data;an artificial intelligence analysis processor configured to receive telemetry data related to file transfer activities, including network traffic characteristics, packet distribution information, authentication attempts, and historical transfer behavior, wherein the artificial intelligence analysis processor processes the telemetry data using in-memory machine learning models to identify anomalous behavior indicative of potential cyber threats;an agent-based decision processor that is operationally linked to the AI ​​analysis processor and configured to assess the risk levels associated with detected anomalies and autonomously determine defensive response measures, including interrupting ongoing file transfers, rerouting transmission paths, initiating additional authentication checks, or modifying encryption parameters; and a cyber threat defense processor configured to perform malicious payload inspection, cryptographic integrity verification, and quarantine procedures for suspicious data packets before completing file transfer operations.

Description

Technical field of the invention The present invention relates generally to the field of secure digital data transmission systems and networked computing infrastructures. In particular, it relates to a machine-based file transfer device and the associated system architecture for secure, managed file transfers across distributed cloud infrastructures. Specifically, the invention relates to a multi-cloud managed file transfer system equipped with agent-based artificial intelligence and mechanisms for orchestrating machine learning. This system is configured to detect, analyze, and defend against cyber threats during file transfers across heterogeneous computing environments.The invention further relates to a computing device and the associated processing architecture configured to autonomously coordinate file transfer operations, threat monitoring, anomaly detection, encryption management and adaptive security responses within a multi-cloud ecosystem. Background of the invention With the rapid spread of cloud computing infrastructures, companies are increasingly relying on multi-cloud environments in which data and applications are distributed across multiple cloud service providers. While such distributed architectures improve scalability and reliability, they also introduce significant security vulnerabilities during file transfers between internal company systems and external cloud services. Traditional file transfer solutions such as FTP, SFTP, or conventional Managed File Transfer (MFT) platforms typically rely on static security policies and centralized monitoring systems. These conventional approaches are inadequate for modern distributed infrastructures because they lack adaptive intelligence capable of detecting evolving cyber threats such as data exfiltration attempts, polymorphic malware, ransomware attacks, credential breaches, and lateral attacks. Furthermore, most existing file transfer solutions operate in single-cloud environments and lack the necessary interoperability mechanisms for secure transfers between heterogeneous cloud providers, including public, private, and hybrid clouds. Because file transfers traverse multiple networks and computing environments, the attack surface increases significantly, making it difficult to enforce consistent security policies. Another limitation of existing systems is the lack of autonomous decision-making mechanisms that can dynamically respond to detected threats. Most security infrastructures rely heavily on human intervention for threat analysis and remediation, which leads to delays and increases vulnerability to sophisticated cyberattacks. Recent developments in artificial intelligence and machine learning have revealed significant potential for detecting cyber threats. However, current implementations are often limited to security monitoring tools and are not tightly integrated with mechanisms for orchestrating file transfers. Therefore, while threat detection systems can identify anomalies, they cannot autonomously control or modify the file transfer process. Therefore, there is a need for a machine-based system that can integrate the functionality of managed file transfer with agent-based orchestration mechanisms of artificial intelligence, thus enabling autonomous threat detection, risk assessment, and adaptive response measures during multi-cloud data transfer operations. The rapid digital transformation of businesses, government agencies, and cloud-based service ecosystems has led to an exponential increase in the volume of data that needs to be exchanged across distributed computing environments. Organizations routinely transfer large amounts of sensitive information, such as financial data, health records, intellectual property, and operational datasets, between internal systems, external partners, and cloud service infrastructures. Therefore, secure and reliable file transfer mechanisms have become a critical component of modern IT infrastructures. In particular, Managed File Transfer (MFT) systems are frequently used to enable controlled file exchange while ensuring authentication, encryption, auditing, and regulatory compliance.These systems are intended to replace traditional file transfer protocols like FTP and HTTP, which historically lacked robust security features. However, the evolution of cloud computing architectures, particularly multi-cloud environments, has introduced new complexities that traditional managed file transfer solutions struggle to handle. Multi-cloud computing environments refer to IT infrastructures in which a company simultaneously uses services from multiple cloud providers, including public cloud platforms, private clouds, and hybrid infrastructures. This distributed architecture offers advantages such as redundancy, cost optimization, scalable performance, and provider diversification. However, using multiple cloud environments significantly increases the complexity of data transfer. Files often need to be transferred between heterogeneous cloud infrastructures that operate with different security policies, authentication mechanisms, network protocols, and data storage architectures. In many cases, companies rely on centralized Managed File Transfer Servers (MFTS) to coordinate these operations through predefined workflows and static policy configurations.Although these systems offer basic automation features, they are generally designed for relatively predictable network environments and therefore do not offer the flexibility required for effective operation in dynamic multi-cloud ecosystems. Common managed file transfer (MFT) solutions primarily rely on static, rule-based mechanisms to enforce security policies during file transfers. These policies typically define predefined rules regarding file size limits, authentication methods, encryption standards, and routing configurations. While such rule-based approaches provide a basic level of control, they are insufficient to defend against modern cyber threats with adaptive and polymorphic behavior. Sophisticated attackers frequently exploit subtle behavioral anomalies in file transfers, including irregular transfer frequencies, unusual packet distributions, or unauthorized access attempts. Traditional rule-based systems cannot effectively detect such anomalies because they rely on predefined signatures or manually configured thresholds.Consequently, many harmful activities remain undetected until a security incident occurs. Another limitation of existing file transfer infrastructures is the lack of real-time analytics capabilities capable of evaluating complex patterns in network traffic and file payload properties. Traditional systems primarily focus on ensuring secure transport using encryption protocols such as Secure File Transfer Protocol (SFTP), Transport Layer Security (TLS), or Secure Shell (SSH). While these protocols protect data during transmission, they lack mechanisms to analyze whether the transferred content itself is malicious or whether the transfer activity indicates potential cyber threats such as insider attacks, compromised credentials, or data exfiltration. As cyber threats become increasingly sophisticated, attackers frequently disguise malicious files within seemingly legitimate transfers or manipulate user credentials to gain unauthorized access to sensitive data.Traditional managed file transfer systems are not designed to detect such behavioral anomalies, as they primarily function as transport programs and not as intelligent security systems. Another significant challenge arises from the increasing prevalence of advanced persistent threats (APTs) targeting data transfer channels within organizations. APTs typically involve coordinated attack campaigns where attackers gain access to networks via compromised credentials, phishing attacks, or malware infections, and then gradually exfiltrate sensitive data through legitimate communication channels. File transfer infrastructures are frequently exploited in these attacks because they provide authorized pathways for transferring large volumes of data outside the organization. While existing security systems such as firewalls and intrusion detection systems offer some monitoring, they often operate independently of file transfer management platforms. This separation creates gaps in visibility, allowing malicious activity to occur without triggering appropriate countermeasures. Traditional managed file transfer systems rely heavily on manual intervention as soon as unusual activity is detected. Security administrators must analyze alerts, assess potential threats, and manually initiate countermeasures, such as halting transfers or revoking user privileges. This manual response process leads to significant delays that attackers can exploit to continue their malicious activities. In modern, high-speed data environments, where file transfers can occur continuously across multiple cloud regions, manual defenses are insufficient to prevent rapid cyberattacks. Automated response capabilities are therefore essential to immediately contain suspicious activity before it escalates into widespread security incidents. Another drawback of current file transfer infrastructures is the limited interoperability between different cloud providers. Each provider typically implements its own authentication frameworks, access control models, and network communication protocols. As a result, organizations often use separate integration mechanisms for each cloud environment, leading to fragmented management systems lacking unified visibility. This fragmentation makes monitoring file transfers across multiple clouds difficult and increases the risk of misconfigured security policies. In many cases, administrators must manually configure transfer paths between cloud providers, leading to operational inefficiencies and increasing the likelihood of configuration errors that can compromise sensitive data. Existing systems are reaching their limits when processing large volumes of data in distributed cloud environments. As businesses generate ever-increasing amounts of data through applications such as IoT platforms, real-time analytics systems, and machine learning pipelines, file transfer workloads are becoming highly dynamic and unpredictable. Traditional architectures for managed file transfers often rely on centralized servers that process transfer requests sequentially or with limited parallelization mechanisms. This centralized design can lead to performance bottlenecks when large file transfers occur simultaneously across multiple cloud endpoints. Furthermore, centralized architectures represent potential sources of failure that can interrupt file transfers in the event of system outages or cyberattacks. Another challenge lies in detecting malicious file content embedded in legitimate data transfers. Conventional file transfer systems typically use simple virus scanners that only become active after the files have been received on the target systems. These scanning methods reach their limits when detecting newly emerging malware variants, especially those that use obfuscation techniques or polymorphic code structures to evade signature-based detection. As a result, malicious files can successfully spread through file transfer systems before detection mechanisms can identify the threat. Furthermore, many existing solutions lack mechanisms for adaptive learning from past transmission behavior. Security policies are typically configured statically and do not evolve in response to changing operational patterns or emerging cyber threats. Consequently, systems can generate a large number of false positives when legitimate transmission activity deviates from predefined thresholds. Too many false positives can overwhelm security administrators and impair the effectiveness of monitoring systems. Conversely, thresholds that are too low can allow malicious activity to go undetected. Current research explores the use of artificial intelligence and machine learning to improve cybersecurity systems. Machine learning techniques such as anomaly detection, clustering, and classification models have demonstrated significant potential in identifying unusual network behavior. However, many existing implementations function as separate analysis platforms that evaluate log data only after events have occurred. These systems typically lack direct integration with file transfer infrastructures. This means that even upon detecting anomalies, the system cannot automatically adjust or interrupt ongoing file transfer operations. This lack of autonomous response capabilities significantly limits the practical effectiveness of AI-based threat detection systems. Despite the availability of various managed file transfer solutions and cybersecurity monitoring tools, current technologies are insufficient to address the security challenges of multi-cloud data exchange environments. Existing systems lack the integrated intelligence to continuously monitor transfer behavior, detect anomalies, and autonomously initiate countermeasures in real time. Therefore, there is a need for an advanced file transfer architecture that combines multi-cloud connectivity with intelligent cyber threat detection and autonomous response mechanisms. Such a system would offer enhanced visibility, adaptive learning capabilities, and automated defense strategies to ensure secure and reliable data transfer across modern distributed cloud infrastructures. Summary of the invention The present invention discloses a multi-cloud managed file transfer device and system that integrates agent-based artificial intelligence and machine learning orchestration mechanisms for defense against cyber threats. The invention relates to a machine structure comprising a processing unit, memory circuits, network interface circuits, storage components, encryption hardware and an artificial intelligence processing circuit configured to coordinate secure file transfers across multiple cloud infrastructures. The system includes a multi-cloud connectivity processor configured to establish authenticated communication channels with multiple cloud service providers and distributed enterprise servers. The connectivity processor manages dynamic routing tables and cloud endpoint mappings to control file transfers in heterogeneous network environments. The invention further comprises a file transfer coordination processor configured to segment, encrypt, schedule and transfer data files via specific cloud nodes, while maintaining transaction integrity, transfer logging and verification records. To defend against cyber threats, the invention integrates an AI analysis processor that implements machine learning methods for anomaly detection, behavioral profiling, and threat classification. The AI ​​processor continuously monitors file transfer patterns, network traffic signatures, authentication attempts, and data payload characteristics to identify potentially malicious activity. Furthermore, an agent-based decision processor is provided for the autonomous coordination of the response to threats. This processor is configured to dynamically modify transmission operations by suspending suspicious transmissions, rerouting data paths, initiating additional encryption protocols, or triggering authentication procedures. In addition, the system has a cyber threat defense processor configured to coordinate multiple defense measures, including sandbox inspection, malicious payload detection, cryptographic integrity checking, and quarantine mechanisms. The invention thus provides a self-adaptive, managed file transfer machine that is capable of continuously monitoring, analyzing and securing data transfers across multi-cloud infrastructures without requiring manual intervention. The main objective of the present invention is to provide a multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning for defense against cyber threats, wherein a computer device is configured to securely coordinate file transfers across heterogeneous cloud infrastructures while continuously monitoring transfer activities for potential cyber threats. Another objective of the present invention is to provide a machine-assisted file transfer device capable of establishing secure communication channels with multiple cloud service providers, including public cloud, private cloud and hybrid cloud environments, thereby enabling seamless and secure data transfer across distributed computing infrastructures. Another objective of the invention is to provide a processing architecture configured to intelligently manage file transfer operations, with a file coordination processor performing tasks such as file segmentation, encryption, integrity checking, and controlled transfer to ensure reliable and secure delivery of digital files between computing nodes. Another objective of the present invention is to provide an artificial intelligence processing unit configured to analyze real-time telemetry data related to file transfer operations, including network traffic patterns, packet characteristics, user authentication behavior, and transmission frequency patterns, in order to identify anomalous or suspicious activities that may indicate potential cyber threats. Another objective of the invention is to provide a machine learning analysis mechanism that is able to continuously learn from historical transfer activities, generating behavioral models to determine basic operational patterns and automatically identify deviations from the expected file transfer behavior. Another objective of the invention is to provide an agent-based decision processor that is capable of independently orchestrating defensive measures when detecting suspicious activities, with the system dynamically evaluating risk levels and initiating corrective measures, such as interrupting file transfers, redirecting data packets via alternative cloud paths, activating extended encryption protocols, or triggering additional authentication verification procedures. Another objective of the present invention is to provide a defense mechanism against cyber threats that is directly integrated into the file transfer infrastructure, wherein transmitted data packets are examined for harmful payloads using automated analysis methods, including sandbox execution environments, cryptographic verification routines and anomaly detection techniques. Another objective of the invention is to provide a system that can reduce the dependence on manual administrative intervention in cybersecurity incidents by having intelligent decision processors automatically implement threat mitigation measures in real time without the need for human oversight. Another objective of the invention is to provide a distributed transfer routing mechanism that is able to dynamically select optimal cloud transfer paths based on factors such as network latency, availability of cloud services, security policies and system load conditions, thereby improving operational efficiency and system reliability. Another objective of the present invention is to provide a machine structure capable of maintaining comprehensive transaction logs and security event records, storing and analyzing detailed transmission logs to support auditing requirements, regulatory compliance, and the continuous improvement of machine learning models used for threat detection. Another objective of the invention is to provide a secure data exchange architecture that is capable of detecting attempts at data exfiltration, unauthorized access and the injection of malicious files during file transfer operations, thereby preventing cyberattacks that exploit legitimate file transfer channels. Another objective of the invention is to provide a scalable file transfer device capable of supporting the transfer of large amounts of data across geographically distributed cloud environments while ensuring consistent enforcement of security and performance optimization. Another objective of the present invention is to provide a computer device capable of integrating AI-driven orchestration with encryption management and network monitoring mechanisms, thereby enabling a unified and intelligent approach to securing data transmission operations in enterprises. Another objective of the invention is to provide a self-adaptive, managed file transfer system that can continuously improve its threat detection capabilities by updating machine learning models based on newly observed transfer behavior and cyber threat patterns. Accordingly, the invention aims to provide a technically advanced, intelligent and autonomous multi-cloud file transfer device that improves data security, operational resilience and the ability to defend against cyber threats in distributed cloud computing environments. BRIEF DESCRIPTION OF THE IMAGE 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. 1 shows a block diagram of a multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning for defense against cyber threats. 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 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. 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. 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. 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. 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. Embodiments of the present disclosure are described in detail below with reference to the attached drawing. Figure 1 shows a block diagram of a multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning for defense against cyber threats. The system 100 comprises: a computing device (102) with at least one central processing unit, a storage unit, a memory unit, and a network interface, interconnected via a communication bus; a multi-cloud connectivity processor (104) operationally connected to the network interface unit and configured to establish authenticated communication channels with a variety of cloud computing infrastructures, including public cloud servers, private cloud servers, and enterprise data centers, with the multi-cloud connectivity processor managing address mappings, endpoint identifiers, and routing configurations to enable file transfer communication across heterogeneous cloud environments;a file transfer coordination processor (106) that is operationally connected to the storage unit and configured to receive file transfer requests from external computer systems, verify the integrity of the file, generate encryption parameters, segment the received file into multiple transfer packets, and schedule the transmission of the packets via the network interface unit to one or more target cloud infrastructures; an encryption processing unit (108) that is configured to apply cryptographic encryption procedures to each transfer packet prior to transmission, the encryption processing unit also generating digital signatures and cryptographic hash values ​​associated with the packets to ensure the confidentiality and integrity of the transferred data;an artificial intelligence analysis processor (110) configured to receive telemetry data related to file transfer activities, including network traffic characteristics, packet distribution information, authentication attempts and historical transfer behavior, wherein the artificial intelligence analysis processor processes the telemetry data using in-memory machine learning models to identify anomalous behavior that indicates potential cyber threats;an agent-based decision processor (112) that is operationally connected to the AI ​​analysis processor and configured to assess the risk levels associated with detected anomalies and autonomously determine defensive response measures, including suspending ongoing file transfers, rerouting transmission paths, initiating additional authentication verification procedures, or modifying encryption parameters; and a cyber threat defense processor (114) that is configured to perform a malicious payload inspection, a cryptographic integrity check, and quarantine procedures for suspicious data packets prior to the completion of file transfer operations. In one embodiment, the file transfer coordination processor (106) is further configured to perform a data segmentation procedure in which a received file is divided into a plurality of variable-length packets based on predetermined packet size thresholds and network bandwidth conditions, with each packet being assigned a packet identification value and a sequence index that enable the reconstruction of the file on a target computer system. In one embodiment, the encryption processing unit (108) is configured to generate cryptographic keys using a secure key generation procedure stored in memory, wherein the encryption processing unit applies symmetric encryption procedures and digital signature authentication procedures to each segmented data packet before it is transmitted over the network interface. In one embodiment, the AI ​​analysis processor (110) is configured to create behavioral profiles for authorized file transfer operations by analyzing historical transfer logs stored in the storage unit. The behavioral profiles include parameters such as average transfer frequency, packet size distribution, source address patterns, destination address patterns, and user authentication characteristics. In one embodiment, the AI ​​analysis processor (110) continuously compares the real-time telemetry data acquired during ongoing file transfer operations with the stored behavior profiles and generates anomaly indicators when deviations are detected that exceed predetermined thresholds. In one embodiment, the agent-based decision processor (112) is configured to implement reinforcement learning-based decision guidelines stored in the memory unit. These reinforcement learning guidelines determine optimal threat mitigation measures based on historical response results and dynamically updated risk assessments generated by the AI ​​analysis processor. In one embodiment, the cyber threat defense processor (114) is configured to redirect suspicious data packets to a sandbox inspection environment implemented within the computer, wherein the sandbox inspection environment executes the packet payload in an isolated virtual environment to identify malicious executable behavior. In one embodiment, the multi-cloud connectivity processor (104) is further configured to manage dynamic routing tables containing cloud endpoint identifiers, network latency measurements, and indicators of cloud resource availability, and wherein the multi-cloud connectivity processor selects a transfer path for each file transfer based on the routing tables. In one embodiment, the routing tables are periodically updated based on network telemetry data collected by the network interface unit. The routing tables are used to dynamically redirect file transfer packets when cyber threats or network congestion are detected. In an embodiment further comprising a transfer logging processor configured to generate detailed transaction logs related to file transfer operations, wherein the logs include packet transmission timestamps, encryption parameters, authentication events, anomaly detection events, and threat mitigation measures. In one embodiment, the system functions as a Managed File Transfer (MFT) infrastructure, supporting secure file exchange between heterogeneous computing environments. The file transfer coordination processor, together with the multi-cloud connectivity processor, forms a secure file transfer orchestration architecture that manages file transfers between enterprise systems and various cloud service providers. This architecture supports cross-cloud file transfer operations by dynamically establishing communication sessions between different cloud endpoints. Transfer workflows are planned, monitored, and validated through automated orchestration processes within the computing device.During these processes, the encryption unit and the processor work together to defend against cyber threats, verifying data integrity through cryptographic hash comparisons and digital signature validation, thus ensuring that the transferred files remain unchanged during transmission across the distributed infrastructure. In another embodiment, the system is implemented in a multi-cloud architecture developed according to the principles of cloud-native applications. The computing device acts as a central orchestration node and interacts with distributed cloud services via secure communication interfaces. The operational architecture can be deployed as a full-stack architecture, encompassing infrastructure services, network services, application interfaces, and security control layers. Internally, processing functions for file transfer coordination, encryption, AI analysis, and cyber threat defense can be logically organized in a microservices architecture. Each processing function executes independently and communicates via secure interprocess communication channels.This architecture improves scalability and fault tolerance, and enables the independent scaling of processors for file transfer coordination, AI analysis, and defense against cyber threats depending on system load. In another embodiment, the system uses an API-driven architecture to enable secure integration with enterprise applications, cloud platforms, and external partner systems. File transfer requests can be initiated via the application programming interfaces (APIs) provided by the endpoint. These requests are first validated through a secure API gateway configured to enforce communication security policies. The gateway performs authentication and authorization procedures before granting access to the file transfer services. These procedures can include verifying digital certificates, access tokens, and proof of identity of the requesting entities. Upon successful authentication and authorization, the request is forwarded to the file transfer coordinator for further processing.The use of an API-driven architecture enables seamless integration of the managed file transfer system into enterprise workflows, automated business processes and cloud-based applications while maintaining high security. In another embodiment, the AI ​​analytics processor and the agent-based decision processor jointly implement an agent-based AI framework that orchestrates machine learning for threat analysis and automated operational decision-making. Within this framework, machine learning models are continuously trained using historical transmission telemetry data and security event logs stored in system memory. The models analyze patterns related to file transfer behavior, network traffic distribution, authentication attempts, and endpoint access. When suspicious behavior is detected, the machine learning orchestration mechanism works with the agent-based decision processor to determine appropriate countermeasures.These measures can include dynamically redirecting transfers to alternative cloud nodes, interrupting suspicious file transfer sessions, or initiating advanced audit procedures by the cyber threat defense processor. Through this integrated cyber threat defense, the system autonomously protects file transfer operations from threats such as unauthorized access attempts, data exfiltration, and the injection of malicious payloads, while ensuring secure and reliable data exchange in multi-cloud environments. The present invention relates to a multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning for defending against cyber threats. The system is implemented as a computer device that coordinates secure file transfers across distributed cloud infrastructures while continuously monitoring transfer activities for potential cyber threats. It consists of a central processing unit (CPU), memory, data storage, a network interface, and several specialized processors interconnected via a communication bus. The memory stores operating instructions, machine learning models, routing configurations, cryptographic parameters, and behavioral data sets required for secure file transfers.The network interface enables communication with various cloud computing infrastructures, including public and private cloud servers as well as enterprise data centers, via secure communication protocols. The computer device can thus act as an intelligent control node, monitoring file transfers in heterogeneous network environments. During operation, the system receives a file transfer request from a source system via the network interface. This request contains metadata about the file, source identification information, authentication data, and identifiers of the target cloud endpoints. First, the request is processed by the authentication verification processor, which validates the associated credentials. The authentication verification processor performs several verification procedures, including password authentication, digital certificate validation, and verification of one-time authentication tokens generated by external authentication services. The authentication processor also compares the user identity and the attributes of the transfer request with the authorization records stored in memory to ensure that the requesting entity is authorized to perform the file transfer.Only after successful authentication is the request forwarded to the file transfer coordination processor for further processing. The file transfer coordinator performs an integrity check of the received file. It calculates a cryptographic base hash value for the entire file using a predefined hash algorithm stored in memory. This hash value serves as a reference for subsequent integrity checks. Following the integrity check, the file transfer coordinator segments the received file into multiple transfer packets. This segmentation takes into account network conditions, packet size thresholds, and transfer efficiency parameters stored in system memory to determine the optimal packet size. Each packet generated by the segmentation is assigned a unique packet identification number and a sequence index to ensure the correct restoration of the file at the destination. After segmentation, the transmission packets are forwarded to the encryption unit. This unit generates cryptographic keys using a secure key generation process implemented in system memory. The encryption process applies symmetric encryption techniques to each packet, transforming its contents into encrypted data segments suitable for secure transmission over external networks. In addition to encryption, the encryption unit generates digital signatures and cryptographic hash values ​​for each packet. These cryptographic signatures allow target systems to verify the authenticity and integrity of the received packets during reconstruction. The encrypted packets are then forwarded to the multi-cloud connectivity processor, which determines the optimal transmission paths across the available cloud infrastructures. The connectivity processor maintains dynamic routing tables containing endpoint IDs for cloud service providers, network latency metrics, bandwidth availability metrics, and resource utilization indicators. These routing tables are continuously updated using telemetry data from the network interface. Based on these routing tables, the connectivity processor selects one or more transmission paths for each packet to optimize transmission speed, reliability, and compliance with security requirements. In certain circumstances, packets of the same file may be transmitted across different cloud infrastructures to improve transmission efficiency and reduce congestion. While packets are transmitted over the network interface, telemetry data is continuously collected and forwarded to the AI ​​analysis processor. This telemetry data includes parameters such as transmission frequency, packet size distribution, user access behavior, authentication activities, network latency variations, and destination address patterns. The AI ​​analysis processor transforms the collected telemetry information into structured feature vectors that represent the operational characteristics of the ongoing file transfer session. The AI ​​analysis processor uses machine learning models stored in system memory to analyze the generated feature vectors. These models include behavior clustering and anomaly detection techniques trained on historical transmission logs from memory. During the training phase, historical data representing legitimate transmission behavior is used to create baseline behavior profiles. These profiles contain statistical representations of normal operating parameters, including expected packet distribution patterns, typical user access frequencies, and authorized network endpoints. During operation, the AI ​​analysis processor compares real-time feature vectors generated from telemetry data with the baseline behavior profiles stored in memory. The processor calculates deviation scores that quantify the extent to which the observed transmission behavior deviates from the expected patterns. If the deviation scores exceed predefined thresholds stored in memory, the processor generates anomaly indicators representing potential cyber threats. Examples of detected anomalies include unusual transmission rates, unexpected changes in packet forwarding patterns, unauthorized destination addresses, or irregular authentication attempts. Once an anomaly indicator is generated, the AI ​​analysis processor forwards the anomaly information to the agent-based decision processor. This processor assesses the severity of the anomaly using risk assessment procedures implemented in system memory. The processor calculates a threat risk score by combining various factors such as the anomaly severity, historical incident data, user permission levels, and the current network security status. Based on this calculated risk score, the processor determines the appropriate countermeasures to mitigate the potential threat. If the risk score exceeds a predefined threshold, the decision processor initiates one or more countermeasures. One possible response is to instruct the file transfer coordination processor to temporarily suspend the ongoing transfer session. Another response is to instruct the multi-cloud connectivity processor to reroute the transfer path via alternative cloud endpoints to isolate potentially compromised network segments. In case of suspected unauthorized access, the processor can instruct the authentication verification processor to perform additional authentication checks before the transfer continues. If anomaly analysis indicates that the transmitted file might contain malicious payload, the decision processor forwards the relevant packets to the cybersecurity processor. This processor performs a detailed payload inspection. Suspicious packets are redirected to a sandbox environment implemented on the computer. There, the payload is executed in an isolated virtual environment to observe its behavior without affecting the operating system. The sandbox environment monitors the executed payload for suspicious activities such as unauthorized memory access, the creation of abnormal processes, or network communication attempts with external systems. If malicious behavior is detected during sandbox testing, the cybersecurity processor initiates a quarantine that prevents the suspicious packets from being delivered to the target system. The processor also generates security alerts and logs the event in the storage system's transaction log database. These logged events are then used to update the machine learning models managed by the AI ​​analytics processor. This allows the system to improve its ability to detect similar threats in future operations. Throughout the entire file transfer process, the transfer logging processor continuously records detailed transaction information, including packet transmission timestamps, routing decisions, encryption parameters, authentication events, anomaly detection events, and countermeasures taken by the agent-based decision processor. The stored logs serve various purposes, such as compliance audits, operational diagnostics, and retraining machine learning models. The AI ​​analytics processor regularly retrieves the stored transaction logs and retrains the machine learning models to account for newly identified transfer patterns and cyber threat behaviors. Through the coordinated interaction of a file transfer coordination processor, encryption processing unit, multi-cloud connectivity processor, AI analysis processor, agent-based decision processor, and cyber threat defense processor, the system achieves autonomous monitoring and protection of file transfer operations in distributed cloud environments. The integrated architecture enables the system to continuously analyze operational behavior, detect anomalies indicative of cyber threats, and automatically implement countermeasures without manual intervention. Thus, the invention provides a robust and intelligent machine-based solution for the secure management of multi-cloud file transfers with advanced cyber threat defense capabilities. The present invention relates to a machine-based, multi-cloud-enabled system for managed file transfer that utilizes agent-based artificial intelligence and machine learning to orchestrate cyber threats. The system is implemented as a dedicated computing device and comprises a structure of processors, communication circuits, memory units, and storage media configured to securely coordinate file transfer operations across distributed cloud infrastructures. The device comprises a central processing unit connected to a high-speed system bus, enabling communication between computing components and peripherals. The processing unit is connected to a memory system that includes volatile and non-volatile storage, where operating instructions, threat detection models, encryption parameters, and network configurations are stored. The device also includes a network interface unit configured for communication with various cloud computing environments, including public cloud servers, private cloud infrastructures, and enterprise data centers. The network interface unit supports various communication protocols, including HTTPS, Secure Shell protocols, and encrypted API-based communication channels to ensure secure data exchange. During operation, the system receives file transfer requests via authenticated application interfaces or enterprise gateway connections. Upon receiving a file transfer request, the file transfer coordinator performs a series of preprocessing steps, including verifying file integrity, extracting metadata, and generating encryption parameters. The file data is then divided into multiple transmission packets using a data segmentation technique that increases transmission security and enables detailed monitoring of data flows. Each packet is encrypted with cryptographic protocols such as AES-256 encryption and associated digital signatures to ensure confidentiality and integrity during transmission. Simultaneously, the multi-cloud connectivity processor determines optimal transmission paths based on the availability of cloud resources, latency metrics, and security policies. This processor manages dynamic routing tables that map enterprise nodes to cloud endpoints. While file packets are transferred over networks, the AI ​​analysis processor continuously monitors several telemetry inputs, including network traffic characteristics, packet size distributions, user access patterns, authentication behavior, and historical transfer protocols. The AI ​​processor implements machine learning models, including anomaly detection, behavior clustering, and classification models trained to distinguish between legitimate transfer patterns and suspicious activity. Feature vectors representing transfer characteristics are continuously generated and compared to baseline operating patterns stored in the system. If the AI ​​processor detects anomalous behavior that exceeds a defined risk threshold, the event is forwarded to the agent-based decision processor for autonomous response assessment. The agent-based decision processor applies reinforcement learning-based decision strategies and probabilistic risk assessment procedures to identify appropriate risk mitigation measures. Possible measures include temporarily suspending file transfer sessions, rerouting transfer paths via alternative cloud nodes, initiating additional authentication checks, or regenerating encryption keys. If malicious user data is suspected, the file data is automatically forwarded to a sandbox analysis environment, where the contents are executed in an isolated virtual computing environment to detect potentially harmful behavior. The system also includes a processor for defending against cyber threats, which implements mechanisms for verifying cryptographic integrity, including hash comparison procedures, certificate validation, and digital signature verification. If tampering is detected, the affected data packets are immediately quarantined. The system also maintains a comprehensive log database that records all transmission events, security alerts, routing decisions, and threat mitigation measures. These logs are used to continuously retrain the machine learning models, enabling the system to adapt to new cyber threats. Furthermore, the system supports adaptive orchestration mechanisms where multiple AI decision agents work together to optimize transmission efficiency while ensuring compliance with security standards across multiple cloud providers. Through this architecture, the invention enables a self-managing file transfer engine that is capable of autonomously detecting, analyzing, and mitigating cyber threats during multi-cloud data exchange operations, thereby significantly improving data security and operational resilience in distributed computing environments. 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. 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 A multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning for defense against cyber threats. 102 Computing device 104 Multi-cloud connectivity processor 106 File transfer coordination processor 108 Encryption processing unit 110 Artificial intelligence analysis processor 112 Agent decision processor 114 Cyber ​​threat defense processor

Claims

A multi-cloud managed file transfer system with agent-based artificial intelligence and machine learning for orchestrating cyber threats, comprising: a computing device comprising at least one central processing unit, a storage unit, a memory unit, and a network interface unit interconnected via a communication bus; a multi-cloud connectivity processor operationally connected to the network interface unit and configured to establish authenticated communication channels with a variety of cloud computing infrastructures, including public cloud servers, private cloud servers, and enterprise data centers, wherein the multi-cloud connectivity processor manages address mappings, endpoint identifiers, and routing configurations to enable file transfer communication across heterogeneous cloud environments;a file transfer coordination processor that is operationally connected to the storage unit and configured to receive file transfer requests from external computer systems, verify the integrity of the files, generate encryption parameters, segment the received file into multiple transfer packets, and schedule the transmission of the packets via the network interface unit to one or more target cloud infrastructures; an encryption processing unit configured to apply cryptographic encryption procedures to each transfer packet prior to transmission, the encryption processing unit also generating digital signatures and cryptographic hash values ​​associated with the packets to ensure the confidentiality and integrity of the transferred data;an artificial intelligence analysis processor configured to receive telemetry data related to file transfer activities, including network traffic characteristics, packet distribution information, authentication attempts, and historical transfer behavior, wherein the artificial intelligence analysis processor processes the telemetry data using in-memory machine learning models to identify anomalous behavior indicative of potential cyber threats;an agent-based decision processor that is operationally linked to the AI ​​analysis processor and configured to assess the risk levels associated with detected anomalies and autonomously determine defensive response measures, including interrupting ongoing file transfers, rerouting transmission paths, initiating additional authentication checks, or modifying encryption parameters; and a cyber threat defense processor configured to perform malicious payload inspection, cryptographic integrity verification, and quarantine procedures for suspicious data packets before completing file transfer operations. System according to claim 1, wherein the file transfer coordination processor is further configured to perform a data segmentation procedure in which a received file is divided into a plurality of variable-length packets based on predetermined packet size thresholds and network bandwidth conditions, with each packet being assigned a packet identification value and a sequence index that enable the reconstruction of the file on a target computer system. System according to claim 1, wherein the encryption processing unit is configured to generate cryptographic keys using a secure key generation method stored in memory, and wherein the encryption processing unit applies symmetric encryption methods and digital signature authentication methods to each segmented data packet before it is transmitted over the network interface. System according to claim 1, wherein the AI ​​analysis processor is configured to create behavioral profiles related to authorized file transfer operations by analyzing historical transfer logs stored in the storage unit, wherein the behavioral profiles include parameters such as average transfer frequency, packet size distribution, source address patterns, destination address patterns, and user authentication characteristics. System according to claim 4, wherein the AI ​​analysis processor continuously compares real-time telemetry data acquired during ongoing file transfer operations with the stored behavioral profiles and generates anomaly indicators when deviations exceeding predetermined thresholds are detected. System according to claim 1, wherein the agent-based decision processor is configured to implement reinforcement learning-based decision guidelines stored in the memory unit, wherein the reinforcement learning guidelines determine optimal threat mitigation measures based on historical response results and dynamically updated risk assessments generated by the AI ​​analysis processor. System according to claim 1, wherein the processor is configured to defend against cyber threats by redirecting suspicious data packets to a sandbox inspection environment implemented within the computer, wherein the sandbox inspection environment executes the packet payload in an isolated virtual environment to identify malicious executable behavior. System according to claim 1, wherein the multi-cloud connectivity processor is further configured to manage dynamic routing tables that include cloud endpoint identifiers, network latency measurements and indicators of cloud resource availability, and wherein the multi-cloud connectivity processor selects a transfer path for each file transfer based on the routing tables. System according to claim 8, wherein the routing tables are periodically updated using network telemetry data acquired by the network interface unit, and wherein the routing tables are used to dynamically redirect file transfer packets to respond to detected cyber threats or network congestion conditions. The system according to claim 1 further comprises a transfer logging processor configured to generate detailed transaction logs in connection with file transfer operations, wherein the logs include packet transmission timestamps, encryption parameters, authentication events, anomaly detection events and threat mitigation measures.

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