AI-powered anomaly detection system for high-volume managed file transfers

Abstract

A real-time anomaly detection system for high-volume managed file transfers (MFT), consisting of: a secure, hardware-accelerated monitoring unit configured to interface with a managed file transfer server and intercept file transfer session data at wire-speed; a metadata extraction engine embedded in the hardware-accelerated unit, the metadata extraction engine configured to analyze protocol-specific session attributes, including, but not limited to, file size, transfer duration, encryption status, source and destination endpoints, transfer frequency, and payload entropy; a contextual AI inference engine communicatively coupled to the metadata extraction engine, the AI inference engine comprising a deep learning model trained on labeled historical MFT activity logs to detect contextual deviations from normative behavior; a federated learning architecture with a plurality of edge nodes, each hosting a local anomaly detection model trained on localized transmission metadata and configured to synchronize with a central aggregator using differentially private gradient updates; an Explainable AI (XAI) subsystem integrated into and configured to generate human-readable anomaly justifications, feature importance maps, and threat categorization labels; and a policy orchestration module configured to dynamically execute pre-configured or AI-based security responses, where the security responses include selective session termination, quarantining of transferred files, generation of alerts, or redirection of MFT workflows.

Description

Field of the InventionThe present invention relates to the field of data security and integrity protection in enterprise IT systems, and more particularly to an artificial intelligence based system for anomaly detection in managed file transfer (MFT) operations of high volume.BACKGROUND OF THE INVENTIONCompanies and authorities routinely perform high volume automated file transfers over geographically distributed systems and cloud services. These transmissions often contain enterprise critical data and confidential content and are therefore a preferred goal for cyber attacks, data corruption, misconfigureds, and insider threats. While conventional solutions for managed file transfers provide scheduling, logging, and encryption mechanisms, they often do not have enhanced, adaptive anomaly detection functions that can proactively identify subtilian behavior-based threats. Existing systems are reactive and are based strongly on static post-transmission rules or audits that cannot keep pace with the dynamics and advancement of modern data exchange environments. Moreover, the increasing complexity of hybrid IT ecosystems with local servers, public clouds, and edge devices complicates anomaly detection solely by software-based or centralized monitoring systems.Managed file transfer (MFT) systems with a high data volume have long been indispensable components of corporate IT infrastructure and allow structured and unstructured data to be transferred securely and on plan over departments, organizations and regulatory limits. These systems are of decisive importance in industries such as banks, healthcare, authorities, logistics and telecommunications, since terabytes of sensitive data must be transmitted there daily under severe service level agreements (SLAs) and compliance conditions. The main goals of MFT platforms are to ensure reliability, security, verification, and traceability of data movement between endpoints. Traditionally, these platforms are based on secure transmission protocols such as SFTP, FTPS, HTTPS, AS2, and MLLP, and often integrate authentication schemes, digital certificates, PGP encryption, scheduling engines, and large logging mechanisms.Despite the maturation of MFT systems, explosive growth of data sets, heterogeneous IT landscapes, and complex cyber threats have exceeded the possibilities of conventional rule- or signature-based security monitoring integrated into legacy MFT tools. Existing MFT solutions focus mainly on static configurations, deterministic workflows and subsequent logging. These functions, while providing some reliability, are insufficient to dynamically identify anomalous or malicious activities that mimic legitimate transmission patterns. For example, insider threats, such as unauthorized data infiltration over authorized accounts, are known to be difficult to detect when acting within protocol boundaries and time slots. Also, zero day attacks, command injection from protocol abuse, and data corruption from subtil payload changes cannot be effectively combated by preconfigured rules or fixed validation mechanisms.Additionally, as companies increasingly employ hybrid cloud environments and distributed microservices architectures, conventional MFT systems have difficulty maintaining consistent monitoring and control. File transfers have meanwhile traversed complex paths with multiple intermediate nodes, cloud storage endpoints, containerized services, and third party integration levels. This complexity results in numerous vulnerabilities and blind spots, such that static monitoring tools cannot accurately map the entire data flow. In addition, conventional logging systems continue to have difficulties, due to lack of semantic perception, even with extensive transmission protocols, in differentiating between normal deviations in the workflow and safety-risk anomalies.Several commercial security products attempt to close this gap by integrating security information and event management (SIEM) solutions or data loss prevention (DLP) tools with MFT systems. SIEM platforms aggregate protocols from different sources and apply correlation rules to detect patterns indicative of threats. Although SIEM systems are useful for retrospective analysis, they have high false positive rates, delayed detection, and limited adaptability. They also depend strongly on the completeness and quality of the data received from MFT platforms, which themselves often lack the contextual depth of their event streams. Moreover, SIEM rules are manually curved and are difficult to keep pace with attackers' developing policies, techniques, and methods (TTPs), particularly when exploiting this legitimate infrastructure and user login information.Data loss prevention tools, on the other hand, attempt to analyze file contents and classify sensitive data prior to transmission. However, DLP tools typically focus on endpoint protection and provide limited support for real-time network-level anomaly detection. They also result in performance degradations and scalability issues in analyzing large amounts of files, particularly in high data throughput enterprise environments. Moreover, DLP tools are often limited to predefined content templates and keyword-based matching. They lack the necessary flexibility to detect contextual anomalies such as unusual file motion behavior, unusual transfer targets, or time shifts in activity patterns.To improve threat detection, some solutions incorporate rule-based anomaly detection systems in which administrators define acceptable base values for file sizes, timings, sources, and destinations. Although these systems provide some level of automated control, they are fundamentally vulnerable and require continuous optimization. Static rules cannot account for the inherent variability of enterprise file flows, as legitimate transmissions may deviate from the standards due to business requirements, third party integrations, or seasonal patterns. This susceptibility often results in alarm fatigue or inadvertent blocking of legitimate events, which reduces operating efficiency and prohibits trust of the users in the monitoring system.Recent research results suggest the integration of machine learning into MFT monitoring to achieve behavior-based anomaly detection. In this case, statistical models are typically trained on the basis of metadata of earlier file transmissions, for example on the basis of file size, frequency, duration, source, destination and protocol use. Although conceptually promising, such solutions are subject to some limitations in practice. First, many of these models are trained using limited data sets that may not be transmitted to enterprise environments or evolving threat landscapes. Second, most research prototypes do not operate in real time or on the required scale and do not meet the high throughput requirements of enterprise MFT systems. Third, the models often ignore deeper protocol semantics, payload entropy, or session dynamics that are indispensable for detecting complex threats such as data corruption, hidden channels, or encrypted command and control communication embedded in legitimate transmission protocols.Moreover, clarity and trustworthiness remain significant challenges when employing AI-based recognition systems. Many existing approaches treat the AI model as a black box and offer only limited interpretation possibilities for why a particular transmission is marked as abnormal. This lack of transparency complicates the response to incidents and gives rise to concern in regulated industries where the verification and reasoning of each action of automated systems is mandatory. Without AI functions that can be elucidated, safety analyzers are forced to rely on trial and error or assumptions. This increases the time and labor required for the solution and reduces the effectiveness of automated defense mechanisms.A further great disadvantage of current approaches is their centralisation. Conventional anomaly detection solutions pass all data to a central processing machine, resulting in performance bottleneck, data protection concerns, and individual failure points. In view of increasingly decentralized IT ecosystems-including on-premium, hybrid and multi-cloud implementations-the need for a federated, edge-enabled recognition system has become more urgent. Centralized systems are often unable to process distributed data sources in real time or quickly adapt to local threats that may not appear significant at the global level, but strongly suggest a violation in a particular context.Moreover, conventional MFT systems rarely have hardware-based detection or inline line speed protocol monitoring. Most solutions are based on software-based logging and analysis that is vulnerable to bypass techniques such as protocol manipulation, encrypted tunneling, and out-of-band exfiltration. Without a dedicated and tamper-proof hardware component that can verify transmission sessions in real-time and in a secure environment, these systems remain vulnerable to domestic and advanced persistent threats (APTs).The lack of contextual learning, real-time inference, hardware security, and federated information exchange results in significant vulnerabilities and complicates effective threat detection. There continues to be an urgent need for an intelligent, integrated system that combines the speed and security of hardware monitoring with the flexibility and depth of AI-based analyses to effectively protect managed file transfers in modern digital enterprise.SUMMARY OF THE INVENTIONThe present invention overcomes the foregoing limitations by introducing a system for AI-based anomaly detection in managed, high volume file transfers that operates in real time and scalable. The system includes both a physical detection unit-the so-called Abnormality Detection and Transfer Integrity Monitoring Unit (ADT-IMU)-and a software-based AI engine that uses federated machine learning and deep temporal modeling to identify irregularities in file transfer patterns, payload data, session metadata, and protocol behavior. The hardware unit is designed for the inline or mirrored node in the network and equipped with high bandwidth ports, secure embedded processing modules, and autonomous firmware-based inspection subsystems. The AI engine is trained using historical transmission data, protocol usage patterns, and behavior baselines to detect anomalies indicative of insider threats, zero day exploits, corrupted payload, misrouted files, and non-compliant access attempts.The system may automatically perform predefined or dynamically generated remedial action such as quarantines of transmissions, administrator notification, or initiation of rollback procedures, while simultaneously logging the telemetry data of the incidents in a secure audit ledger. Integration with enterprise orchestration tools and security information and event management (SIEM) platforms ensures seamless adaptation to existing IT environments. Moreover, the invention supports encrypted federated learning mechanisms that allow companies to benefit from common intelligence models without jeopardizing privacy.The primary object of the present invention is to provide a robust, smart and adaptive system for anomaly detection in high volume managed file transfers (MFTs) that can operate in real time in complex, distributed enterprise environments. The object of this invention is to overcome the limitations of existing rule-based, signature-based and static behavior models. To do so, advanced artificial intelligence (AI) and machine learning techniques are integrated that continuously learn, adapt, and refine recognition mechanisms based on context, history, and real-time data. Another object of the invention is to introduce a hybrid architecture that seamlessly integrates software and hardware components. The hardware layer is configured to perform inline inspection, metadata acquisition, and cryptographic integrity checks in a secure tamper resistant environment, thus providing resistance to protocol manipulations and bypass policies.Another object of the invention is to detect a broad spectrum of anomalies - including behavioral deviations, time offsets, unauthorized targets, occluded channel attempts, payload entropy anomalies, and use unusual protocols - without appreciable performance penalty or extensive manual control configurations. The system is designed for operation in federated environments and allows decentralized learning and decision making at edge nodes while simultaneously synchronizing superordinate threat models with the central inference engine. Another important goal is to improve the clarity and interpretability of the anomaly detection pipeline through the integration of clearable AI modules (XAI). These provide transparent reasons for warning messages and thus allow a more rapid response to incidents, better verification and compliance with legal regulations.Moreover, the invention aims to provide an anomaly detection AI-based framework that is protocol independent and can process both structured and unstructured file types in different transmission formats such as SFTP, FTPS, HTTPS, AS2, and others, regardless of the underlying infrastructure or cloud platform. Another object of this invention is to provide dynamic policy orchestration functions based on risk assessment and threat categorization. This allows the system to trigger automated responses, such as quarantine of a file, stopping a session, alerting security operations, or adjusting workflow paths according to predefined or AI-based security protocols. By coping with these multi-layer objectives, the invention seeks to provide a consistent anomaly detection system tailored to modern MFT ecosystems that require high throughput, zero trust resiliency, scalability, and useful information.BRIEF DESCRIPTION OF THE FIGURESThese and other features, aspects and advantages of the present invention will become more fully understood by reading the following detailed description when taken in conjunction with the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: FIG. 1 shows a block diagram of a system for AI-based anomaly detection in managed high volume file transfers.Those skilled in the art will also appreciate that the elements in the drawing are shown for simplicity and are not necessarily to scale. For example, the flowcharts illustrate the method using the key steps to improve understanding of aspects of the present disclosure. Also, as for the construction of the apparatus, individual or plural components of the apparatus may be represented by conventional symbols in the drawing. The drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art after the present description.DETAILED DESCRIPTION OF THE INVENTIONIn order to promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be described in an comprehensible manner. However, the scope of the invention is not limited thereby. Changes and further modifications of the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to a person skilled in the art.It will be understood by 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 intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the phrases "in one embodiment," "in another embodiment," and similar phrases in this specification may or may not refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also other steps not expressly listed or inherent in that process or method. Likewise, the phrase "comprises... for" one or more devices, subsystems, elements, structures, or components does not exclude, without further limitations, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by one of ordinary skill in the art. The systems, methods, and examples provided herein are for illustrative purposes only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.Referring now to FIG. 1, a block diagram of a system for AI-based anomaly detection in managed, high volume file transfers is shown. The system 100 includes: a secure hardware accelerated monitoring unit (102) configured to communicate with a managed file transfer server and to intercept line speed file transfer session data; a metadata extraction engine (104) embedded in the hardware accelerated unit configured to analyze protocol specific session attributes including file size, transfer duration, encryption status, source and destination endpoints, transfer frequency, and payload entropy; a contextual AI inference engine (106) communicatively coupled to the metadata extraction engine. The AI inference engine includes a deep learning model trained on labeled MFT historical activity protocols to detect contextual deviations from normative behavior; a federated learning architecture (108) having a plurality of edge nodes, each hosting a local anomaly detection model trained on localized transmission metadata and configured to be synchronized to a central aggregator using differentially private gradient updates; a system (110) with an clearable AI (XAI) integrated with the inference engine and configured to generate human readable anomaly associations, feature importance maps, and threat categorization labels, and a policy orchestration module (112) configured to dynamically perform preconfigured or AI-derived security responses, wherein the security responses include selective session termination, quarantines of transmitted files, alarm generation, or redirection of MFT workflows.In one embodiment, the secure hardware accelerated monitoring unit (102) includes a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC) configured to support inline decryption, signature validation, and transport level protocol decomposition in real-time without introducing latency of more than 5 milliseconds per gigabyte transmission throughput.In one embodiment, the metadata extraction engine (104) also includes a semantic entropy profiler that computes Shannon entropy metrics over segmented payload blocks to identify potential obfuscated or encrypted infiltration attempts, and identifies sessions where the entropy thresholds deviate from the normal historical ranges associated with similar file types.In one embodiment, the deep learning model in the contextual AI inference engine includes a hybrid architecture incorporating a short-term session modeling temporal convolutional network (TCN) and a long short-term memory (LSTM) network for modeling long-term behavioral drifts, the architecture being trained using a sliding window technique over time-series sequences of file transfer metadata.In one embodiment, the federated learning architecture (108) implements secure aggregation protocols and homomorphic encryption during model synchronization such that model updates are aggregated in an encrypted domain, thereby preventing sensitive session features from running down to the central aggregator.In one embodiment, the XAI subsystem (110) uses SAPLEY ADDITIVE (Temporal Explanation) values and counterfactic explanations to provide for each detected anomaly an ordered set of contributing session parameters and a proposed change that would render the session non-anomalous among the current model parameters.In one embodiment, the policy orchestration module (112) includes a stateful policy engine configured to trigger multi-level mitigation responses based on the anomaly trust score, the file criticality score, the compliance tags, and the real-time user authentication context, the responses escalate from soft alert through hard transmission prohibitions by programmable security workflows.In one embodiment, the system is also configured to monitor encrypted sessions without decrypting content by correlating encrypted metadata patterns, transmission behavior fingerprints, and TLS handshake anomalies using unsupervised clustering to detect anomalies in non-visibility contexts.In one embodiment, the contextual AI inference engine ( 106) includes reinforcement learning to update policy reward functions based on feedback loops to respond to incidents, whereby the model may learn operationally acceptable false positive rates and dynamically adjust anomaly detection thresholds accordingly.In one embodiment, the system is also configured for operation in heterogeneous environments consisting of on-site servers, containerized cloud-native MFT platforms, and hybrid VPN tunneled architectures, wherein normalization of session metadata is done using a uniform data scheme independent of the underlying protocol, operating system, or provisioning model.The present invention provides a comprehensive and highly scalable system for AI-based anomaly detection in high volume managed file transfers (MFTs) designed for operation in complex, hybrid enterprise environments with heterogeneous transmission protocols and distributed operating nodes. The core part of the invention is a hardware-based monitoring unit which is integrated into the MFT infrastructure and can be configured with a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). This unit captures session data streams in real time and performs protocol aware reassembly and metadata extraction without causing latencies over 5 milliseconds per gigabyte. This maintains the high throughput standards expected in enterprise-wide MFT workflows.The extracted metadata is streamed to a contextual AI inference engine that includes a hybrid deep learning architecture specifically tuned to recognition of behavioral anomalies. The inference engine consists of two closely integrated components: a Temporal Convolutional Network (TCN) optimized for profiling short-term transfer behavior, and a Long Short-Term Memory (LSTM) network trained to model long-term behavioral deviations across users, systems, and workflows. Training of this hybrid model is offline using a sliding window time-series strategy, in which the network learns to associate legitimate and anomalous sequences of metadata vectors within identified historical data sets. The model is regularly retrained to integrate newly marked anomalies. Its performance is monitored using precision-redial metrics and Area Under Curve (AUC) statistics to ensure robustness and low false positive rates.To support scalability and decentralized deployment, the system has a federated learning architecture. Each edge node in the system hosts a local model instance that is independently trained from metadata from that node's transmissions. These local models communicate with a central federated aggregator that differentially aggregates private gradients. The model updates are performed using homomorphic encryption to ensure that raw metadata or session specific information is not exchanged between the nodes. This decentralized learning approach allows the system to acquire organization-specific transmission patterns and at the same time contribute to a global anomaly model without jeopardizing data protection or compliance with legal regulations.Upon detection of an anomaly, the system forwards it to the policy orchestration module, which determines and initiates a corresponding countermeasure. This module contains a stateful policy engine that associates risk categories with predefined or AI-based response protocols. Each anomaly is evaluated for reliability, severity, file criticality, and user behavior, and the response is dynamically adjusted. For example, a highly reliable anomaly involving attempting to transfer a highly sensitive file to an unknown external domain may trigger immediate session termination and file quarantines, while a less reliable anomaly may only trigger a soft alert in a low risk session.The system also includes an enhanced learning mechanism that adjusts policy thresholds and response strategies based on the feedback of incident response teams and operative baselines. Each policy result is logged, evaluated, and used to fine tune the reward function that controls the detection sensitivity. This mechanism ensures that the system complies with an enterprise's operational tolerance to false alarms and continuously adjusts to changed network behavior and threat surfaces.The system is highly modular in design and can be used in cloud-native environments, local data centers, or hybrid configurations. To enable consistent anomaly detection in various environments, a protocol independent normalization layer is incorporated that converts all session metadata to a uniform format prior to analysis. This allows the AI models to operate seamlessly across different protocols and infrastructure types without requiring retraining for each environment.Thanks to this detailed architecture, the present invention provides a technically advanced, secure, comprehensible and adaptive system for AI-based anomaly detection in managed file transfer environments. It overcomes the limitations of existing static rule-based systems by introducing intelligent contextual models and a tightly integrated hardware software co-design for high performance operation. The ability of the system to continuously learn to adapt and respond autonomously makes it suitable for enterprise environments with stringent requirements of data integrity, compliance, and threat mitigation.The AI engine is first trained using annotated transmission protocols that reflect both normal and abnormal behavior, including unauthorized access patterns, frequency shifts, time-of-day anomalies, content entropy spikes, and protocol violations. The training phase uses federated learning protocols in which encrypted feature representations are regularly exchanged with a central coordination server. This allows model creation across multiple deliveries without sacrificing sensitive data.After deployment, the AI engine continuously operates in inference mode and identifies each transaction with a risk score and the associated anomaly vector. If a predefined or AI-determined threshold is exceeded, the system initiates a series of actions, including suspension of file transfer, connection termination, operator alert generation, or automatic damage limitation by integration with third party orchestration systems. The system manages a cryptographically signed incident protocol, which is stored in a secure safe and thus ensures traceability and audit readiness.The physical structure of the ADT IMU is designed for use in standard 19 inch data center racks and has redundant hot swap enabled power supplies, SSD memory for forensic data collection, and modular expansion slots for future upgrades. An internal watchdog mechanism provides continuous operation of all inspection and inference subsystems and provides fallback routing functions in the event of a device failure.The invention relates to secure data transmission systems, and more particularly to systems and methods for intelligent anomaly detection in managed file transfer (MFT) operations using artificial intelligence. It integrates technologies from machine learning, cybersecure, federated computing, and hardware accelerated telemetry analysis to identify file transfer behavior variances that may be indicative of potential threats, policy violations, or operational irregularities. The invention finds application in enterprise environments where high volume, protocol diversity, and latency sensitive file transfers are critical and smart anomaly detection mechanisms are essential in real-time to ensure data integrity, compliance, and operational continuity.The drawings and the foregoing description show examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Moreover, the actions of a flow chart need not be performed in the order shown; nor do all actions necessarily need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is by no means limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages and solutions to problems have been described above with reference to specific embodiments. However, the advantages, merits, solutions to problems and any components that may result in an advantage, merit or solution being introduced or enhanced are not to be understood as critical, required or essential features or components of individual or all claims.REFERENCES100 A system for AI-based detection of anomalies In managed file transfers High volume 102 Secure hardware accelerated monitoring unit 104 Metadata extraction engine 106 Contextual AI inference engine 108 Federated learning architecture 110 AI subsystem (XAI) 112 Policy orchestration module

Claims

A system for real-time anomaly detection in high volume managed file transfers (MFT), comprising: a secure hardware accelerated monitoring unit configured to interface with a managed file transfer server and to intercept line speed file transfer session data; a metadata extraction engine embedded in the hardware accelerated unit, wherein the metadata extraction engine is configured to analyze protocol specific session attributes including, but not limited to, file size, transfer duration, encryption status, source and destination endpoints, transfer frequency, and payload entropy; a contextual AI inference engine communicatively coupled to the metadata extraction engine, the AI inference engine comprising a deep learning model trained using labeled historical MFT activity protocols to detect contextual deviations from normative behavior; a federated learning architecture having a plurality of edge nodes each hosting a local anomaly detection model trained using localized transmission metadata and configured to synchronize with a central aggregator using differentially private gradient updates; an enlarifiable AI (XAI) subsystem integrated with the inference engine configured to generate human readable anomaly associations, feature importance maps, and threat categorization labels; A policy orchestration module configured to dynamically perform preconfigured or AI-based security responses, wherein the security responses comprise selective session termination, quarantines of transmitted files, generation of alerts, or redirection of MFT workflows.The system of claim 1, wherein the secure hardware accelerated monitoring unit comprises a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) configured to support inline decryption, signature validation, and transport level protocol decomposition in real time without introducing latency greater than 5 milliseconds per gigabyte transmission throughput.The system of claim 1, wherein the metadata extraction engine further comprises a semantic entropy profiler that computes Shannon entropy metrics over segmented payload blocks to identify potential obfuscated or encrypted infiltration attempts, and identifies sessions where the entropy thresholds deviate from the normal historical ranges associated with similar file types.The system of claim 1, wherein the deep learning model in the contextual AI inference engine comprises a hybrid architecture incorporating a temporal convolutional network (TCN) for modeling short-term sessions and a long short-term memory (LSTM) network for modeling long-term behavioral drifts, the architecture being trained using a sliding window technique over time-series sequences of file transfer metadata.The system of claim 1, wherein the federated learning architecture implements secure aggregation protocols and homomorphic encryption during model synchronization such that model updates are aggregated in an encrypted domain, thereby preventing sensitive session features from flowing down to the central aggregator.The system of claim 1, wherein the XAI subsystem uses SAP (Shapley Additive exPlan) values and counterfactic explanations to provide for each detected anomaly an ordered set of contributing session parameters and a proposed change that would render the session non-anomalous among the current model parameters.The system of claim 1, wherein the policy orchestration module comprises a stateful policy engine configured to trigger multi-level mitigation responses based on the anomaly trust score, the file criticality score, the compliance tags, and the real-time user authentication context, the responses escalating from soft alarms through hard transmission prohibitions through programmable security workflows.The system of claim 1, wherein the system is further configured to monitor encrypted sessions without decrypting content by correlating encrypted metadata patterns, transmission behavior fingerprints, and TLS handshake anomalies using unsupervised clustering to detect anomalies in non-visibility contexts.The system of claim 1, wherein the contextual AI inference engine includes reinforcement learning to update policy reward functions based on feedback loops to respond to incidents, whereby the model can learn operationally acceptable false positive rates and dynamically adjust anomaly detection thresholds accordingly.The system of claim 1, wherein the system is further configured for operation in heterogeneous environments comprising on-site servers, containerized cloud-native MFT platforms, and hybrid VPN tunneled architectures, wherein normalization of session metadata is performed using a unified data scheme independent of the underlying protocol, operating system, or provisioning model.

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