AI-powered cybersecurity system for regulatory compliance in energy distribution

Abstract

A system for AI-supported cybersecurity and regulatory compliance in energy distribution networks, consisting of: a hardware-embedded data acquisition module configured to intercept, capture, and time-stamp operational data streams and to control data traffic from SCADA (Supervisory Control and Data Acquisition) systems, AMI (Advanced Metering Infrastructure) systems, and energy management systems (EMS) via multiple communication protocols without operational latency; an FPGA-based deep packet inspection unit coupled with the data acquisition module, wherein the FPGA firmware is configured to perform line rate filtering, protocol decomposition and metadata extraction of the acquired data and forwards preprocessed packet data to an AI processing unit; an AI processing unit consisting of a multi-core central processing unit (CPU), a dedicated AI accelerator selected from a graphics processing unit (GPU) or a tensor processing unit (TPU), and a volatile memory buffer; a response orchestration module that is communicatively coupled with network management devices and operations controllers, wherein the response orchestration module is configured to perform automated security and compliance remediation measures, including network isolation of compromised segments, enforcement of protocol encryption, and privilege revocation; and an immutable audit logging subsystem configured to record all detected events, compliance assessments, and corrective actions in a blockchain-based distributed ledger, with each log entry cryptographically anchored with a secure hash value and digitally signed with keys stored in a secure hardware enclave.

Description

AREA OF INVENTION

[0001] The present invention relates to cybersecurity in critical infrastructure systems and, in particular, an AI-supported cybersecurity system for ensuring compliance with legal regulations in energy distribution networks. The invention integrates machine learning-based anomaly detection, cryptographically secured audit trails, and automated mechanisms for compliance testing, thereby protecting SCADA (Supervisory Control and Data Acquisition) and AMI (Advanced Metering Infrastructure) components in the energy distribution sector from both cyberattacks and compliance violations. Background of the invention

[0002] Energy distribution networks, consisting of substations, transformers, load management units, and customer-side metering systems, are becoming increasingly digitized and interconnected. While this digitization increases operational efficiency, it also increases the vulnerability of these networks to cyber threats such as distributed denial-of-service (DDoS) attacks, data manipulation, ransomware, and advanced persistent threats (APTs). Traditional cybersecurity systems for energy distribution are based either on static, rule-based intrusion detection systems or isolated, manual compliance checks. These approaches lack real-time adaptability, often fail to detect zero-day exploits, and struggle to maintain compliance in a rapidly evolving regulatory environment.Therefore, there is a need for an AI-driven, continuously learning cybersecurity system that integrates compliance auditing into its detection, response, and reporting pipelines, while also being directly connected to the power distribution hardware and control software.

[0003] Energy distribution networks form the backbone of modern civilization, ensuring a reliable supply of electricity to end users from generation sources via transmission and distribution infrastructure. Over the past two decades, these networks have undergone fundamental changes, evolving from isolated, analog-controlled systems to complex, digitally managed cyber-physical systems. The implementation of SCADA (Supervisory Control and Data Acquisition) systems, AMI (Advanced Metering Infrastructure) systems, and energy management systems (EMS) has enabled utilities to increase efficiency, reduce losses, and integrate renewable energy sources into the grid. However, this increasing digitalization and connectivity has also expanded the attack surface for cyber threats, creating new vulnerabilities that can be exploited by malicious actors.

[0004] Existing cybersecurity solutions for energy distribution networks traditionally rely on signature-based intrusion detection systems (IDS) and rule-based firewalls to detect and block malicious activity. While these tools are effective against known threats, they are inherently reactive, using predefined threat signatures or static rules to detect anomalies. Therefore, they are poorly suited to detecting novel, complex, or zero-day attacks that do not match existing patterns. Furthermore, many of these systems operate in isolation from regulatory compliance processes. Consequently, even when a cyber threat is detected, there is no integrated mechanism to assess whether the event constitutes a regulatory violation. There is also a lack of an optimized method for generating auditable audit logs for compliance authorities.This leads to a significant operational gap, as compliance monitoring is often manual and reliant on reviewing logs after the event rather than continuous real-time assessment.

[0005] Another category of existing solutions are Security Information and Event Management (SIEM) platforms. These aggregate logs and alerts from various sources and apply correlation rules to identify suspicious patterns. While SIEM systems are widely used across industries, their deployment in power distribution networks presents unique challenges. The sheer volume of real-time operational data from SCADA and AMI systems can overwhelm SIEM platforms, leading to performance bottlenecks, delayed detection, and false alarms.

[0006] Some utility companies have implemented compliance-oriented solutions in the form of governance, risk, and compliance (GRC) platforms that monitor adherence to regulatory standards and manage audit documentation. While these systems are excellent for structuring compliance evidence, managing documentation workflows, and tracking corrective actions, they are not pure security systems. They rely on manual input from operators or periodic data exports from other monitoring tools. Therefore, they cannot perform real-time compliance verification, and compliance violations that occur between reporting intervals may go undetected until the next scheduled audit. This time lag is particularly problematic in the context of evolving cybersecurity.Threats where even a short delay in remediation could lead to significant operational disruptions or regulatory sanctions.

[0007] A new approach to cybersecurity in critical infrastructure involves the use of anomaly detection systems that leverage statistical models or basic machine learning techniques to establish normal operating values ​​and identify deviations. While this represents an improvement over static, signature-based detection, many of these systems have limited adaptability. Baseline profiles must be manually retrained when network topology changes, new equipment is installed, or load patterns shift due to seasonal demand. In power distribution networks, where operating parameters constantly fluctuate due to load balancing, renewable energy integration, and demand response programs, static or semi-static baselines can generate numerous false alarms, leading to operator fatigue and reduced confidence in the system's alerts.Furthermore, these anomaly detection systems typically focus solely on identifying suspicious behavior without integrating a compliance assessment, leaving the burden of regulatory interpretation and reporting to separate processes.

[0008] Another layer of complexity arises from the heterogeneity of the power distribution infrastructure. A single utility may operate equipment from various manufacturers, spanning decades of technological development and running on a mix of modern Ethernet-based communication and legacy serial protocols. Many legacy devices lack built-in security features such as encryption, authentication, or firmware integrity checks, making them easy targets for attackers. Retrofitting these devices with modern security features is often technically challenging and economically impractical, especially if they are part of mission-critical systems that cannot simply be taken offline. Existing solutions that attempt to protect legacy equipment typically do so through network segmentation and perimeter protection.While these measures can reduce the risk, they cannot detect or prevent attacks originating from within the segmented zone or exploiting insider threats.

[0009] In addition to security challenges, complying with legal standards also brings operational burdens. The manual nature of these compliance activities often leads to inconsistencies in recording, incomplete documentation, and difficulties in demonstrating continuous compliance during unannounced audits. Traditional compliance tools are unable to automatically capture evidence from operational systems in real time and to reconcile this evidence with evolving regulatory frameworks. This gap increases the risk of violations, which can result in fines, reputational damage, and increased scrutiny by regulatory authorities.

[0010] Several research initiatives have explored the integration of artificial intelligence into the cybersecurity of industrial control systems, including power distribution networks. These AI-based systems utilize machine learning models to detect complex attack patterns, adapt to emerging threats, and reduce false positives. However, most current implementations are still in the trial or pilot phase and lack the robustness, explainability, and regulatory integration required for deployment in mission-critical infrastructure. A common limitation is that many AI models operate as "black boxes," issuing anomaly alerts without clear explanations or traceability. This hinders their acceptance by compliance officers and regulators, who demand evidence-based justifications for all operational actions.Furthermore, AI models trained with limited or non-representative datasets may not be applicable to different real-world scenarios occurring in different substations or geographical regions.

[0011] There are also solutions that attempt to combine security and compliance by integrating automated reporting capabilities into cybersecurity platforms. While these can accelerate the generation of auditable reports, they are often based on post-processed data and lack mechanisms for cryptographically securing the evidence to ensure its long-term integrity. Without tamper-proof storage, such reports can be challenged during audits, undermining their credibility. Furthermore, these solutions are typically designed as pure software platforms that must be installed on an existing IT infrastructure, which may not be optimized for processing high-throughput, low-latency operational technology (OT) data.This limits their ability to capture and analyze real-time control system traffic without causing latency or missing transient events.

[0012] Finally, the industry is increasingly recognizing that cybersecurity and regulatory compliance cannot be considered separately. Cyber ​​incidents often have a direct impact on compliance, and regulatory violations can expose systems to increased security risks. However, the current solution landscape reflects a fragmented approach: cybersecurity tools, compliance management systems, and operational monitoring platforms largely operate in isolation. This fragmentation leads to inefficiencies, increases operating costs, and leaves gaps that attackers can exploit. What is needed is a unified system that simultaneously detects security threats, assesses compliance in real time, generates immutable audit evidence, and automates both security responses and measures to address compliance requirements.Such a system must be able to operate with the speed and scalability required by modern energy distribution networks, integrate seamlessly with existing and modern equipment, and continuously adapt to evolving threat landscapes and regulatory requirements. Summary of the invention

[0013] The invention provides an AI-powered cybersecurity system that integrates hardware-based network monitoring modules with machine learning analysis engines to detect anomalies in the operational traffic of energy distribution systems. The system features a Compliance Knowledge Base (CKB) aligned with applicable legal frameworks, enabling real-time mapping of detected events and configurations to compliance requirements. Upon detection of deviations—whether from fundamental security principles or legal requirements—the system generates cryptographically secured compliance reports and, if necessary, initiates automated corrective actions.

[0014] The hardware device according to the invention is implemented as a modular rack unit and can be used in control centers of substations or distribution management centers. It features interfaces for high-throughput packet capture, FPGA-based cryptographic modules, and AI acceleration hardware (such as GPU or TPU cores). The device is networked via redundant high-bandwidth connections to SCADA master terminals, AMI headend systems, and energy management systems (EMS), thus ensuring low-latency data acquisition and analysis.

[0015] The main objective of the present invention is to provide an AI-powered cybersecurity system specifically tailored to energy distribution networks, ensuring operational safety and continuous regulatory compliance. The invention aims to overcome the limitations of conventional cybersecurity solutions by integrating advanced machine learning models with domain-specific protocol analysis to enable the real-time detection of known and unknown cyber threats. A further objective is to create a unified platform that bridges the gap between cybersecurity monitoring and compliance verification. This allows any detected anomaly, configuration change, or operational event to be automatically assessed against applicable regulatory requirements without the need for separate manual reviews.The invention also aims to provide a low-latency, hardware-integrated monitoring device that can be used in substation and distribution control environments. It utilizes FPGA-based packet capture and AI acceleration hardware to process large volumes of SCADA and AMI traffic without impacting operational performance. A further objective is the implementation of an immutable, cryptographically secured audit logging mechanism, such as a blockchain-based ledger, to ensure that all compliance evidence and security events are verifiable, tamper-proof, and admissible in regulatory audits. The invention also aims to support seamless integration with both modern Ethernet-based control systems and legacy serial devices, thereby ensuring comprehensive coverage of heterogeneous power distribution infrastructures.Furthermore, the invention aims to minimize false alarms and strengthen operator confidence through explainable AI techniques by providing clear, human-readable explanations for detected threats and compliance violations. Ultimately, the invention aims to establish a proactive, self-adapting cybersecurity and compliance framework that not only protects critical energy infrastructures from evolving cyber threats but also continuously demonstrates adherence to prescribed security standards. This reduces operational risks, avoids regulatory sanctions, and ensures uninterrupted and secure power distribution. BRIEF DESCRIPTION OF THE FIGURE

[0016] 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 consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of an AI-enhanced cybersecurity system for compliance with legal regulations in energy distribution.

[0017] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0018] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0019] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0020] References in this specification to “an aspect”, “another aspect”, or similar expressions 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, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0021] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0023] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0024] Fig.Figure 100 shows a block diagram of an AI-powered cybersecurity system for regulatory compliance in energy distribution. The system comprises: a hardware-embedded data acquisition module (102) configured to intercept, capture, and time-stamp operational data streams and to control data traffic from SCADA (Supervisory Control and Data Acquisition), AMI (Advanced Metering Infrastructure), and energy management (EMS) systems over multiple communication protocols, including IEC 60870-5-104, DNP3, and Modbus TCP, with zero operational latency; and an FPGA-based deep packet inspection unit (104) coupled to the data acquisition module, with the FPGA firmware configured to perform line rate filtering, protocol decomposition, and metadata extraction of the captured data and to forward preprocessed packet data to an AI processing unit.an AI processing unit (106) comprising a multi-core central processing unit (CPU), a dedicated AI accelerator (GPU) or tensor processing unit (TPU), and a volatile memory buffer, wherein the AI ​​processing unit executes a set of trained machine learning models, including: (i) a graph neural network (GNN) (106a) configured to analyze topology-aware communication patterns between nodes for anomaly detection in power distribution control networks; and (ii) a transformer-based time-series predictive model (106b) configured to predict operational parameters and detect anomalies indicative of cyber-physical threats;a Compliance Knowledge Base (CKB) (108) comprising a structured database of rules encoded as logical compliance predicates for compliance with legal regulations, wherein the AI ​​processing unit is further configured to evaluate detected anomalies, configuration states and operational behavior against the compliance predicates to determine the compliance status in real time; a response orchestration module (110) communicatively coupled with network management devices and operational controllers, wherein the response orchestration module is configured to perform automated security and compliance remediation actions, including network isolation of compromised segments, enforcement of protocol encryption and revocation of privileges;and an immutable audit logging subsystem (112) configured to record all detected events, compliance assessments and corrective actions in a blockchain-based distributed ledger, with each log entry cryptographically anchored with a secure hash value and digitally signed with keys stored in a secure hardware enclave.

[0025] In one embodiment, the FPGA-supported deep packet inspection unit (104) is also configured to perform hardware-level anomaly pre-filtering by comparing captured packet streams with a dynamically updatable set of protocol state machine templates stored in on-chip memory, immediately flagging deviations from valid state transitions and assigning them priority ratings for AI-based deep analysis.

[0026] In one embodiment, the Graph Neural Network (GNN) (106a) is configured to create a dynamic, directed graph of all communication endpoints within the power distribution network over a sliding observation window. The GNN employs message transmission techniques that incorporate boundary attributes such as protocol type, message frequency, and payload entropy to detect multi-stage cyberattack patterns spanning multiple substations.

[0027] In one embodiment, the transformer-based time series prediction model (106b) is configured to receive synchronized telemetry data from multiple distributed measurement points, normalize the telemetry data into a uniform temporal representation, and calculate attention-weighted deviation values ​​that distinguish between harmless operational fluctuations due to load balancing and malicious manipulation of sensor data that indicate attacks by injecting false data.

[0028] In one embodiment, the compliance knowledge base (108) is implemented as a version-controlled, signed rule repository that supports cryptographic verification of rule authenticity, with updates to the compliance rules being received as digitally signed delta packets from a trusted regulatory server, verified by the secure hardware enclave, and applied without system downtime.

[0029] In one embodiment, the immutable audit logging subsystem (112) is also configured to segment the blockchain-based ledger into compliance event chains and security event chains, with each chain anchored to an external compliance trust system via Merkle root commitments, enabling regulators to verify the authenticity of selected audit subsets without disclosing unrelated operational data.

[0030] In one embodiment, the AI ​​processing unit (106) also includes an explainability engine configured to generate human-readable justifications for each detected anomaly and compliance violation, the justifications containing a description of the triggering event, the specific compliance predicate that was violated, the confidence level of the AI ​​model, and a causal trace of the contributing network behaviors, thereby enabling regulatory acceptance of AI-driven decisions.

[0031] In one embodiment, the hardware-embedded data acquisition module (106) is structurally integrated into a rack-mounted 3U or 4U enclosure with industrial-grade EMI shielding, redundant power supplies, and a thermal management system consisting of dual redundant active cooling fans and dustproof intake filters, wherein the enclosure is designed for use in substation environments subject to vibration and electromagnetic interference.

[0032] In one embodiment, the AI ​​processing unit (106) supports federated learning across multiple distributed substation deployments, with model updates being transmitted in encrypted form using homomorphic encryption schemes, enabling collaborative training without sharing raw operational data, thereby preserving data privacy while improving model accuracy across different network topologies.

[0033] In one embodiment, the AI ​​processing unit participates in federated learning across multiple substation deployments by transmitting encrypted model updates using a homomorphic encryption scheme. This enables collaborative model improvement without transmitting raw operational data and ensures compliance with data retention regulations.

[0034] The present invention describes an AI-based cybersecurity system for regulatory compliance in energy distribution networks. It comprises both hardware- and software-based components for low-latency threat detection, regulatory compliance verification, and automated remediation. The system is deployed as a modular rack unit in energy distribution control environments such as substations or central distribution management centers and is designed for direct interface with SCADA (Supervisory Control and Data Acquisition) systems, AMI (Advanced Metering Infrastructure) headend systems, and energy management systems (EMS).

[0035] The operational process begins with a hardware-integrated data acquisition module that continuously intercepts, captures, and timestamps data traffic at the control and data levels. The acquisition process is contactless, ensuring that no operational latency enters the control systems. Data acquisition is performed via FPGA-based network interface cards whose firmware is specifically programmed to perform line-rate deep packet inspection. Within the FPGA logic, protocol state templates for supported industrial protocols are stored in high-speed on-chip memory. The captured packets are compared to these templates in real time, and any deviation from valid state transitions—such as unexpected function codes or faulty control frames—is flagged.Each flagged anomaly is assigned a priority rating based on the severity and duration of the deviation, thus enabling prioritized further processing.

[0036] The preprocessed packet streams are streamed directly to an AI processing unit that combines multi-core CPU processing with dedicated AI acceleration hardware such as a GPU or TPU and is supported by high-throughput volatile memory buffers. The AI ​​models deployed in this unit are trained to process both structural and temporal patterns in operational data. A graph neural network (GNN) is used to model the network's operational topology as a dynamic, directed graph, in which nodes represent communication endpoints (e.g., remote terminal units, smart electronic devices, and control servers) and edges represent communication channels with associated attributes such as protocol type, message frequency, payload size, and payload entropy. Through message transmission layers, the GNN propagates node and edge features across the graph to capture multi-hop dependency structures.This enables the detection of complex intrusion patterns that develop over several stages and may encompass multiple substations before manifesting as operational disruptions.

[0037] The GNN is complemented by a transformer-based time-series prediction model designed for continuous telemetry analysis. This model processes synchronized, time-stamped sensor readings, operational setpoints, and control outputs from multiple distributed sources. A normalization process balances these inputs on a uniform time base. Subsequently, the transformer model applies multi-head attention mechanisms to weight the influence of different time segments. This enables the differentiation between harmless deviations—such as those caused by normal load balancing—and malicious manipulations, such as attacks involving the injection of false data. The model generates attention-weighted deviation scores that indicate the probability that each observed change is attributable to a security incident.

[0038] As soon as anomalies or unusual behavior are detected, they are evaluated against a Compliance Knowledge Base (CKB) stored within the system. CKB updates are provided by trusted regulatory authorities in the form of digitally signed delta packages. The system's secure hardware enclave verifies the authenticity and integrity of these packages before the updates are applied without downtime. This ensures that compliance checks always meet the latest regulatory requirements. During operation, the AI ​​processing unit executes these compliance predicates based on the current operational state and detected anomalies, enabling real-time determination of whether compliance violations have occurred.

[0039] If a security threat or compliance violation is confirmed, the response orchestration module generates an appropriate remediation workflow. This can include sending commands to programmable logic controllers (PLCs) or software-defined networking (SDN) controllers to reconfigure the network topology, isolate compromised segments, or inject encryption into active, unencrypted control sessions without interrupting ongoing communication. For example, if a plaintext DNP3 session is detected and flagged as a compliance violation, the module can transparently encapsulate the ongoing session in a TLS tunnel, thus ensuring protocol security during operation. Similarly, if malicious traffic patterns are detected in a particular substation, that network segment can be quarantined via access control list updates sent directly to managed switches and firewalls.

[0040] All detected events, compliance assessments, and corrective actions are recorded by an immutable audit logging subsystem. This subsystem maintains a blockchain-based distributed ledger where each entry is cryptographically secured using a secure hash function and digitally signed with keys stored in a secure hardware enclave. The ledger is segmented into separate chains for security and compliance events. Each chain is linked to an external compliance trust system via Merkle root commitments. This allows regulators to verify the integrity of audit records without needing to access unrelated or sensitive operational data. This tamper-proof logging ensures that all compliance evidence remains verifiable and admissible during regulatory audits or forensic investigations.

[0041] An integral part of the system is the explainability engine, which operates in parallel with the AI ​​detection models. For each anomaly or compliance violation, the engine generates a structured, understandable explanation. This explanation includes the triggering network or system event, the violated compliance rule or predicate, the model's confidence level, and a causal trail of contributing behaviors. These explanations are accessible via a hardened human-machine interface (HMI) on the rack unit. This interface supports role-based access control and multi-factor authentication to prevent unauthorized access.

[0042] The system also supports federated learning across multiple substation locations. Instead of exchanging raw operational data, each location trains its AI models locally and transmits only encrypted model parameter updates using a homomorphic encryption scheme. This approach enables collective model improvement across the utility's entire infrastructure while ensuring privacy and compliance with data protection laws. The AI ​​training process incorporates continuous learning mechanisms to adapt to evolving attack patterns and changing operational fundamentals, thereby reducing the risk of model obsolescence.

[0043] The device is housed in a 3U or 4U rack enclosure with industrial-grade shielding against electromagnetic interference (EMI), redundant power supplies, and vibration-resistant mounting brackets for harsh substation environments. A thermal management system with redundant active fans and dust-tight filters ensures optimal operating conditions even in high-temperature and high-particle environments. Redundant optical and copper Ethernet interfaces, as well as serial ports for legacy SCADA devices, provide comprehensive connectivity.

[0044] By combining FPGA-based line-rate packet inspection, AI-driven anomaly detection, compliance rule evaluation, automated troubleshooting, and blockchain-based audit logging, the invention offers an integrated solution that unites security and regulatory compliance in a way unattainable with existing systems. By directly embedding compliance evaluation into the detection pipeline and cryptographically securing audit evidence, the system addresses the operational and regulatory challenges of modern energy distribution networks while ensuring resilience, low latency, and interoperability across heterogeneous infrastructures.

[0045] The drawing and the preceding description show examples of 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 embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0046] Advantages, further benefits, and problem solutions have been described above with regard to specific embodiments. However, the advantages, benefits, problem solutions, and all components that may lead to a particular advantage or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of any or all claims. REFERENCES 100 An AI-supported cybersecurity system for compliance with legal regulations in energy distribution. 102 Hardware-Integrated Data Acquisition Module 104 FPGA-supported Deep Packet Inspection Unit 106 AI processing units 106a Graph Neural Network (GNN) 106b Transformer-Based Time Series 108 Compliance Knowledge Base (CKB) 110 Response Orchestration Module 112 Immutable Audit Logging Subsystem

Claims

[1] A system for AI-supported cybersecurity and regulatory compliance in energy distribution networks, consisting of: a hardware-embedded data acquisition module configured to intercept, capture, and timestamp operational data streams and to control data traffic from SCADA (Supervisory Control and Data Acquisition) systems, AMI (Advanced Metering Infrastructure) systems, and energy management systems (EMS) over multiple communication protocols without operational latency; an FPGA-based deep packet inspection unit coupled with the data acquisition module, wherein the FPGA firmware is configured to perform line rate filtering, protocol decomposition and metadata extraction of the acquired data and forwards preprocessed packet data to an AI processing unit; an AI processing unit consisting of a multi-core central processing unit (CPU), a dedicated AI accelerator selected from a graphics processing unit (GPU) or a tensor processing unit (TPU), and a volatile memory buffer; a response orchestration module that is communicatively coupled with network management devices and operations controllers, wherein the response orchestration module is configured to perform automated security and compliance remediation measures, including network isolation of compromised segments, enforcement of protocol encryption, and privilege revocation; and an immutable audit logging subsystem configured to record all detected events, compliance assessments, and corrective actions in a blockchain-based distributed ledger, with each log entry cryptographically anchored with a secure hash value and digitally signed with keys stored in a secure hardware enclave. [2] System according to claim 1, wherein the FPGA-supported deep packet inspection unit is further configured to perform hardware-level anomaly pre-filtering by comparing captured packet streams with a dynamically updatable set of protocol state machine templates stored in on-chip memory, wherein deviations from valid state transitions are immediately flagged and priority ratings are assigned to them for AI-based deep analysis. [3] System according to claim 1, wherein the Graph Neural Network (GNN) is configured to create a dynamic, directed graph of all communication endpoints within the power distribution network over a sliding observation window, wherein the GNN applies message transmission techniques that incorporate edge attributes such as protocol type, message frequency and payload entropy to detect multi-stage cyberattack patterns spanning multiple substations. [4] System according to claim 1, wherein the immutable audit logging subsystem is further configured to segment the blockchain-based ledger into compliance event chains and security event chains, each chain being anchored to an external compliance trust system via Merkle root commitments, thereby enabling regulators to verify the authenticity of selected audit subsets without disclosing unrelated operational data. [5] System according to claim 1, wherein the AI ​​processing unit further comprises an explainability engine configured to generate human-readable justifications for each detected anomaly and compliance violation, the justifications comprising a description of the triggering event, the specific violated compliance predicate, the confidence score of the AI ​​model and a causal trace of the contributing network behaviors, thereby enabling regulatory acceptance of AI-driven decisions. [6] System according to claim 1, wherein the data acquisition module embedded in the hardware is structurally integrated into a rack-mounted 3U or 4U enclosure with industrial-grade EMI shielding, redundant power supplies and a thermal management system with dual redundant active cooling fans and dustproof intake filters, wherein the enclosure is designed for use in substation environments that are subject to vibration and electromagnetic interference. [7] System according to claim 1, wherein the AI ​​processing unit supports federated learning across multiple distributed substation deployments, with model updates being transmitted in encrypted form using homomorphic encryption schemes that enable collaborative training without sharing raw operational data, thereby preserving data privacy while improving model accuracy across different network topologies.

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