A system for securing cloud-based large language models through cyber threat defense and compliance monitoring

Abstract

A system (100) for securing cloud-based large language models through cyber threat defense and compliance monitoring, comprising: (a) an access control and authentication module configured to authenticate users and applications using role-based access control (RBAC) and multi-factor authentication (MFA); (b) a data protection and encryption module configured to encrypt data in transit and at rest and to anonymize sensitive input / output data using cryptographic techniques; (c) a threat detection and behavior monitoring module employing machine learning algorithms to identify anomalies and detect potential cyber threats in real time;(d) a regulatory compliance monitoring module configured to continuously assess system operations against applicable regulatory frameworks and generate alerts in the event of deviations from the regulations; (e) an incident response and mitigation module configured to automatically execute predefined response actions upon detection of threats or regulatory violations; (f) a logging, auditing, and forensic analysis module configured to securely log system activities, interactions, and threat events with cryptographic integrity protection; and (g) a governance and policy management module configured to define, update, and enforce organizational AI usage and compliance policies via the cloud-based LLM infrastructure.

Description

The present invention relates to cyber security systems, in particular for protecting cloud-based large language models (LLMs). The focus is on containing cyber threats and ensuring compliance with regulations in real time. The invention integrates security, compliance monitoring, and AI clearance in distributed cloud environmentsCloud-based large language models (LLMs) have revolutionized various industries by offering scalable and intelligent solutions to tasks such as natural language processing, data analysis, and decision making. However, their use in cloud environments subjects them to significant cyber threats, including data violations, hostile attacks, and unauthorized access to sensitive model outputs. Existing security measures are often not sufficient to address the dynamic and complex threat landscape that is specific to LLMs in distributed cloud architectures.In addition to cyber threats, companies employing LLMs are experiencing increasing pressure to meet emerging privacy and AI regulations such as GDPR, HIPAA, and emerging AI government frameworks. Monitoring and ensuring compliance across different jurisdictions is a challenge, particularly when cloud infrastructures are distributed worldwide. Conventional compliance monitoring tools are not designed for integration into real-time operation of LLMs, leading to delayed responses to violations and increased regulatory risks.To solve these problems, the present invention proposes a unified system that combines advanced threat detection, automatic response mechanisms, and continuous monitoring of compliance with regulations tailored to LLMs in cloud environments. The system uses AI-controlled anomaly detection, blockchain-based test protocols, and adaptive compliance rules to provide consistent protection and responsibility. This integrated approach not only reduces cybersecurity risks, but also provides for a permanent adaptation to legal regulations and thus enables the safe and legal use of LLMs in companies and in the public sector.The object of the present disclosure is to improve one or more problems of the prior art or at least provide a useful alternative.The object of the present disclosure is to improve cybersecurity for cloud-based LLMs through multi-layer threat defenses.Another object of the present disclosure is to provide compliance with legal requirements by automated real-time monitoring and alerts.Another object of the present disclosure is to protect sensitive data by robust encryption and anonymousization techniques.Another object of the present disclosure is to immediately detect and respond to threats by Kl-controlled behavior analysis.Another object of the present disclosure is to prevent unauthorized access with dynamic, role-based authentication checks.Another object of the present disclosure is to enable tamper-proof forensic logging via blockchain-based log storage.Another object of the present disclosure is to facilitate policy enforcement and ethical government in cloud infrastructures.Another object of the present disclosure is to reduce downtime and risk effects by automated mechanisms to respond to incidents.Other objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.The present invention relates generally to a unitary system for protecting cloud-based large language models (LLMs) from developing cyber threats. It integrates multiple levels of defense, including authentication, encryption, anomaly detection, and response to incidents.In one embodiment of the present invention, a dedicated access control module forces severe user and application authentication with RBAC and MFA. It monitors user behavior and dynamically adjusts permissions based on risk scores in real-time. This prevents unauthorized interactions and protects model integrity.A further embodiment of the invention, sensitive input and output data, is encrypted with robust cryptographic protocols both during transmission and in the idle state. The system also anonymousizes the user data to meet privacy regulations such as GDPR. This ensures confidentiality and prevents the transmission of personal data.Another embodiment of the invention is AI-based behavioral analysis tools that continuously monitor LLM interactions for abnormal patterns or malignant behavior. Prompt injection, API abuse and data infiltration attempts are recognized in real time. This ensures early detection of threats and rapid damage limitation.A further embodiment of the invention consists in that, when a cyber threat or a violation against the regulations is detected, predefined work sequences for responding to an incident are automatically triggered. The actions may include terminating sessions, isolating instances, and notifying the administrator. This minimizes damage and facilitates immediate containment.Another embodiment of the invention is that the system includes a live compliance engine that maps operations to relevant rules such as HIPAA, GDPR, and AI Government laws. It generates real-time warnings, audit records, and legal requirements reports. This supports continuous legal adaptation and simplifies audits.A further embodiment of the invention consists in all system activities, interactions and security events being logged and cryptographically signed to ensure integrity. The blockchain-based storage ensures tamper-proof forensic records. These protocols support findings and provide legal evidence in the event of security violations.Another embodiment of the invention is that a management interface enables real-time updates of AI usage policies, ethical supervisor mechanisms, and compliance rules. The system supports organization-wide policy enforcement across all LLM instances. This ensures ethical, safe and right-handed AI use.The present invention relates to a system for securing cloud-based large language models (LLMs) through multi-layer cyber threat defense and dynamic regulatory compliance monitoring. The system is modular, scalable, and can be deployed in various cloud environments (public, private, or hybrid). It includes six core modules that cooperate synergetically to ensure the security, integrity, and right conformance of LLM operation.Threat Detection and Behavior Monitoring Module:This module uses AI-based anomaly detection methods to monitor the behavior of LLMs and associated user interactions in real-time. It sets baselines for normal activities and continuously evaluates deviations that may indicate threats such as prompt injection, unauthorized API calls, or model extraction attempts. The module integrates threat data and utilizes machine learning models trained on cloud-specific attack patterns to detect both known and new threats.Module Access Control and Authentication:This module regulates secure access to the LLM infrastructure using advanced identity and access management protocols (IAM). Role-based access controls (RBAC), multi-factor authentication (MFA), and dynamic policy enforcement are implemented to ensure that only authorized users and applications can interact with the LLM. The module also monitors session behavior and, in the case of suspect activities, applies automatic recall and reauthentication mechanisms.Data protection and encryption module:This component provides for the encryption of data during transmission and idle using industry standard cryptographic protocols such as AES-256 and TLS 1.3. In addition, sensitive data input to the LLM is anonymousized and tokenized to prevent pricing of personal or protected information. The module implements data minimization and memory restriction policies to meet data protection regulations such as GDPR and CCPA.Module for Monitoring Compliance with Legal Regulations:This module keeps track of and forces regulatory requirements applicable to the regions and sectors in which the LLM is deployed. It has a rule-based compliance engine that matches model usage and data flow with legal constraints such as HIPAA, GDPR, and future Kl-specific laws. Real-time compliance dashboards provide alerts and reports to auditors and administrators. Smart contracts or blockchain-based protocols can be used for tamper-proof verification of compliance.Module for Reaction to Events and Damage Limitation:When a threat is detected or a compliance violation is detected, this module activates preconfigured incident response workflows. It can isolate affected LLM instances, terminate malicious sessions, notify administrators, and initiate auto-restore protocols. The module uses decision trees and AI-based playbooks to recommend and perform optimal response actions with minimal downtime.Module for Logging, Auditing and Forensic Analysis:This module collects and stores comprehensive protocols of all interactions, access attempts, data transmissions, and security events associated with the LLM. The protocols are cryptographically signed and optionally stored on a blockchain ledger to ensure integrity and traceability. In the event of an incident, the forensic tools in this module allow detailed cause analysis and compliance reporting, which facilitates legal defense and post-incident assessment.Module for Government and Policy Management:This last module allows companies to define, enforce, and update government policies for LLM usage. It supports version control of policies, integration with enterprise SOPs, and real-time synchronization with global policy updates. It also provides interfaces for human supervisor and ethical assessment so that the organization can conform Kl utilization to internal values and external social expectation.The invention is explained again below with reference to the figure. The following shows: FIG. 1 is an illustration of a system (100) for securing cloud-based large language models through cyber threat defense and monitoring compliance with legal regulations.FIG. 1 illustrates a system (100) for securing cloud-based big language models by cyber threat defense and monitoring compliance with legal regulations. Operation of the system begins with the user or application interacting with the large language model (LLM) hosted in the cloud, with the access control and authentication module first checking identity and authorization using multi-factor authentication and role-based policies. After authentication, all data entering or exiting the LLM passes through the data protection and encryption module, which encrypts and anonymously encrypts inputs and outputs to protect sensitive information and ensure data minimization. At the same time, the threat detection and behavior monitoring module continuously analyzes usage patterns, model responses, and API behavior using Kl-controlled anomaly detection to detect potential threats such as data infiltration, prompt injection, or abuse of model functions. If irregularities or violations are detected, the Incident Response and Notification Module activates automatic countermeasures such as access locks, session termination and forensic protocol detection and notifies the system administrators. All activities are logged by the logging, auditing and forensic analysis module, which ensures data integrity and supports real-time studies through invariable and cryptographically verified protocols. In parallel, the legal compliance module evaluates ongoing operation based on regional-specific legal and regulatory constraints (e.g., GDPR, HIPAA) and generates real-time warnings and auditable reports in the event of non-compliance. Finally, the government and policy management module allows administrators to dynamically update and apply organizational AI usage policies throughout the system to ensure that ethical and legal standards progress in parallel with the technological opportunities.

Claims

A system (100) for securing cloud-based big language models by cyber threat defense and regulatory compliance monitoring, comprising: (a) an access control and authentication module configured to authenticate users and applications using role-based access control (RBAC) and multi-factor authentication (MFA); (b) a data protection and encryption module configured to encrypt data during transmission and idle and anonymousize sensitive input / output data using cryptographic methods; (c) a threat detection and behavior monitoring module employing machine learning algorithms to identify anomalies and detect potential cyber threats in real time; (d) a legal compliance monitoring module configured to continuously evaluate system operation with respect to applicable legal constraints and generate alerts upon deviations from the regulations; (e) a incident and damage limiting module configured to automatically perform predefined responsive actions upon detection of threats or violations of the regulations; (f) a logging, auditing, and forensic analysis module configured to securely log system activities, interactions, and threat events with cryptographic integrity protection; and (g) a government and policy management module configured to define, update, and enforce organizational AI usage and compliance policies over the cloud-based LLM infrastructure.The system (100) of claim 1, wherein the access control and authentication module dynamically adjusts the session privilege based on a real-time risk assessment.The system (100) of claim 1, wherein the data protection and encryption module uses AES-256 encryption for idle data and TLS 1.3 for secure data transmission.The system (100) of claim 1, wherein the threat detection and behavior monitoring module utilizes a combination of monitored and unsupervised machine learning models to detect prompt injection and unauthorized model access.The system (100) of claim 1, wherein the legal compliance monitoring module supports automatic allocation of data flows and model outputs to policy specific rules such as GDPR and HIPAA.The system (100) of claim 1, wherein the module for responding to an incident and limiting damage includes a decision engine configured to prioritize responsive actions based on severity and potential impacts.The system (100) of claim 1, wherein the logging, review, and forensic analysis module stores protocols on a blockchain ledger to ensure immuncibility and traceability for post-incident reviews.The system (100) of claim 1, wherein the government and policy management module includes an administrative dashboard that enables live policy updates, ethical verification integration, and system-wide policy synchronization.

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