Description:The present invention relates to the field of cybersecurity, more particularly to intelligent systems for digital threat management. Specifically, it pertains to an AI-driven cybersecurity framework designed to autonomously detect, classify, and mitigate cybersecurity threats using advanced machine learning algorithms and real-time behavioral analytics, thereby enhancing the protection of digital infrastructure across various domains such as enterprise networks, cloud environments, and IoT ecosystems.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

With the rapid digitalization of businesses and critical infrastructures, cyber threats have become increasingly sophisticated, frequent, and damaging. Traditional security systems primarily depend on signature-based detection methods and rule-based engines that require constant human oversight and manual updates. These methods often fall short in identifying zero-day vulnerabilities, polymorphic malware, and advanced persistent threats (APTs), making modern systems vulnerable to exploitation.

The increasing complexity of cyberattacks demands real-time detection capabilities and rapid responses, which conventional systems are ill-equipped to provide. The inability to detect anomalous behavior in time or to respond proactively to emerging threats results in significant data breaches, financial losses, and reputational damage for organizations.

Moreover, modern digital environments such as cloud computing platforms, smart devices, and interconnected enterprise networks generate enormous volumes of data every second. Extracting meaningful patterns from such high-dimensional, high-velocity data for threat detection is a daunting task that conventional tools are incapable of performing efficiently or accurately.

Manual incident response is both time-consuming and prone to errors. Cybersecurity teams are overwhelmed by the sheer volume of alerts, many of which are false positives. This alert fatigue leads to critical threats being overlooked or not addressed in a timely manner, further exposing systems to attacks.

Additionally, cyber adversaries continuously evolve their tactics to bypass static security rules. Without the ability to learn from past events and adapt to new threat vectors, conventional systems remain reactive rather than proactive in their defense strategies.

There is thus a pressing need for an intelligent, adaptive, and automated cybersecurity framework that not only detects threats with high accuracy but also mitigates them in real-time with minimal human intervention. This invention aims to address these gaps through the integration of AI-based technologies.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

An objective of the present invention is to develop an AI-driven cybersecurity framework that enables real-time detection and mitigation of digital threats using a combination of supervised and unsupervised learning algorithms.

Another objective is to create a fully automated system that can operate autonomously with minimal human intervention, thereby reducing response time to cyberattacks and minimizing the need for constant manual monitoring.

A further objective is to leverage behavioral analytics and threat intelligence to improve detection accuracy by analyzing user and system behavior across multiple dimensions, identifying anomalies that deviate from established baselines.

An additional goal is to provide a scalable and modular system architecture capable of being deployed across diverse environments such as cloud infrastructures, enterprise networks, and edge computing systems, ensuring consistent protection across all layers of the digital ecosystem.

Another important objective is to incorporate dynamic learning mechanisms that allow the system to evolve continuously by integrating feedback from past incidents, thereby enhancing its capability to respond to emerging and unknown threats.

It is also an objective to reduce false positives and improve alert prioritization through a risk-based assessment module that assigns severity scores to detected anomalies based on contextual analysis and historical data.

Finally, the invention aims to offer an intuitive user interface and a configurable dashboard that allows cybersecurity administrators to monitor threat status in real-time, customize response policies, and override automated actions when necessary.

SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

The present invention provides an AI-powered cybersecurity framework that autonomously detects and mitigates cybersecurity threats through continuous monitoring, data analysis, and machine learning. The framework comprises key functional modules including data collection, preprocessing, an AI engine, a threat assessment module, and an automated mitigation engine. These components work collaboratively to provide a real-time, adaptive, and proactive defense system for modern digital infrastructures.

By employing a hybrid AI approach—leveraging both supervised and unsupervised learning techniques—the system is capable of identifying known threats as well as uncovering new, previously unseen attack vectors. The framework not only detects threats but also initiates timely countermeasures such as IP blocking, endpoint isolation, and alerting relevant stakeholders, thereby enhancing the overall cyber resilience of the protected environment.

BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary AI-driven cybersecurity framework for automated threat detection and mitigation, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means 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 appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

The present invention provides an intelligent, AI-driven cybersecurity framework designed to detect and mitigate cyber threats in real-time with minimal human intervention. The framework leverages artificial intelligence and machine learning algorithms to analyze behavioral patterns, detect anomalies, classify threats, and execute automated responses across complex and dynamic digital ecosystems. The system is designed to handle data from multiple sources, including network traffic, endpoint devices, user activity logs, and external threat intelligence feeds.

At the core of the framework lies the AI Engine, which incorporates a combination of unsupervised learning (e.g., clustering, autoencoders) and supervised classification models (e.g., neural networks, support vector machines) to continuously learn from incoming data. The models are trained to recognize both known threat signatures and abnormal patterns that may indicate zero-day vulnerabilities or previously unseen attacks. The engine dynamically adapts its decision-making process using a feedback loop informed by threat response effectiveness and updated intelligence.

The Data Collection Module is responsible for aggregating structured and unstructured data from various digital endpoints. It interfaces with operating systems, firewalls, cloud applications, and IoT devices. This module supports encrypted data transmission and ensures integrity during data ingestion. A Preprocessing Unit filters redundant or irrelevant information and transforms raw data into feature-rich representations suitable for machine learning analysis. This includes time-series normalization, tokenization, and context enrichment.

Once a threat is identified by the AI engine, the Threat Assessment Module calculates a contextual risk score based on factors such as severity, attack pattern, asset value, and historical behavior. This score determines the priority of the response and may trigger either a full-scale mitigation protocol or a notification for manual review. The Automated Mitigation Module acts based on this decision, employing response strategies such as isolating network nodes, blocking IPs, terminating suspicious processes, or invoking sandbox analysis.

The framework is designed with modularity in mind, allowing seamless integration with existing security infrastructure such as SIEM platforms, firewalls, and endpoint protection tools. A User Dashboard provides real-time insights, visual analytics, threat maps, and control over system parameters. Administrators can customize thresholds, set policy rules, and override AI-driven responses if necessary.

The system continuously evolves by learning from past attack scenarios and administrator feedback. This self-improvement mechanism ensures that the detection models remain up-to-date and effective against evolving cyber threats. The combination of proactive detection, contextual analysis, and automated mitigation results in a robust and scalable cybersecurity solution.

In one embodiment, the AI-driven cybersecurity framework is deployed within a large enterprise network comprising employee workstations, cloud-based applications, on-premise servers, and mobile devices. The Data Collection Module interfaces with local agents installed on endpoints and with the organization's central log repository to collect real-time data on application usage, login behavior, file access patterns, and network connections.

The AI Engine detects a sudden surge in outbound traffic from a finance department computer and flags it as anomalous behavior. Using unsupervised clustering, the system determines that the activity does not match regular usage patterns. The supervised classification model confirms the presence of known malware signatures within the data payload.

Based on the calculated risk score, the Threat Assessment Module categorizes the incident as “critical,” and the Automated Mitigation Module responds immediately by quarantining the affected machine, blocking external communication, and notifying the IT security team through the dashboard. The incident is also logged for compliance and auditing purposes.

In another embodiment, the framework is applied to secure a hybrid cloud infrastructure hosting microservices and containerized applications. The system is integrated with cloud monitoring APIs and collects telemetry from container logs, API gateway requests, and virtual network interfaces. The Preprocessing Unit translates the telemetry into machine-readable formats and enriches them with context tags such as geolocation, resource identity, and usage history.

The AI Engine detects an abnormal pattern of repeated failed login attempts across multiple containers followed by successful access from an unknown IP range. The anomaly detection mechanism flags this as a possible brute-force attack followed by lateral movement.

The Mitigation Module automatically disables the compromised credentials, blocks the attacker’s IP range, and rolls back affected containers to a previous secure state using orchestration commands. The security team is alerted with a detailed forensic report and a recommended policy update to strengthen access control rules.

In a third embodiment, the invention is employed within a smart grid infrastructure involving interconnected IoT devices, smart meters, and SCADA systems. Data collected from edge devices includes voltage fluctuations, device firmware updates, communication logs, and control signal exchanges. The AI Engine is trained to identify unusual command sequences that might indicate spoofing or remote hijacking attempts.

An anomaly is detected when a group of smart meters begins sending repetitive control requests to a substation. The system identifies this as a potential distributed denial-of-service (DDoS) attack originating from compromised IoT devices. The Threat Assessment Module recognizes the threat as high risk due to its potential impact on grid stability.

The framework responds by isolating the communication channel of the malicious devices, alerting the central monitoring center, and initiating a firmware patch rollout to prevent future exploitations. Additionally, a policy is auto-generated to restrict similar traffic patterns and is pushed across all IoT nodes through the orchestration layer.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.
, Claims:1. An AI-driven cybersecurity framework for automated threat detection and mitigation, comprising:
• a data collection module configured to receive and aggregate data from a plurality of digital sources;
• a preprocessing module adapted to normalize and structure said data;
• an artificial intelligence engine comprising one or more machine learning models configured to detect anomalies and classify potential security threats;
• a threat assessment module adapted to score and prioritize detected threats based on predefined or learned parameters; and
• an automated mitigation module configured to initiate predefined threat response actions based on the threat classification,
wherein the framework operates in real-time and continuously adapts the detection models using a feedback loop based on past threat responses.

2. The framework of claim 1, wherein the artificial intelligence engine comprises both supervised and unsupervised learning models including neural networks, clustering algorithms, and decision trees.
3. The framework of claim 1, wherein the automated mitigation module performs actions selected from the group consisting of isolating affected systems, blocking IP addresses, disabling network ports, and alerting system administrators.
4. The framework of claim 1, further comprising a threat intelligence integration module configured to incorporate external threat feeds into the AI engine’s learning process.
5. The framework of claim 1, wherein the AI engine utilizes a feedback loop to update model weights and threat detection thresholds based on post-incident analysis.
6. The framework of claim 1, further comprising a user interface configured to display real-time threat analytics and allow manual override of automated mitigation actions.
7. The framework of claim 1, wherein the data collection module supports encrypted data streams and applies secure data transmission protocols.
8. The framework of claim 1, wherein the threat assessment module includes a dynamic risk scoring algorithm that accounts for historical threat data and contextual network behavior.