1. A system for context-aware cybersecurity incident response and data recovery, comprising: • a Neural Context Engine (NCE) configured to generate contextual embeddings from system logs, alerts, and topology data; • an Adaptive Decision Layer (ADL) employing reinforcement learning to dynamically select recovery pathways based on incident type, severity, and operational dependencies; • an Integrity Verification Module (IVM) integrating cryptographic validation and anomaly detection to prevent reinjection of compromised data; • a Restoration and Orchestration Module (ROM) configured to prioritize recovery of mission-critical services and orchestrate restoration across local, cloud, and distributed backups; and • a Self-Learning Feedback Loop (SLFL) that continuously updates recovery strategies based on execution outcomes to optimize Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs).

2. The system as claimed in claim 1, wherein the Neural Context Engine generates embeddings that capture attack vectors, system state, and dependency relationships to guide recovery decisions.

3. The system as claimed in claim 1, wherein the Adaptive Decision Layer applies reinforcement learning policies to select recovery sources, granularity, and order of restoration.

4. The system as claimed in claim 1, wherein the Integrity Verification Module performs multi-gate validation combining cryptographic hash checks with AI-driven anomaly detection.

5. The system as claimed in claim 1, wherein the Restoration and Orchestration Module prioritizes mission-critical services for immediate recovery while deferring non-essential data.

6. The system as claimed in claim 1, wherein the Self-Learning Feedback Loop improves recovery accuracy and efficiency by updating neural models based on past incident outcomes.

7. A method for context-aware cybersecurity incident response and data recovery, comprising: • detecting a cyber incident and encoding contextual information using a neural context engine; • planning adaptive recovery pathways through reinforcement learning; • verifying candidate backups using cryptographic and anomaly detection techniques; • restoring prioritized assets across local, cloud, and distributed sources; and • updating recovery strategies through a self-learning feedback mechanism.

8. The method as claimed in claim 7, wherein recovery prioritization ensures that mission-critical services are restored first to minimize downtime.

9. The method as claimed in claim 7, wherein adaptive recovery pathways dynamically adjust based on incident severity, system dependencies, and available resources.

10. The method as claimed in claim 7, wherein the self-learning feedback mechanism continuously reduces recovery time and enhances resilience against future cyber incidents.

Description:FIELD OF THE INVENTION
The invention relates to a Context-Aware Neural Recovery Network (CNRN) that integrates cybersecurity incident response with intelligent, integrity-aware data recovery. Traditional recovery systems are static, rule-based, and unable to adapt to dynamic cyber threats such as ransomware, insider breaches, and advanced persistent threats (APTs).
BACKGROUND OF THE INVENTION
Current incident response and recovery mechanisms rely heavily on static rules and preconfigured workflows. Such rigidity prevents them from adapting to the wide variety of modern attacks, including ransomware, insider threats, and advanced persistent threats (APTs). Backup systems often perform restorations without validating the trustworthiness of data, which increases the risk of reintroducing corrupted or malicious content. Furthermore, recovery tools generally lack mechanisms to prioritize mission-critical services, treating all data equally, which results in prolonged downtime and service disruption. These shortcomings highlight the absence of a flexible, intelligence-driven framework that can respond to dynamic threats while ensuring secure, efficient recovery.
Current commercial solutions typically separate cybersecurity incident handling and data recovery, resulting in fragmented workflows and slower response. Security platforms focus on threat containment, while recovery tools attempt to restore data without factoring in the incident’s context or severity. In practice, this leads to inefficient recovery processes, incomplete integrity checks, and unnecessary delays.
There is a clear need for an integrated approach that combines context-aware decision-making with intelligent recovery orchestration. Such an innovation should be capable of dynamically adjusting to the nature of the threat, verifying the reliability of data before restoration, and prioritizing the recovery of the most essential systems.
The increasing frequency and sophistication of cyberattacks, such as ransomware, advanced persistent threats (APTs), and insider data breaches, have exposed the limitations of traditional cybersecurity and data recovery mechanisms. Current solutions often operate in silos—with incident response systems focused on threat containment and recovery systems limited to restoring data without context-awareness. This disconnect leads to delayed recovery, compromised data integrity, and prolonged downtime, especially in critical infrastructures like healthcare, finance, and cloud services.
Moreover, existing data recovery systems are largely static and rule-based, lacking the ability to adapt dynamically to the evolving nature of cyber incidents. They typically restore entire datasets without prioritization, wasting time and resources, and in some cases, reintroducing malicious or corrupted data back into the system.
The need for innovation lies in bridging this critical gap by creating an integrated, intelligent, and adaptive framework that not only responds to cyber incidents but also ensures resilient, context-aware data recovery. By leveraging neural networks, machine learning, and self-learning mechanisms, the proposed system provides:
1. Context-driven decision-making, adapting recovery strategies to specific threats.
2. Intelligent data prioritization, ensuring critical services are restored first.
3. Data integrity validation, preventing compromised data reinjection.
4. Self-healing and continuous optimization, reducing recovery time (RTO) and improving recovery accuracy over time.
Such innovation is essential for modern organizations where downtime, data corruption, and incomplete recovery can lead to severe financial losses, reputational damage, and operational disruption.
OBJECTIVES OF THE INVENTION
The primary objective of this invention is to develop a Context-Aware Neural Recovery Network (CNRN) that integrates cybersecurity incident response with intelligent data recovery to enhance resilience against cyberattacks and system failures.
Specific objectives include:
1. To design a context-aware neural framework that captures and analyzes system state, attack vectors, and recovery requirements in real time.
2. To enable adaptive incident response, allowing the system to dynamically select recovery pathways based on the type, severity, and context of the cyber incident.
3. To ensure resilient and prioritized data recovery, restoring mission-critical services first while deferring non-critical data, thereby reducing downtime.
4. To integrate data integrity verification using cryptographic or AI-driven anomaly detection techniques, preventing reinjection of corrupted or compromised data.
5. To implement self-learning mechanisms through reinforcement learning and feedback loops, enabling continuous optimization of Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs).
6. To provide multi-purpose recovery support, intelligently switching between local, cloud, and distributed backups depending on trust, availability, and recovery context.
7. To enhance organizational resilience by unifying cybersecurity incident response and automated data restoration in a single adaptive framework.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The proposed Context-Aware Neural Recovery Network addresses critical cybersecurity gaps across industries by providing an integrated AI system that unifies cybersecurity incident response with intelligent, integrity-aware data recovery.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, 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.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed Context-Aware Neural Recovery Network addresses critical cybersecurity gaps across industries by providing an integrated AI system that unifies cybersecurity incident response with intelligent, integrity-aware data recovery.
Neural Context Engine (NCE): creates context embeddings from logs, topology and alerts.
Adaptive Decision Layer (ADL): RL-driven planner that selects recovery paths (source, granularity, order).
Integrity Verification Module (IVM): cryptographic + ML checks to block compromised backups.
Restoration & Orchestration Module (ROM): executes prioritized restores across local/cloud/distributed backups.
Self-Learning Feedback Loop (SLFL): updates models from outcomes to improve RTO/RPO over time.
Proposed Workflow
Detect incident → encode context → plan adaptive recovery → verify candidate backups → restore prioritized assets → learn from outcome.
The proposed Context-Aware Neural Recovery Network (CNRN) differs from existing solutions by introducing a neural context engine that learns embeddings from system state, attack signatures, and operational dependencies. This context guides a reinforcement learning–based decision layer, which dynamically selects recovery strategies tailored to each incident. Unlike existing recovery products, CNRN integrates a multi-gate integrity verification module that combines cryptographic checks with anomaly detection before restoration.
In addition, CNRN employs a prioritization mechanism to ensure critical services are restored first, while non-essential data can be scheduled later. The architecture also supports multi-source adaptive recovery, enabling seamless switching between local, cloud, and distributed backups. Finally, a self-learning feedback loop ensures that recovery performance improves continuously, reducing downtime and enhancing resilience with each incident handled.
Combines contextual embeddings + RL planning + integrity-first verification + multi-source prioritized orchestration in one self-learning framework — unlike current siloed, rule-based products.
ADVANTAGES OF THE INVENTION
Reduced Downtime
 By adaptively prioritizing critical systems and optimizing recovery workflows, CNRN significantly lowers system downtime during cyber incidents.
Enhanced Resilience
 Integration of cybersecurity response with data recovery ensures organizations can withstand and recover from attacks with minimal disruption.
Improved Data Integrity
 Integrity checks prevent the reinjection of compromised data, ensuring secure restoration.
Continuous Improvement
 Self-learning capabilities enable the system to become more effective with each incident handled.
Cost and Resource Efficiency
 By avoiding unnecessary full-scale recoveries and focusing only on what is needed, organizations save time, storage, and operational resources.
Scalability and Flexibility
 Applicable across enterprises, cloud infrastructures, critical infrastructures, and government systems, the framework adapts seamlessly to different environments.
Key benefits:
Reduced downtime, lower reinfection risk, prioritized recovery of critical services, and continuous improvement of recovery strategies.
CNRN distinguishes itself by combining:
 learned contextual embeddings (not simple rule matching)
 RL-based adaptive recovery planning,
 integrity-first restore verification,
 prioritized multi-source orchestration, and
 a continuous self-learning feedback loop
These elements together form the inventive step that bridges cybersecurity incident response and resilient data recovery in an integrated, adaptive system.
, Claims:1. A system for context-aware cybersecurity incident response and data recovery, comprising:
• a Neural Context Engine (NCE) configured to generate contextual embeddings from system logs, alerts, and topology data;
• an Adaptive Decision Layer (ADL) employing reinforcement learning to dynamically select recovery pathways based on incident type, severity, and operational dependencies;
• an Integrity Verification Module (IVM) integrating cryptographic validation and anomaly detection to prevent reinjection of compromised data;
• a Restoration and Orchestration Module (ROM) configured to prioritize recovery of mission-critical services and orchestrate restoration across local, cloud, and distributed backups; and
• a Self-Learning Feedback Loop (SLFL) that continuously updates recovery strategies based on execution outcomes to optimize Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs).
2. The system as claimed in claim 1, wherein the Neural Context Engine generates embeddings that capture attack vectors, system state, and dependency relationships to guide recovery decisions.
3. The system as claimed in claim 1, wherein the Adaptive Decision Layer applies reinforcement learning policies to select recovery sources, granularity, and order of restoration.
4. The system as claimed in claim 1, wherein the Integrity Verification Module performs multi-gate validation combining cryptographic hash checks with AI-driven anomaly detection.
5. The system as claimed in claim 1, wherein the Restoration and Orchestration Module prioritizes mission-critical services for immediate recovery while deferring non-essential data.
6. The system as claimed in claim 1, wherein the Self-Learning Feedback Loop improves recovery accuracy and efficiency by updating neural models based on past incident outcomes.
7. A method for context-aware cybersecurity incident response and data recovery, comprising:
• detecting a cyber incident and encoding contextual information using a neural context engine;
• planning adaptive recovery pathways through reinforcement learning;
• verifying candidate backups using cryptographic and anomaly detection techniques;
• restoring prioritized assets across local, cloud, and distributed sources; and
• updating recovery strategies through a self-learning feedback mechanism.
8. The method as claimed in claim 7, wherein recovery prioritization ensures that mission-critical services are restored first to minimize downtime.
9. The method as claimed in claim 7, wherein adaptive recovery pathways dynamically adjust based on incident severity, system dependencies, and available resources.
10. The method as claimed in claim 7, wherein the self-learning feedback mechanism continuously reduces recovery time and enhances resilience against future cyber incidents.