Organizational Learning in Industry 4.0: Applying Crossan's 4I Framework with Double Loop Learning
- URL: http://arxiv.org/abs/2512.21813v1
- Date: Fri, 26 Dec 2025 00:54:01 GMT
- Title: Organizational Learning in Industry 4.0: Applying Crossan's 4I Framework with Double Loop Learning
- Authors: Nimra Akram, Atif Ahmad, Sean B Maynard,
- Abstract summary: The Advanced Dynamic Security Learning (forward) Process Model is an Industry 4.0 cybersecurity incident response architecture proposed in this paper.<n>This model addresses proactive and reflective cybersecurity governance across complex cyber-physical systems by combining Argyris and Schn's double-loop learning theory with Crossan's 4I organizational learning framework.
- Score: 0.09176056742068811
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The Advanced Dynamic Security Learning (DSL) Process Model is an Industry 4.0 cybersecurity incident response architecture proposed in this paper. This model addresses proactive and reflective cybersecurity governance across complex cyber-physical systems by combining Argyris and Schön's double-loop learning theory with Crossan's 4I organizational learning framework. Given that 65% of industrial companies suffer ransomware attacks annually and many of them lack cybersecurity awareness, this reveals the gravity of cyber threats. Feedforward and feedback learning loops in this paradigm help promote strategic transformation and ongoing growth. The DSL model helps Industry 4.0 organizations adapt to growing challenges posed by the projected 18.8 billion IoT devices by bridging operational obstacles and promoting systemic resilience. This research presents a scalable, methodical cybersecurity maturity approach based on a comprehensive analysis of the literature and a qualitative study.
Related papers
- PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities [42.61805002268063]
We introduce PACEbench, a practical AI cyber-exploitation benchmark.<n>PACEbench comprises four scenarios spanning single, blended, chained, and defense vulnerability exploitations.<n>We propose PACEagent, a novel agent that emulates human penetration testers by supporting multi-phase reconnaissance, analysis, and exploitation.
arXiv Detail & Related papers (2025-10-13T17:50:25Z) - Enabling Cyber Security Education through Digital Twins and Generative AI [1.2619493260255112]
Digital Twins (DTs) are gaining prominence in cybersecurity for their ability to replicate complex IT infrastructures.<n>This study investigates how integrating DTs with penetration testing tools and Large Language Models (LLMs) can enhance cybersecurity education.
arXiv Detail & Related papers (2025-07-23T13:55:35Z) - Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report [50.268821168513654]
We present Foundation-Sec-8B, a cybersecurity-focused large language model (LLMs) built on the Llama 3.1 architecture.<n>We evaluate it across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks.<n>By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts.
arXiv Detail & Related papers (2025-04-28T08:41:12Z) - Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey [25.73174314007904]
Traditional cybersecurity approaches are struggling to adapt to the rapidly evolving nature of modern cyberattacks.<n>The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats.<n> exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field.
arXiv Detail & Related papers (2025-04-22T06:28:08Z) - LLM-Assisted Proactive Threat Intelligence for Automated Reasoning [2.0427650128177]
This research presents a novel approach to enhance real-time cybersecurity threat detection and response.<n>We integrate large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems with continuous threat intelligence feeds.
arXiv Detail & Related papers (2025-04-01T05:19:33Z) - Cyber Defense Reinvented: Large Language Models as Threat Intelligence Copilots [36.809323735351825]
CYLENS is a cyber threat intelligence copilot powered by large language models (LLMs)<n>CYLENS is designed to assist security professionals throughout the entire threat management lifecycle.<n>It supports threat attribution, contextualization, detection, correlation, prioritization, and remediation.
arXiv Detail & Related papers (2025-02-28T07:16:09Z) - Countering Autonomous Cyber Threats [40.00865970939829]
Foundation Models present dual-use concerns broadly and within the cyber domain specifically.
Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations.
This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
arXiv Detail & Related papers (2024-10-23T22:46:44Z) - Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories [15.764094200832071]
Cyber resilience focuses on preparation, response, and recovery from cyber threats that are challenging to prevent.
Game theory, control theory, and learning theories are three major pillars for the design of cyber resilience mechanisms.
This chapter presents various theoretical paradigms, including dynamic asymmetric games, moving horizon control, conjectural learning, and meta-learning.
arXiv Detail & Related papers (2024-04-01T16:02:21Z) - Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process [39.677420930301736]
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management.
One of the limitations to the widespread adoption of these technologies is the vulnerability of neural networks to adversarial attacks.
This study explores the threats in deploying deep learning models for fault diagnosis in ACS using the Tennessee Eastman Process dataset.
arXiv Detail & Related papers (2024-03-20T10:59:06Z) - Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models [41.068780235482514]
This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants.
CyberSecEval provides a thorough evaluation of LLMs in two crucial security domains: their propensity to generate insecure code and their level of compliance when asked to assist in cyberattacks.
arXiv Detail & Related papers (2023-12-07T22:07:54Z) - Review: Deep Learning Methods for Cybersecurity and Intrusion Detection
Systems [6.459380657702644]
Artificial Intelligence (AI) and Machine Learning (ML) can be leveraged as key enabling technologies for cyber-defense.
In this paper, we are concerned with the investigation of the various deep learning techniques employed for network intrusion detection.
arXiv Detail & Related papers (2020-12-04T23:09:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.