The study explores the application of new technologies, particularly Natural Language Processing (NLP), to automate policy creation and enforcement in cybersecurity. Various researchers have investigated using NLP to translate written policies into formal access control frameworks and automate compliance verification processes, with advances in large language models (LLMs) expanding these capabilities. However, challenges remain, such as interpreting ambiguous language and integrating policies into existing systems. Additionally, human factors in cybersecurity, including user awareness and behavior, remain critical areas of focus. Studies have shown that while security awareness training can raise knowledge, long-term behavioral changes require personalized, ongoing interventions. Research on network intrusion detection and phishing detection using artificial neural networks (ANN) and machine learning has demonstrated promising results, though evolving threats continue to challenge the effectiveness of these models. Overall, a comprehensive, multi-faceted approach combining technology and human-centered strategies is necessary to address the complexities of modern cybersecurity.

Current cybersecurity measures focus heavily on technological solutions but often overlook the human element, which remains the weakest link. While many organizations invest in advanced cyber defenses, they fail to address security hygiene, user awareness, and behavior analytics adequately. There is also a lack of comprehensive frameworks that integrate AI-based intrusion detection, NLP-driven policy enforcement, and behavior profiling to bridge this gap between human vulnerabilities and cybersecurity solutions.

The research addresses the critical issue of user-related vulnerabilities in corporate environments, which are exploited through phishing, poor security hygiene, and browser-based attacks. Specifically, the problem revolves around how organizations struggle to maintain security due to:

• Limited user awareness and poor online behavior hygiene.
• Inefficient enforcement of browser-based security policies.
• Ineffective real-time phishing detection and user profiling mechanisms.
• Depending on traditional Network Intrusion Detection Systems.

The study aims to find a solution that connects human behavior with advanced cybersecurity mechanisms, minimizing the risks posed by human error.

The primary objective is to develop a centralized framework that monitors, evaluates, and improves employees’ online presence within a corporate environment. Specific objectives include:

1. Implementing behavior analytics for profiling user activities.
2. Automating the generation and enforcement of browser security policies using NLP.
3. Designing a real-time intrusion detection system powered by Artificial Neural Networks (ANN).
4. Improving phishing detection through URL and visual analysis to identify high-risk activities.
5. Promoting better security hygiene by bridging the gap between user awareness and the evolving threat landscape.

The methodology includes the following components
1. User Behavior Profiling:
• Identify and analyze risk factors affecting browser hygiene using Principal Component Analysis (PCA) and Bayesian Network Analysis.
• Create a Browser Hygiene Risk Assessment Model to quantify risks based on various factors like outdated browsers, malicious extensions, and unsafe protocols.

2. Intrusion Detection and Prevention System:
• Develop an ANN-based solution trained on datasets like NSL-KDD and updated with real-time threat intelligence.
• Employ a binary classification model that flags malicious traffic on each host system for faster detection and prevention.

3. NLP-Assisted Policy Generation and Enforcement:
• Use BERT models for classifying and extracting policy intents from natural language inputs.
• Implement scripts for applying browser security policies across multiple Chromium-based browsers.

4. Phishing Detection Mechanism:
• Use a browser plugin to compare website visuals and URLs with a predefined safe list.
• Redirect users to safety when phishing attempts are detected, adding malicious domains to a blacklist.