Year: 2025
Type: article
Abstract: The Website Domain & Compliance Analyzer is a Python-based command-line tool designed to assess websites and domain names across various compliance, technical, and informational parameters.It collects information such as WHOIS ownership details, DNS records, domain pricing (in USD and INR), and contact details using libraries like requests, BeautifulSoup, and tldextract.The tool also checks for key legal and policy content like privacy policies, cookie notices, accessibility statements, and data protection practices.It performs basic security assessments by detecting mentions of SSL, HTTPS, and encryption, providing a percentage breakdown of strong versus weak connection indicators found in the site's content.Beyond technical assessments, the analyzer extracts business descriptions, identifies social media links, and summarizes policy references to provide a comprehensive overview of a domain's public presence and legal transparency.It supports domains across multiple TLDs and estimates pricing in both USD and INR.The tool features a visually engaging interface powered by the Rich library, displaying results in well-structured tables and panels for easy interpretation.Whether for cybersecurity reviews, compliance audits, educational purposes, or SEO evaluations, this tool serves as a practical solution for assessing the trustworthiness and completeness of a website from both technical and policy perspectives.The tool's modular architecture allows for future enhancements, such as integrating machine learning models to predict domain trustworthiness or incorporating APIs for real-time threat intelligence.Its open-source nature encourages community contributions, fostering continuous improvement and adaptation to emerging web standards and security protocols.In summary, the Website Domain & Compliance Analyzer stands as a comprehensive solution for individuals and organizations aiming to evaluate and ensure the integrity, security, and compliance of web domains in an increasingly complex digital landscape.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411455173