ScrapeGraphAI

Deployed ScrapeGraphAI-powered web crawlers for the AI Data Breach Hub that collect breach intelligence from diverse sources across the internet. The system scrapes main web sources (news sites, security advisories), deep web sources (specialized forums and databases), and dark web sources (breach notification channels) to gather comprehensive cybersecurity intelligence totaling 3,100+ reports annually. Implemented strict rate limiting to collect appropriate volumes of data without overwhelming target servers, respecting robots.txt files to maintain ethical scraping practices. The scraping pipeline leverages ScrapeGraphAI's LLM integration to automatically categorize data during extraction - identifying breach types, affected industries, attack vectors, and severity levels - which significantly reduces downstream processing requirements.

Built a scraping architecture that handles diverse content formats including PDFs (security advisories, incident reports), structured web data (breach disclosure databases), and unstructured news articles (cybersecurity media coverage). Implemented intelligent rate limiting that adapts to different source types - slower rates for individual sites to avoid bot detection, faster rates for API-based sources that support bulk access. The system respects robots.txt directives and implements backoff strategies when encountering rate limits or access restrictions, ensuring legally-compliant data collection. All scraped data goes through automatic PII filtering to maintain the platform's zero-PII policy, extracting only breach metadata and intelligence without capturing personal information.