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Introduction Large Language Models (LLMs) have become part of daily life. People use AI assistants to write emails, generate code, summarize documents, conduct research, and answer questions. As these systems become more common in homes, businesses, and government environments, protecting the privacy of user interactions has become increasingly important. Many users assume that encryption completely hides their AI conversations from outside observers. While encryption protect

Introduction A company may discover 20,000 vulnerabilities during a scan, but fixing all 20,000 immediately is impossible. Vulnerability Management exists to solve this problem. Every day, organizations are exposed to new security weaknesses. Operating systems, applications, cloud services, network devices, containers, and third-party software all introduce vulnerabilities that attackers may attempt to exploit. As environments grow larger and more complex, the number of vulne

For years, vulnerability management has revolved around a familiar ritual: scan, score, sort, patch. At the center of that ritual sat CVSS, a single numerical expression of danger that promised clarity in a chaotic threat landscape. Over time, that promise hardened into dependency. CVSS scores became policy triggers, SLA benchmarks, and executive talking points. Yet as systems grew more interconnected and attacks more adaptive, the gap between what CVSS measured and what defe