CompTIA SecurityX Blog Series
Core Exam Objectives 1: Governance, Risk, and Compliance

Organizational changes, such as mergers, acquisitions, divestitures, and staffing transitions, can significantly impact a company’s attack surface. During these transitions, new assets, personnel, and systems

A comprehensive approach to threat modeling begins with attack surface determination—analyzing and understanding every point where potential attackers could interact with an organization’s systems. This

Attack trees and graphs are structured methods used in threat modeling to visualize potential attack paths and assess system vulnerabilities. By breaking down attacks into

In threat modeling, antipatterns refer to common design or implementation choices that appear beneficial but, in practice, lead to unintended vulnerabilities or inefficiencies. Recognizing these

In threat modeling, abuse cases are a critical tool for identifying how an application, system, or process could be misused by malicious actors. Unlike typical

In threat modeling, one of the most critical steps for a security professional is assessing how identified threats apply specifically to the organization’s systems and

The rapid adoption of AI technology brings not only numerous benefits but also significant risks of misuse. Potential misuse of AI—from privacy violations to malicious

The adoption of AI in sensitive areas like finance, healthcare, and law enforcement requires careful consideration of model transparency and accountability. Explainable models are those

The widespread adoption of artificial intelligence (AI) in organizational environments introduces unique security and privacy challenges. Organizational policies on the use of AI play a

As artificial intelligence (AI) adoption accelerates, establishing frameworks for ethical governance is crucial to address unique information security challenges. Ethical governance in AI involves ensuring

As AI models, particularly natural language processing (NLP) and large language models (LLMs), become more sophisticated, they are increasingly used in applications that rely on

In AI systems, insecure output handling refers to vulnerabilities in how a model’s predictions or outputs are managed, shared, and protected. If not handled securely,