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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,
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