클라우드 보안 약어와 업계 전문 용어의 알파벳 수프를 탐색하는 데 도움이 되는 CloudSec Academy에 오신 것을 환영합니다. 기본 사항부터 모범 사례까지 다루는 명확하고 간결하며 전문적으로 제작된 콘텐츠로 소음을 차단하세요.
AI is transforming cloud security operations by enabling real-time threat detection, automated response, and predictive risk analysis, helping teams stay ahead of attackers.
In this article, we’ll discuss the benefits of AI-powered SecOps, explore its game-changing impact across various SOC tiers, and look at emerging trends reshaping the cybersecurity landscape.
There are many sneaky AI security risks that could impact your organization. Learn practical steps to protect your systems and data while still leveraging AI's benefits.
Traditional security testing isn’t enough to deal with AI's expanded and complex attack surface. That’s why AI red teaming—a practice that actively simulates adversarial attacks in real-world conditions—is emerging as a critical component in modern AI security strategies and a key contributor to the AI cybersecurity market growth.
AWS offers a complete, scalable suite for AI that covers everything from data prep to model deployment, making it easier for developers to innovate quickly.
AI-assisted software development integrates machine learning and AI-powered tools into your coding workflow to help you build, test, and deploy software without wasting resources.
Generative AI (GenAI) security is an area of enterprise cybersecurity that zeroes in on the risks and threats posed by GenAI applications. To reduce your GenAI attack surface, you need a mix of technical controls, policies, teams, and AI security tools.
Our goal with this article is to share the best practices for running complex AI tasks on Kubernetes. We'll talk about scaling, scheduling, security, resource management, and other elements that matter to seasoned platform engineers and folks just stepping into machine learning in Kubernetes.
The AI Bill of Rights is a framework for developing and using artificial intelligence (AI) technologies in a way that puts people's basic civil rights first.
AI-SPM (AI security posture management) is a new and critical component of enterprise cybersecurity that secures AI models, pipelines, data, and services.
Artificial intelligence (AI) compliance describes the adherence to legal, ethical, and operational standards in AI system design and deployment.
An AI bill of materials (AI-BOM) is a complete inventory of all the assets in your organization’s AI ecosystem. It documents datasets, models, software, hardware, and dependencies across the entire lifecycle of AI systems—from initial development to deployment and monitoring.
The NIST AI Risk Management Framework (AI RMF) is a guide designed to help organizations manage AI risks at every stage of the AI lifecycle—from development to deployment and even decommissioning.
Shadow AI is the unauthorized use or implementation of AI that is not controlled by, or visible to, an organization’s IT department.
이 가이드에서는 AI 거버넌스가 조직에 매우 중요해진 이유를 분석하고, 이 공간을 형성하는 주요 원칙과 규정을 강조하고, 자체 거버넌스 프레임워크를 구축하기 위한 실행 가능한 단계를 제공합니다.
이 게시물에서는 EU가 이 법을 제정한 이유, 이 법의 내용, 규정 준수를 간소화하기 위한 모범 사례를 포함하여 AI 개발자 또는 공급업체로서 알아야 할 사항에 대해 자세히 설명합니다.
The short answer is no, AI is not expected to replace cybersecurity or take cybersecurity jobs. It will, however, augment cybersecurity with new tools, methods, and frameworks.
AI data security is a specialized practice at the intersection of data protection and AI security that’s aimed at safeguarding data used in AI and machine learning (ML) systems.
LLM models, like GPT and other foundation models, come with significant risks if not properly secured. From prompt injection attacks to training data poisoning, the potential vulnerabilities are manifold and far-reaching.
데이터 유출은 조직 데이터를 제3자에게 무단으로 반출하는 것입니다. 잘못 구성된 데이터베이스, 제대로 보호되지 않은 네트워크 서버, 피싱 공격 또는 부주의한 데이터 처리와 같은 다양한 수단을 통해 발생합니다.
ChatGPT security is the process of protecting an organization from the compliance, brand image, customer experience, and general safety risks that ChatGPT introduces into applications.
AI risk management is a set of tools and practices for assessing and securing artificial intelligence environments. Because of the non-deterministic, fast-evolving, and deep-tech nature of AI, effective AI risk management and SecOps requires more than just reactive measures.
Adversarial artificial intelligence (AI), or adversarial machine learning (ML), is a type of cyberattack where threat actors corrupt AI systems to manipulate their outputs and functionality.
LLM jacking is an attack technique that cybercriminals use to manipulate and exploit an enterprise’s cloud-based LLMs (large language models).
프롬프트 주입 공격은 공격자가 자연어 처리(NLP) 시스템에서 입력 프롬프트를 조작하여 시스템의 출력에 영향을 미치는 AI 보안 위협입니다.
