CloudSec Academy へようこそ。クラウドセキュリティの頭字語と業界用語のアルファベットスープをナビゲートするためのガイドです。 明確で簡潔、かつ専門的に作成されたコンテンツで、基本的なことからベストプラクティスまでをカバーします。
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.
このガイドでは、AIガバナンスが組織にとって非常に重要になった理由を解説し、この分野を形作る主要な原則と規制に焦点を当て、独自のガバナンスフレームワークを構築するための実行可能な手順を提供します。
この投稿では、EUがこの法律を施行した理由、その内容、AI開発者またはベンダーとして知っておくべきこと(コンプライアンスを簡素化するためのベストプラクティスなど)についてお伝えします。
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.
データ漏洩とは、組織データが第三者に対して野放しに持ち出されることです。 これは、データベースの設定ミス、ネットワークサーバーの保護が不十分な、フィッシング攻撃、さらには不注意なデータ処理など、さまざまな手段で発生します。
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).
プロンプトインジェクション攻撃は、攻撃者が自然言語処理(NLP)システムの入力プロンプトを操作してシステムの出力に影響を与えるAIセキュリティの脅威です。
