Bienvenido a CloudSec Academy, tu guía para navegar por la sopa de alfabeto de los acrónimos de seguridad en la nube y la jerga de la industria. Cortar el ruido con contenido claro, conciso y elaborado por expertos que cubra los fundamentos de las mejores prácticas.
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.
En esta guía, analizaremos por qué la gobernanza de la IA se ha vuelto tan crucial para las organizaciones, destacaremos los principios y regulaciones clave que dan forma a este espacio y proporcionaremos pasos prácticos para crear su propio marco de gobernanza.
En esta publicación, te pondremos al día sobre por qué la UE implementó esta ley, qué implica y qué necesitas saber como desarrollador o proveedor de IA, incluidas las mejores prácticas para simplificar el cumplimiento.
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.
La fuga de datos es la exfiltración incontrolada de datos de la organización a un tercero. Se produce a través de varios medios, como bases de datos mal configuradas, servidores de red mal protegidos, ataques de phishing o incluso un manejo de datos descuidado.
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).
Los ataques de inyección de avisos son una amenaza para la seguridad de la IA en la que un atacante manipula el mensaje de entrada en los sistemas de procesamiento de lenguaje natural (NLP) para influir en la salida del sistema.
