CloudSec Academy

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.

Learn about the most pressing security risks shared by all AI applications and how to mitigate them.

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.

Shaked Rotlevi

March 24, 2025

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.

Nicolas Ehrman

March 6, 2025

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.

Shaked Rotlevi

February 7, 2025

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.

In this guide, we’ll break down why AI governance has become so crucial for organizations, highlight the key principles and regulations shaping this space, and provide actionable steps for building your own governance framework.

In this post, we’ll bring you up to speed on why the EU put this law in place, what it involves, and what you need to know as an AI developer or vendor, including best practices to simplify compliance.

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.

Shaked Rotlevi

October 17, 2024

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.

Shaked Rotlevi

September 19, 2024

Data leakage is the unchecked exfiltration of organizational data to a third party. It occurs through various means such as misconfigured databases, poorly protected network servers, phishing attacks, or even careless data handling.

Shaked Rotlevi

September 13, 2024

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.

Shaked Rotlevi

September 11, 2024

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.

Shaked Rotlevi

August 5, 2024

LLM jacking is an attack technique that cybercriminals use to manipulate and exploit an enterprise’s cloud-based LLMs (large language models).

Shaked Rotlevi

August 2, 2024

Prompt injection attacks are an AI security threat where an attacker manipulates the input prompt in natural language processing (NLP) systems to influence the system’s output.

Data poisoning is a kind of cyberattack that targets the training data used to build artificial intelligence (AI) and machine learning (ML) models.