Learn how to secure AI models and the cloud systems that support them. These articles explore emerging risks, evolving attack techniques, and the safeguards teams use to protect models, pipelines, and inference workflows — while also showing how AI can boost core security operations.
Understand how Wiz protects AI models, data, and workflows across your cloud environment.
The OWASP Top 10 for LLM Applications is a specialized security framework that catalogs the ten most critical vulnerabilities impacting large language models within enterprise environments.
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.
AI agent security is the practice of keeping autonomous AI systems safe, predictable, and controlled when they take actions on real systems.
Regardez comment Wiz transforme la visibilité instantanée en une remédiation rapide.
MCP acts as a universal security control plane that standardizes policy enforcement across enterprise AI workflows.
AI agent sprawl is the uncontrolled multiplication of autonomous AI workloads, and the active permissions they carry, across your cloud environment.
In this guide, we'll help you navigate the rapidly evolving landscape of AI security best practices and show how AI security posture management (AI-SPM) acts as the foundation for scalable, proactive AI risk management.
AI threat detection uses advanced analytics and AI methodologies such as deep learning (DL) and natural language processing (NLP) to assess system behavior, identify abnormalities and potential attack paths, and prioritize threats in real time.
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
Agentic AI security protects AI systems that autonomously make decisions, use tools, and take action in live environments. Agentic AI doesn't just answer questions—it acts on them.
Discover the 8 best AI security tools for protecting ML workloads in 2026. Compare features, use cases, and how to secure AI across the full lifecycle.
Discover how AI security posture management helps you secure AI models, data, and pipelines across cloud environments—risks traditional security misses.
GenAI appsec tools secure the full AI application stack, from model infrastructure and guardrails to agents, tools, and training data.
GitHub Copilot is an AI extension that plugs into existing IDEs like VS Code and JetBrains, while Cursor is a standalone VS Code fork with AI woven into the editor itself; the right choice depends on your workflow, not a universal winner.
Claude Code is a terminal-based agentic coding tool built by Anthropic; Cursor is an AI-native IDE forked from VS Code. They solve different problems, and many teams use both.
Data security posture management (DSPM) for AI extends standard data security posture management into AI-specific data flows, including training datasets, vector databases, embedding stores, inference pipelines, and AI agents.
An AI IDE is a code editor with embedded AI capabilities like code generation, debugging, and natural-language interaction that go far beyond traditional autocomplete, turning the development environment into a conversational workspace.
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.
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.
Start with investigation and triage (lowest risk, fastest value), then move to response automation, then vulnerability prioritization. Trying to do everything at once is how implementations stall.
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-DLC is an AI-centric approach to software development that positions AI as the primary executor across every phase of the lifecycle, from planning through operations, while humans provide strategic direction, approval, and oversight.
The right AI cybersecurity software for you depends on your real-world needs: posture management, noise reduction, automation, and unification with your existing cloud stack.
To manage risks associated with AI, organizations need a strategic and well-coordinated security approach that extends traditional cybersecurity measures to the unique needs of AI.
An AI Application Protection Platform (AI-APP) is a purpose-built security solution that integrates visibility, risk assessment, and active defense across the AI lifecycle.
AI coding assistants accelerate development but create new security bottlenecks. Discover the risks of AI-generated code and how to scan for vulnerabilities.
AI apps break traditional security rules. Learn how to protect models, agents, and data from prompt injection, shadow AI, and supply chain vulnerabilities.
AI agent development is the process of designing, building, and deploying software systems that use LLMs to autonomously reason, plan, and take actions. Unlike traditional chatbots or simple automation, agents make decisions, call tools, and interact with external systems on their own, which makes their development fundamentally different from conventional software engineering.
AI agent orchestration coordinates multiple specialized AI agents to accomplish complex tasks that no single agent can handle alone, using a central orchestrator to manage task delegation, data flow, and execution order across agents, tools, and cloud services.
Learn how to build an AI-BOM to track AI models, datasets, and dependencies and strengthen AI security, compliance, and governance across your organization.
Claude Code is a terminal-based agentic coding tool that reasons across entire repositories and executes multi-step tasks autonomously, while GitHub Copilot is an IDE-embedded assistant built for real-time inline code suggestions. They solve fundamentally different problems, and many teams use both.
Explore whether AI will replace cybersecurity professionals and learn why human expertise remains essential for security while AI enhances threat detection.
Shadow AI is the unauthorized use or implementation of AI that is not controlled by, or visible to, an organization’s IT department.
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.
AI security involves using AI tools for cybersecurity and protecting your AI systems themselves. Learn how to do both to mitigate evolving AI security risks.
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.
Discover how AI risk management safeguards your business against threats like bias and cyberattacks while fostering innovation and ensuring compliance.
Vibe coding is a style of coding that involves using plain speech prompts in generative AI applications to get code.
LLM guardrails are technical controls that restrict how AI-powered applications behave in production. Rather than modifying the model itself, guardrails wrap the model with policies that govern what it can see, what it can say, and what it can do, on every request.
AI model security protects AI model artifacts from attacks targeting unique vulnerabilities across the model lifecycle, from initial training through production deployment and runtime use.
Learn how to defend AI systems against prompt injection attacks that exploit LLMs to leak sensitive data, bypass controls, and corrupt model output integrity.
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.
AI model security scanning is the process of checking your models and their surrounding stack for security issues across the entire lifecycle.
An AI security graph is a graph-based model that maps how AI systems actually operate in the cloud. Instead of analyzing models, infrastructure, identities, or data in isolation, it represents them as interconnected nodes.
Agentic AI threats are control failures, not intelligence failures. They emerge when autonomous systems operate with persistent access, delegated authority, and insufficient guardrails across cloud environments.
AI supply chains expand the attack surface far beyond traditional software, introducing new risk across models, training data, inference pipelines, and AI-specific dependencies.
AI guardrails (also called LLM guardrails or GenAI guardrails) are preventive safety controls that constrain an AI system’s behavior within defined policy boundaries.
An AI inventory is a continuously updated view of every AI system running in your environment – including models, endpoints, SDKs, and the cloud resources they rely on.
AI data classification is the process of using machine learning to automatically sort and label data based on its content and sensitivity.
Dark AI involves the malicious use of artificial intelligence (AI) technologies to facilitate cyberattacks and data breaches. Dark AI includes both accidental and strategic weaponization of AI tools.
AI runtime security safeguards your AI apps, models, and data during active operation. Going beyond traditional security’s focus on static pre-deployment analysis, runtime security monitors AI behavior at inference while it actively processes user requests and sensitive data.
Data poisoning threatens the cloud, especially when 70% of cloud environments use AI services. Learn about the top threats and how to protect your organization.
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.
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.
In this blog post, you’ll discover how Kubernetes plays a crucial role in AI/ML development. We’ll explore containerization’s benefits, practical use cases, and day-to-day challenges, as well as how Kubernetes security can protect your data and models while mitigating potential risks.
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.
LLM jacking is an attack technique that cybercriminals use to manipulate and exploit an enterprise’s cloud-based LLMs (large language models).
