AI security is the practice of defending systems with AI capabilities while protecting machine learning assets from prompt injection, model inversion, and data poisoning. In practice, this splits into two focus areas. Security of AI covers defending models, pipelines, and vector stores by locking down training data and monitoring inference endpoints. AI for security flips the lens, putting machine learning to work to sharpen threat detection and correlate signals faster. Explore the resources below to dig into risk frameworks, compliance standards, and practical controls.
Understand how Wiz protects AI models, data, and workflows 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 infrastructure is the hardware, software, and networking used to build, train, and run AI models. Learn its core components, benefits, and security.
# Meta Description Agentic AI use cases explained: how autonomous AI agents work across customer service, IT, finance, and cybersecurity, plus the new security risks to manage.
Agentic AI vs generative AI: generative AI creates content from prompts, while agentic AI plans and takes multi-step actions on its own. See the key differences.
Guarda come Wiz trasforma la visibilità istantanea in una rapida bonifica.
The A2A protocol is an open standard that lets independent AI agents discover each other and coordinate tasks. Learn how it works and how to secure it.
AI data integration is the use of machine learning, natural language processing, and large language models to automatically connect, clean, map, and move data from many sources into a single, unified view.
AI guardrails (also called LLM guardrails or GenAI guardrails) are preventive safety controls that constrain an AI system’s behavior within defined policy boundaries.
Shadow AI creates data exposure, compliance gaps, and governance blind spots. Learn the risks, detection methods, and how to build a program that works.
Claude Mythos security explained: how Anthropic's vulnerability-finding AI reshapes the threat model and the practical steps teams can take to defend.
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.
Learn how generative AI security differs from traditional security, its key risks, frameworks, and best practices to protect your organization's AI investments.
Learn how AI agent security helps CloudSec, AppSec, and SecOps teams control risks, monitor behavior, and close critical security gaps in agent deployments.
Learn what an AI audit evaluates, which frameworks apply, and how continuous cloud visibility supports AI audit readiness for security teams for your company.
Learn how model context protocol security works, which MCP attack vectors to prioritize, and the best practices your team needs to reduce real exposure.
AI agent sprawl is the uncontrolled multiplication of autonomous AI workloads, and the active permissions they carry, across your cloud environment.
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.
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 misconfigurations are security mistakes in how your AI systems are set up. They happen when AI infrastructure, models, or services have improper settings, excessive permissions, or insecure defaults that expose your organization to risk.
AI in cybersecurity is the use of artificial intelligence – combining data ingestion, behavior- and context-aware analysis, and automated actions – to detect, interpret, and respond to threats across today's cloud-native, dynamic environments.
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.
AI cyberattacks are threats that either target AI systems –models, pipelines, agents, APIs, and the sensitive data behind them –or use AI to enhance or automate traditional attack techniques.
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).
