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.

Snegha Ramnarayanan

6월 23, 2026

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.

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.

Lauren Place

6월 2, 2026

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.

Shaked Rotlevi

6월 2, 2026

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

Snegha Ramnarayanan

5월 13, 2026

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.

Shaked Rotlevi

5월 8, 2026

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.

Shaked Rotlevi

5월 7, 2026

Discover how AI security posture management helps you secure AI models, data, and pipelines across cloud environments—risks traditional security misses.

Ziad Ghalleb

4월 30, 2026

GenAI appsec tools secure the full AI application stack, from model infrastructure and guardrails to agents, tools, and training data.

Mike McGuire

4월 24, 2026

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.

Mike McGuire

6월 17, 2026

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.

Mike McGuire

4월 29, 2026

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.

Shaked Rotlevi

4월 17, 2026

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.

Mike McGuire

4월 16, 2026

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.

Shaked Rotlevi

3월 25, 2026

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.

Snegha Ramnarayanan

3월 23, 2026

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.

Shaked Rotlevi

3월 19, 2026

Learn how to build an AI-BOM to track AI models, datasets, and dependencies and strengthen AI security, compliance, and governance across your organization.

Mike McGuire

3월 19, 2026

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.

Shaked Rotlevi

2월 3, 2026

Discover how AI risk management safeguards your business against threats like bias and cyberattacks while fostering innovation and ensuring compliance.

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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.

Snegha Ramnarayanan

12월 30, 2025

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.

Shaked Rotlevi

12월 29, 2025

Learn how to defend AI systems against prompt injection attacks that exploit LLMs to leak sensitive data, bypass controls, and corrupt model output integrity.

Shaked Rotlevi

12월 24, 2025

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.

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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.

Snegha Ramnarayanan

12월 22, 2025

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.

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.

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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.

Snegha Ramnarayanan

10월 30, 2025

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.

Shaked Rotlevi

3월 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.

Nicolas Ehrman

3월 15, 2025

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.

Nicolas Ehrman

3월 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.

Shaked Rotlevi

8월 5, 2024

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