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Snyk in 30: Building AI Trust with Snyk

Demystifying Traditional (Symbolic) AI

If deep learning is instinct, Symbolic AI is wisdom. Uncover the power of traditional, rule-based AI, why its explainability is critical for security , and how it creates powerful hybrid systems when combined with machine learning.

Addressing the Hidden Risks of Single-Page Applications

Single-Page Applications are changing the way we interact online, but at what cost to security? Explore the unique security challenges of SPAs and how to safeguard them effectively.

10 MCP Servers for Cybersecurity Professionals and Elite Hackers

Explore 10 powerful MCP servers for cybersecurity professionals and ethical hackers. Connect your AI to tools like Snyk, Nmap, and Wireshark for AI-assisted vulnerability scanning, network analysis, and security research.

Balancing Act: The Six Keys to Successfully Navigating Security and App Development Team Tensions

Tired of the friction between developers and security? Learn six keys to bridge the gap, from better training to developer-first tools. Turn roadblocks into collaboration and build secure applications faster.

How to build Node.js MCP Servers that Expose Data Resources to LLMs

MCPs are famous for exposing Tools that augment Large Language Models (LLMs) with the ability to perform actions and invoke generic functions that MCP Servers can implement, but exposing data resources to LLMs is just as important. In this article, we're building an MCP Server in JavaScript to run with a Node.js runtime and define Resources as part of the MCP Server data points.

Building Trust in AI: A Practical Guide to AI Readiness

Catch Bugs Faster: Cursor's BugBot for AI Code Review

AI Code Guardrails

What You Need to Know About Agent2Agent Protocol

Discover how Google's new Agent2Agent protocol is set to solve AI interoperability and how it enables AI agents to collaborate across enterprise environments, breaking down silos, reducing vendor lock-in, and accelerating automation.

Understanding the AI Bill of Rights: U.S. Framework for Ethical AI | Principles, Compliance, and Key Takeaways

Learn what the AI Bill of Rights is, why it matters, and how to align with its five core principles to build safer, fairer, and more accountable AI.

Claude 4 Builds Apps from ONE Prompt - An In-depth Overview

Can Google Jules Build a SECURE Note Taking App?

Agentic AI vs Generative AI

Discover the key differences between agentic and generative AI, and why those distinctions matter for innovation, automation, and security planning.

From Vision to Trust: How to Launch an AI Governance Program

Unlock safe AI innovation! Learn how Snyk built its AI governance program from vision to execution, ensuring trust, managing risk, and empowering teams to build securely and responsibly.

Building AI Trust: Secure Code at the Speed of AI | AI-Driven Security Coding Frameworks

Introducing AI TrustOps | Secure AI-Driven Development

StarChat

Discover StarChat, a uniquely tailored internal AI assistant designed to streamline the process of writing StarLang code.

Secure AI Coding With Snyk: Now Supporting Model Context Protocol (MCP)

Snyk’s newfound support for Model Context Protocol (MCP) offers real-time security to AI-powered workflows, expanding secure development across other MCP-supported AI developer tools.

Is Windsurf's SWE-1 Model a Game Changer? Let's See...

Vibe Coding with Claude Agentic AI Tool

5 Best Practices for Building MCP Servers

Building MCP Servers has become a mainstream gateway to externalize product capabilities to AI applications and AI-driven workflows. Here are 5 best practices for building MCP Servers.

From Slack Threads to Structured Knowledge: Implementing RAG at Snyk

Snyk converts Slack conversations into structured knowledge using RAG. Learn how focusing on Q&A and Gemini API extracts valuable insights for a smarter internal knowledge base.

Understanding Prompt Injection: Techniques, Challenges, and Risks

A prompt injection attack is a GenAI security threat where an attacker deliberately crafts and inputs deceptive text into a large language model (LLM) to manipulate its outputs.