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Tensor Steganography and AI Cybersecurity

Tensor steganography exploits two key characteristics of deep learning models: the massive number of parameters (weights) in neural networks and the inherent imprecision of floating-point numbers. Learn about this novel technique that combines traditional steganography principles with deep-learning model structures.

What You Need to Know About PCI DSS 4.0

5 Critical Capabilities for Progressing Your DevSecOps Program

Best Practices for SOC2

How to Secure Your GitHub Actions Workflows with Snyk to Enhance JavaScript Security

Snyk provides a pre-built custom Snyk GitHub Actions workflow that you can add to your CI and saves you the trouble of managing the vulnerability scans using the Snyk CLI directly.

Is TypeScript All We Need for Application Security?

What are the security controls and fallacies in TypeScript security? Securing TypeScript applications involves a multi-layered approach.

Open Source Security in 2024

Defending your FinServ Application Against Modern Threats Infographic

Thank you for checking out our infographic on securing your FinServ Applications! To learn more, download our Optimizing AppSec in FinServ eBook.

5 Regulations Every FinServ Organization Should Know About

5 Steps to Prioritize Based on Risk with Snyk - Risk-Based Prioritization Cheat Sheet

Keeping up with security can feel like a juggling act, but Snyk makes it easier by helping you focus on what matters. Follow these five steps
to protect your most important application assets and prioritize issues based on the actual risk to your organization.

Why the fastest tech organizations choose Snyk

Can Machine Learning Find Path Traversal Vulnerabilities in Go? Snyk Code Can!

Explore how Snyk’s machine learning-powered security tools tackle path traversal vulnerabilities in Golang code. Learn how to secure your Go applications and challenge yourself to detect and exploit vulnerabilities like a pro!