This article explores why AI is complicating compliance and the critical steps enterprises must take to avoid regulatory pitfalls.

CVE-2025-0108 is a high-severity authentication bypass vulnerability affecting Palo Alto’s PAN-OS, the operating system for their next-generation firewalls. This flaw allows an unauthenticated attacker with network access to the PAN-OS management web interface to bypass authentication controls and execute restricted PHP scripts.

Lately, it seems like the only thing anyone is talking about in the technology sector is Artificial Intelligence. With good reason! AI is an incredibly powerful tool that is only going to grow in usage and scope. However, there seems to be a lot of confusion around various terms involving AI and security. The focus of this blog will be breaking down the differences between securing AI, secure AI use, AI for security, and AI safety.

Confidently scale AI initiatives with irrefutable proof of ethical data practices and compliant AI operations. In today’s rapidly evolving digital landscape, the intersection of data provenance and AI governance has become mission-critical for businesses. As AI continues to shape industries – from healthcare and finance to media and technology – the need for verifiable data integrity and responsible AI oversight has never been more urgent.

Our journey with GitHub proxy support began with an internal challenge: securing our most critical repositories against unauthorized acess. As a company that manages infrastructure as code, including sensitive systems like Hardening Okta with Terraform, we needed an ironclad solution to lock down acess to our codebase. The problem was that traditional authentication methods like SSH keys and Personal Access Tokens (PATs) left our repositories vulnerable to unauthorized access.

Machine Learning (ML) has revolutionized industries by empowering systems to learn from data, make predictions, automate decisions, and uncover insights—all without the need for explicit programming. With ML, systems can: In network security and cybersecurity, ML and other emerging technologies are crucial for detecting malicious activities such as unauthorized access, data breaches, and other complex security threats.

Companies are rapidly moving to hybrid cloud environments, with most of them already making this transition. This fundamental change affects how organizations handle their infrastructure.