Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For years, phishing has worked for one simple reason: it exploits the weakest link, the user. The defensive strategy has followed the same formula: better email filtering, more user awareness, and an extra layer of authentication. It wasn't perfect, but it was a workable balance.

Most cloud breaches begin with a configuration error the customer made. Gartner projected that through 2025, 99% of cloud security failures would be the customer’s responsibility, caused by misconfigured identity and access management, exposed storage, and over-permissioned services. Cloud penetration testing is the simulation of real-world attacks against cloud infrastructure on AWS, Azure, and GCP to find those exploitable gaps before an attacker does.

Last week, an AI system crossed a line many still considered theoretical. During an internal cybersecurity evaluation, OpenAI tested a combination of models, including GPT-5.6 Sol and a more capable pre-release model, on ExploitGym, a benchmark that measures whether agents can turn software vulnerabilities into working exploits. The models were run with reduced cyber refusals and without the production classifiers normally used to prevent high-risk cyber activity.

Artificial intelligence is reshaping how enterprises process information, but it is also redefining where sensitive data is exposed. Every prompt, retrieval request, API call, and AI-generated response creates another opportunity for personal or confidential information to move beyond its intended boundaries.

Large language models are becoming the operational layer behind enterprise AI, powering intelligent assistants, automated workflows, and AI agents that interact with sensitive business systems. But as LLMs process confidential prompts, retrieve enterprise context, and execute connected actions, every runtime interaction introduces new security risks.

The recent developments surrounding vulnerabilities in major AI repositories like Hugging Face serve as a critical wake-up call for the cybersecurity community. As we accelerate toward an agentic future, the platforms we rely on for innovation are increasingly becoming the primary vectors for systemic risk.

TL;DR: AI security testing solutions find vulnerabilities in code, dependencies, and AI components across the dev pipeline.

The enterprise AI security market in 2026 is crowded and confusing. Vendors that built their products to use AI for cybersecurity operations now market themselves alongside vendors that built their products to secure AI systems and govern AI usage. These are fundamentally different product categories solving different problems, and conflating them leads to evaluation errors that leave organizations protected against external threats but exposed to the risks their own AI systems introduce. ‍

Managing a WordPress site no longer means logging in to the dashboard for every update or routine task. With the Model Context Protocol (MCP), AI assistants such as ChatGPT, Claude, and Cursor can securely interact with your WordPress site through natural language. They can retrieve content, update posts, manage WooCommerce stores, and perform other actions without custom integrations.