Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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. ‍

For a long time, compliance meant paperwork. Fill out the right forms, pass the annual audit, file it away. Done. Now, your development pipeline is generating code at a pace no human team can review manually, your supply chain runs three layers deep into open-source packages and AI plugins you didn’t choose, and regulators are watching in real time. The old approach doesn’t just underperform; it creates a false sense of security.

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

Cybercrime used to have a ‘"tell." It was the digital equivalent of a villain stroking their cat - clunky grammar, misspelt links and suspicious attachments that screamed ‘phishing’. But the arrival of AI has changed everything. Typos have been replaced by perfect prose. Generic lures have evolved into highly personalized attacks that mimic your internal language and align with your project timelines.

TL;DR While improving our AI Pentest, we ran a whitebox assessment on NodeBB, a forum software powered by NodeJS. The result? Eight high-severity vulnerabilities that would all be exploitable on default instances of NodeBB. This includes Cross-Site Scripting (XSS), two of which require interaction with a custom Federation server that the AI agent had to set up itself. Another affects practically every input on NodeBB due to a template injection.