Security & Threat Intelligence · 25.07.2026, 15:29 UTC
AI Red Teaming Still Comes Back to Identity, Access, and Attack Paths
| Schweregrad | info |
|---|---|
| Kategorie | Security & Threat Intelligence |
| Quelle | SpecterOps ↗ |
| Veröffentlicht | 25.07.2026 UTC |
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Introduction
Most enterprise AI system risk is not a novel model failure; it’s familiar security failure modes showing up in systems with broader access, more autonomy, and more ways to touch sensitive data. However, I don’t want to discount that there are unique risks and attacks to AI systems.
The short version is simple: AI did not make secure-by-default, deny-by-default, or least privilege less important. It made ignoring them more consequential.
A lot of the real risk in AI systems that I’m seeing is not some exotic model breakout. It is familiar security failure modes showing up in systems that now have more reach, more autonomy, and more ways to touch sensitive data.
I recently joined Patrick Gray on the Risky Business podcast to talk about AI and identity security. Below is an abridged version of our conversation.
Check out the full interview with SpecterOps VP of Services Russel Van Tuyl on the Risky Business Podcast.
The Risky Business Interview: A Recap
Patrick: When people say “AI red teaming,” what are they actually testing?
Russel: That depends on who is using the term.
Sometimes people mean model testing: jailbreaks, adversarial inputs, bias, safety, alignment, and all the rest. That work matters.
But in most enterprise environments, that is not the main thing being assessed.
Most organizations are not building foundation models. They are building applications around models. That usually means a web front end, APIs, identity providers, data stores, SaaS integrations, browser sessions, internal tooling, and some model provider behind all of …