AI misuse didn’t start with today’s LLMs — early adoption already showed a pattern of accidental data loss, shadow tools, and people pasting sensitive information into systems they didn’t fully understand. As AI scaled, those small missteps became systemic risks because data handed to AI tools is often retained permanently, creating irreversible exposure.

Threat actors now use AI to speed up reconnaissance, craft highly convincing social engineering, and automate exploitation — collapsing what used to take days into minutes. The result: AI dramatically amplifies attacker efficiency while widening the gap for defenders relying on legacy controls.

DLPs, CASBs, and DSPMs weren’t built to understand prompts, embedded AI features, or the long tail of SaaS apps now exposing data to models — they block or log, but they don’t understand behaviour or intent. To protect AI usage, you need context-, content-, and behaviour-aware visibility that operates where risk actually happens: inside browsers and SaaS workflows.

If you can’t see every prompt, file, or AI interaction across sanctioned, shadow, and embedded tools, you don’t have control — you have blind spots. A proper AI Risk Assessment gives instant visibility into who’s using AI, what data they’re exposing, and where your policies are (or aren’t) being enforced.

The Rise of AI: Innovation, Exploitation & the Battle for Control

The early history of AI misuse and abuse

Hosted by
Oliver Simonnet

The Rise of AI: Innovation, Exploitation & the Battle for Control

The early history of AI misuse and abuse

Hosted by Oliver Simonnet

Hosted by
Oliver Simonnet