AI Security

A stat I can’t get out of my head:

61% of organizations say their AI models, data, or assets have already been compromised.

Not predicted. Not theoretical. Already happened.

And yet, when we talk about AI security in leadership rooms, the conversation still sounds familiar:

“What tools should we invest in?” “Which vendor covers this risk?”

It feels… outdated.

Because the report I was reading recently made one thing very clear:

This is not a tooling gap. This is a model mismatch.

We built cybersecurity for a world where:

• systems were deterministic
• identities were human
• threats were episodic

AI breaks all three.

Now:

• systems behave non-deterministically
• identities include agents we don’t fully track
• threats evolve continuously, at machine speed

And here’s the part that should concern every leader:

51% of organizations say their current infrastructure cannot securely support autonomous or multi-agent AI.

So we’re scaling AI… on foundations that we already know are not ready.

One idea from the report really stayed with me:

Security is shifting from protecting assets → to protecting decisions.

That’s a profound shift.

Because if AI systems are making decisions, then risk is no longer just about access.

It’s about:

• intent
• context
• and outcomes

Which is why “AI exposure management” is emerging as a core discipline.

Not just finding vulnerabilities… but continuously answering:

·       Where are we exposed?

·       Which risks actually matter to the business?

·       Can we respond faster than the system can act?

The organizations pulling ahead (only ~24% today) are not doing more security.

They are doing different security:

• treating AI agents as identities
• integrating AI + security + operations into one platform
• moving from human-led response → AI-led response with human oversight

And most importantly…

They are not asking: “How do we secure AI?”

They are asking: “How do we build systems that remain trustworthy even when they act autonomously?”

This feels like one of those inflection points.

Where incremental improvements won’t help.

You either redesign for an AI-first world… or you carry forward assumptions that no longer hold.

Curious how others are thinking about this:

Are you extending your current security model for AI? Or are you rethinking it from first principles?

In the next post, I’ll share a few practical best practices that are emerging for securing AI at scale.