Everything You Wanted to Know About AI Risk (But Didn’t Want Another Webinar About)

Even if you’re still accidentally writing “2025” on important documents…
AI has already moved on without you.

Not in a sci-fi, you-no-longer-exist kind of way. In a very boring, very operational, very “wait—that tool has AI now?” way.

And that’s a problem.

The biggest myth in AI risk

Most AI risk is not about large language models. You know, LLMs.
It’s about everything they’re quietly plugged into.

And yet, most security conversations still fixate on the model:

• Is it GPT-based?

• Is it trained on our data?

• Is it secure?

• Is it hallucinating? (everyone’s favorite concern)

Those are reasonable questions. They’re just not the ones that you should start with.

Because in real organizations, AI doesn’t exist in isolation. It’s embedded across thousands of SaaS applications, connected to identities, permissions, workflows, APIs, and sensitive data. Often introducing widespread risk that no one formally acknowledged, reviewed, or accepted.

In the average enterprise, that number looks like 3,891 SaaS applications.
Yes, really — yours included.

Now imagine adding AI into that environment.

‍Explain It Like I’m New to Security

Think of AI like a very capable intern.

On its own, it’s harmless. Helpful, even. Small errors happen. The value usually outweighs them.

The risk shows up when:

• You give it access to everything

• You don’t track what it touches

• You forget it exists once it’s “working”

Now multiply that intern across:

• HR systems

• CRM platforms

• Finance tools

• Support tickets

• Internal docs

• Third-party integrations

That stops being an intern. That’s infrastructure.

AI doesn’t fail like traditional security failures

This is where many security teams get tripped up.

When AI-driven systems fail, they rarely look like:

• Ransomware

• Obvious outages

• Alarms blaring in the SOC

Instead, failures show up as:

• Data quietly leaking through prompts

• Over-permissioned AI agents acting exactly as allowed

• Sensitive data used in training without clear ownership

• Third-party exposure through SaaS integrations no one reviewed recently

• Decisions made or actions taken automatically, without meaningful human oversight

There’s often no single “incident.” Just a slow realization that something sensitive moved somewhere it shouldn’t have. Said another way: seemingly small errors accumulated into organizational risk.

This is why AI risk keeps showing up in:

• Board conversations

• Audit findings

• Compliance reviews

Even when no one can point to a clean breach timeline, there's growing recognition of a silent, growing threat.

The scale is what makes this dangerous

AI didn’t enter your environment carefully.

It arrived quietly and without an announcement:

• Through feature updates

• Embedded by default in SaaS platforms

• Enabled by individual teams solving local problems

Today—and yes, we’ve said this before—100% of modern SaaS environments include AI-powered features, whether security teams approved them or not.

And attacks are scaling to match.

In 2025, SaaS-related attacks increased 490% year over year. Many exploited gaps in visibility and governance, not flaws in the AI itself.

The risk isn’t that AI is “too powerful.” It’s that it’s everywhere.

“But we don’t use AI like that”

This is the sentence almost every organization says right before discovering:

• Shadow AI tools

• AI features turned on by default

• API-connected copilots no one reviewed

• Vendors training models on customer data

If you can’t confidently list where AI is used across your SaaS stack, you’re not alone. That’s the norm.

And that’s the problem.

The real question security teams should be asking

Not:

“Is this LLM secure?”

But:

“What does this AI have access to—and what happens if it’s wrong?”

That includes:

• Identities it can act as

• Data it can see, generate, or worse, modify

• Systems it can trigger

• Business decisions it can make

• Vendors it shares data with

• Permissions inherited through SaaS roles

AI risk isn’t a new category of risk. It’s the same old problem of who has access to what, now happening at machine speed.

From chaos to control (without the drama)

Good AI governance isn’t about banning tools or slowing teams down.

It’s about:

• Visibility into where AI exists today (not where you think it exists)

• Understanding how it’s connected across SaaS

• Governing access the same way you would any other high-impact system

• Treating AI as part of your operational environment, not a novelty

Because once AI is embedded in your workflows, pretending it’s only experimental is the riskiest move of all.

The takeaway?

AI risk isn’t coming. It’s already distributed across thousands of tools, identities, and integrations.
And it's making business decisions you don't even know.