Why are AI Governance Platforms dead on arrival?

Martin Snyder
Jun 12
3 min read

Updated: Jun 26

AI Governance Tools and especially AI Security Tools are all the rage. Unfortunately the reality looks completely different. According to SaaS Capital’s latest report, 76% of private SaaS companies are already leveraging AI in their products. That number is only going to rise.

AI is no longer a differentiator. It’s not even a “feature.” It’s infrastructure — just like TLS for encryption or OIDC for authentication and we don't here about "TLS Governance Platforms".

So when enterprises start throwing around the term “AI governance” as a future-proofing mechanism, we have to ask:

Are you governing AI…or are you just labeling wishful thinking?

The AI Is Already Here — You Just Don’t See It

The issue isn’t that AI is ungoverned. It’s that you can’t govern what you don’t know exists.

AI is already deeply embedded in the tools and platforms you use every day — and you probably never approved it.

Here’s a snapshot of your day-to-day AI exposure:

Your SASE vendor is optimizing traffic with AI — ungoverned.
Your browser is personalizing tabs and autofill with AI — ungoverned.
Your OS is using AI to predict usage patterns — ungoverned.
Your email provider is filtering with machine learning — ungoverned.
Every time you call vendor support, AI is scripting the call — ungoverned.
That new SaaS app you rolled out last week? Likely trained on user behavior — definitely ungoverned.
Even your search engine is rewriting queries and personalizing results with AI — and yes, still ungoverned.

AI is no longer a product decision. It’s a vendor decision.

And often, the only way you’ll know it’s there is when it fails.

Governance Platforms Are Solving the Wrong Problem

According to CIO.com ’s analysis, AI governance platforms are still waiting for customers to catch up. The reality?

Most organizations don’t even have a basic inventory of their AI exposure
Vendors aren't transparent about how or where AI is used
Governance platforms assume centralized, visible, in-house models
But the real risk lives in external apps, APIs, and integrations

As the article points out, the governance stack is being built for a future that doesn’t match today’s messy reality.

“The problem with AI governance isn’t that people don’t care. It’s that they don’t know where to start — or what they’re even trying to govern.”

AI Governance Is Not the Answer — SaaS Governance Is

If 76% of SaaS vendors are already using AI, then you don’t need a framework that governs models. You need a strategy that governs the tools that use those models.

Here’s what that looks like in practice:

✅ Inventory every SaaS application

✅ Monitor OAuth and API access

✅ Review AI disclosures in vendor privacy policies

✅ Classify data flows based on exposure risk

✅ Track how AI-powered tools interact with regulated data

✅ Ask: Who owns this? Who uses this? What data does it touch?

SaaS Governance ≠ blocking innovation

It means making invisible AI visible — so you can measure and manage risk.

The Bottom Line

AI governance isn’t just dead on arrival. It was born into the wrong battle.

The real war isn’t happening in your private LLM sandbox. It’s happening across the 150+ SaaS apps, tools, platforms, and vendor systems your business uses every single day — many of them with full access to customer and employee data, and most of them invisible to security teams.

Stop trying to govern AI like it’s a contained system.Start governing the real-world tools it’s embedded in.

Because you don’t need to control the model —You just need to control who’s using it, what it touches, and whether it’s even supposed to be there.