Can You Actually See Your AI Environment — Or Are You Just Assuming? For the Practitioner
- ~Mimi
- 5 days ago
- 3 min read
Updated: 24 hours ago
When was the last time someone in your organization installed an AI tool without telling anyone?
If you're being honest, you probably don't know. And that's exactly the problem. The last few weeks have definitely kept me up at night, thinking about how quickly AI is changing our environments.
What's Been Happening
Autonomous Mythos Preview at Anthropic found a 27-year-old bug in OpenBSD and a 17-year-old remote code execution flaw in FreeBSD — vulnerabilities that survived decades of expert review and millions of automated tests. Found in hours, for under $20,000.
Meanwhile, a company called Anodot — an AI analytics platform — was breached. Attackers stole the authentication tokens that Anodot used to access customer environments and walked straight into over a dozen organizations connected via Snowflake. No direct breach. Just a trusted integration nobody was watching.
And Oracle announced tens of thousands of layoffs while doubling down on AI infrastructure spend. While Salesforce, which cut its support workforce nearly in half, expected AI agents to fill the gap, it is now reportedly trying to rehire those workers after executives acknowledged they overestimated what AI could handle.
The Visibility Problem We Keep Not Solving
We've had this conversation before — just with cloud. Shadow IT, ungoverned environments, fragmented visibility. Most organizations are still working through that. Now we're having the same conversation about AI, except faster and with higher stakes.
I've seen this firsthand. When you pull vulnerability scanning data and actually look at what's running, you can find AI tools that were never approved, browser extensions on corporate credentials, and apps registered with company emails that never went through any vetting. The people who installed them weren't being reckless. They were being efficient. But you can't secure what you can't see — and most security teams are working with an incomplete picture of their AI environment right now.
Maturity Scores Don't Fix a Visibility Gap
Maturity models assume you know what exists in your environment, how it's being used, and that governance is in place. If your AI inventory is incomplete, your maturity score is measuring a partial picture. You're not immature — you're just not fully in view.
The Snowflake situation is a good example. Anodot had legitimate access — that access was the product. When Anodot was compromised, that trust became the attack path. It's not enough to know a vendor is approved. You need to know what access they hold, how it's used, and whether you'd know if something went wrong. KPMG found 62% of business leaders cite workforce skills gaps as their top AI ROI barrier — but the gap most security teams aren't talking about is visibility into the AI integrations their vendors run on their behalf.
Where to Start
Get an honest inventory. Not what's approved — what's actually running, including vendor integrations that touch your data.
Assign ownership. Someone has to own AI risk across security, IT, legal, and the business. If it's unclear who, that's your answer.
Pressure test your response speed. AI is compressing the window between discovery and exploitation. Slow incident response is a liability.
If you need to bring this conversation to your board, I wrote a separate piece for that audience: Your Board Is Asking the Wrong AI Question.
Maturity without visibility isn't maturity. It's an assumption.
Sources
1. Anthropic — Project Glasswing & Claude Mythos Preview (April 7, 2026) https://www.anthropic.com/glasswing
2. Anthropic Red Team — Mythos Preview technical details (April 7, 2026) https://red.anthropic.com/2026/mythos-preview/
3. BleepingComputer — Snowflake customers hit in data theft attacks after SaaS integrator breach (April 7–9, 2026)
4. KPMG — AI Quarterly Pulse Survey Q4 2024
5. Wipfli — Salesforce failed to replace thousands of workers with AI (February 2026)
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