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Your employees are using AI right now.
They're pasting code into ChatGPT to debug faster. Uploading customer lists to AI tools for analysis. Asking Gemini to draft emails with project details. Using Copilot with access to your entire codebase.
And according to Software AG's 2024 research, there's a 75% chance you have no visibility into any of it.
Worse: 40% of workers admit to using AI tools without authorization. That's not a small group of rule-breakers—that's nearly half your organization actively using AI tools your security team doesn't know about.
Think about the most common security issues companies are currently blind to:
An employee uploads a spreadsheet of customer emails to an AI tool to "help with segmentation." That data is now in a model you don't control. It could be in training data. It could appear in responses to other users. You have no way to know.
A developer uses an AI coding assistant connected to your private repository. Proprietary algorithms, API keys, database schemas—all processed by an external service. You have no logging. No monitoring. No governance.
Your legal team uses AI to summarize confidential documents. Your finance team asks AI to analyze sensitive projections. Your HR team uploads employee reviews for "better feedback suggestions."
None of this violates your current policies—because you don't have AI policies yet.
This is shadow AI. And it's already in your organization.
Your traditional security controls can't see it. Your DLP tools weren't built for it. Your perimeter defenses don't prevent it. And without AI-specific governance frameworks, you're blind to the risk.
So what does a solution actually look like?
Start with visibility. You can't govern what you can't see—so you need monitoring that shows which AI tools employees are using and what data they're sharing. From there, establish governance frameworks that don't just block AI (which drives it underground) but create approved tools, clear usage policies, and data classification rules employees actually follow.
Then layer in technical controls: API gateways that inspect AI interactions, updated DLP policies for AI-specific data flows, and logging that captures what's being shared. And critically, you need an incident response plan that accounts for AI breaches—because when sensitive data leaks through an LLM, traditional containment strategies don't work.
This is what AAISM teaches you. How to establish AI governance frameworks that give you visibility and control over AI adoption. How to detect and manage shadow AI usage across your organization. How to implement controls that secure AI tools and data flows before sensitive data walks out the door through tools your security stack can't even detect.
AAISM is the only certification of its kind—meant for security management, not developers or researchers.
The question is: do you implement AI governance before or after you discover what's been shared?
Our AAISM bootcamp starts December 1.
Stay secure,
The DestCert Team
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