AI tools are everywhere. Your team is probably already using ChatGPT, Copilot, or a dozen other AI-powered apps — whether you know it or not.
That's not necessarily a problem. But here's what is: most organizations have zero strategy for securing their AI usage.
The Shadow AI Problem
When employees use AI tools without IT oversight, you get "shadow AI" — the AI equivalent of shadow IT. And it creates real risks:
- Data leakage: Employees paste sensitive data into public AI tools without realizing it gets stored and potentially used for training.
- Prompt injection attacks: AI-powered customer-facing tools can be manipulated to reveal system prompts, internal data, or behave in unintended ways.
- Compliance gaps: Regulated industries (healthcare, finance, government) face serious liability when AI tools process protected data without proper controls.
What an AI Security Strategy Looks Like
It doesn't have to be complicated. Start with these three pillars:
1. Inventory Your AI Usage
You can't secure what you don't know about. Audit which AI tools your organization uses — officially and unofficially. You'll probably be surprised.
2. Classify Your Data
Not all data carries the same risk. Create clear guidelines about what can and can't be shared with external AI services. Make it simple enough that anyone can follow it.
3. Test Your AI Applications
If you're building AI-powered features (chatbots, document processors, recommendation engines), test them for vulnerabilities. Prompt injection, data extraction, and jailbreaking are real attack vectors that most dev teams don't think about.
The Bottom Line
AI is a powerful tool. But like any powerful tool, it needs guardrails. The companies that get this right early will have a massive advantage over those scrambling to fix problems after the fact.
Don't wait for an incident to take AI security seriously.
Need help building an AI security strategy for your organization? Let's talk.