AI has become one of the most common topics in technology conversations. Some organizations are already using it extensively, while others worry they're falling behind.
My advice is simple: don't start with the AI tool. Start with the problem you're trying to solve.
AI can be incredibly useful for improving productivity, organizing information, and helping employees work more efficiently. It can also introduce security, compliance, and data protection concerns if it's used without the right guardrails.
In this guide, we'll cover:
Before opening ChatGPT, Copilot, Gemini, Claude, or any other AI platform, ask yourself:
If you can't clearly explain the problem you're solving, you may not need AI at all.
Shadow AI refers to the use of AI tools that have not been approved by your organization's IT or security team.
Most employees aren't trying to create risk. They're simply trying to work faster. The challenge is that organizations often have little visibility into which AI tools are being used or what information is being entered into them.
Examples include:
As AI adoption continues to increase, visibility and governance become just as important as the technology itself.
If your organization has approved AI platforms, start there.
Different AI tools have different security models, privacy practices, and data handling policies. Just because two products appear similar does not mean they provide the same protections.
For organizations already invested in Microsoft 365, Copilot is often a natural place to start.
Copilot works within existing Microsoft 365 permissions and respects the same access controls users already have. Security controls such as data classification, encryption, and data loss prevention policies can continue to play a role in protecting information.
That doesn't eliminate risk. Organizations still need governance, policies, and user training. AI should be treated like any other business technology: useful, but requiring oversight.
Great beginner projects include:
AI can be helpful, but it can also be wrong.
Always:
Know the difference between:
If you're unsure whether a piece of information should be entered into an AI system, stop and ask. The few minutes spent getting clarification are worth avoiding a security incident.
Examples include:
Always follow your organization's policies regarding approved AI use and data handling.
One of the most common misconceptions is:
"It's okay. I deleted the conversation afterward."
Deleting a conversation does not change the fact that the information was entered into the system in the first place.
The better approach is making sure sensitive information was approved for use in that platform before it's ever entered.
AI generates responses using a combination of its training, the information you provide, and, depending on the platform, information from connected systems or the internet.
That means:
Treat AI like a helpful assistant, not a final authority.
The fastest solution is not always the safest one.
Saving five minutes today isn't worth creating a compliance issue, exposing sensitive information, or introducing unnecessary risk to the organization.
Vibe coding is the practice of creating software primarily through AI-generated code.
A user describes what they want:
"Build me an employee time-off tracking system with approvals and reporting."
AI generates much of the code. While that can dramatically speed up development, it can also introduce risks if nobody reviews what was created.
Common risks include:
|
Risk |
Example |
Key Takeaway |
|
Working code that isn't secure |
An AI-built application functions correctly but lacks proper access controls. |
If it's not secure, it's not ready for production. |
|
Exposed credentials |
Passwords, API keys, or sensitive information are embedded in the code. |
Never assume AI follows security best practices. |
|
Applications nobody can support |
The creator doesn't understand how the system works. |
If nobody can maintain it, it becomes a business risk. |
Before relying on an AI-generated application, ask:
If the answer to any of those questions is no, the project needs more review before it's ready for business use.
Successful AI adoption doesn't come from choosing the right chatbot.
It comes from:
Organizations should encourage employees to explore practical AI use cases. In many cases, the biggest risk isn't AI itself. It's employees adopting tools without guidance.
A practical approach includes:
AI isn't going away. The organizations that will benefit most are the ones that balance innovation with responsible governance.
Start with the business problem, understand the data involved, put reasonable guardrails in place, and expand from there.