Before you paste anything into an AI tool, ask yourself one question first: what kind of information am I about to share?
You've probably paused before hitting enter on an AI prompt and wondered if you were about to make a costly mistake.
That hesitation makes sense.
According to a 2025 Harris Poll survey commissioned by Express Employment Professionals, 72% of U.S. companies already have AI tools in place, but 55% say they don't have the training or resources to use them well.
Many employees believe AI is either completely safe or completely dangerous. Neither is true. The real risk depends on the data you're handing over.
For example, a public press release is nothing like a customer database, and a product description is nothing like next year's financial forecast. Treat them the same, and you either create unnecessary risk or unnecessary fear.
That uncertainty slows organizations down. Some employees avoid AI because they're worried about exposing something sensitive. Others move too fast because they assume every AI tool works the same way. Both leave value on the table.
Using AI responsibly is more about habit than expertise. You don't need a security background, just one simple move: check what you're about to share before you think about what AI can do with it.
What Kind of Data Are You Working With?
Most people decide whether to use AI before they decide what they're sharing. That's where the mistakes start.
The safest AI users ask a different question first: what kind of data am I about to paste into this prompt?
Think of your information as falling into one of four categories:
1. Public information is already out in the world: press releases, published blog posts, product descriptions, anything already on your company's website. This is usually the safest type of content to hand to AI.
2. Internal business information is meant for employees, but wouldn't do serious damage if it leaked (e.g., meeting notes, draft marketing ideas, internal procedures). Some companies allow this in approved AI tools. Others don't. Check your company's policy.
3. Confidential business information needs real protection, like sales forecasts, pricing strategy, product roadmaps, source code, merger talks, and customer lists. Don't put any of this into an AI system unless your organization has approved both the tool and the specific use.
4. Regulated or personal information gets the highest level of care (customer records, employee files, account numbers, medical information, or anything personally identifiable). Entering this without authorization can mean legal, financial, and reputational trouble.
This shifts your attention away from the AI tool and back to the information itself.
If you're in marketing, you can safely use public product information for social copy, but think twice before pasting in an unreleased campaign strategy. In IT, you can ask AI to explain a programming concept without handing over proprietary code. And if you're a business leader, you can get help polishing a presentation without uploading confidential board materials.
You don't need to memorize every privacy regulation to get this right. You just need to recognize what kind of information you're holding. Once you know that, the rest of the decision gets easy.
Everything else here builds on this first habit. Identify your data before you touch AI, and you're already ahead of most people.
How Do You Measure the Risk Before You Paste?
Once you've identified the type of data you're working with, it's time to measure what could go wrong. According to IBM's 2025 Cost of a Data Breach Report, breaches tied to shadow AI, AI use that never got IT's approval, already cost companies $670,000 more on average than standard breaches. You don't need to imagine the worst case every time you use AI. You just need to pause long enough to measure the risk.
Who owns this information?
If it belongs to a customer, partner, employee, or anyone outside your organization, handle it with extra care. If it belongs to your company, make sure you're authorized to share it with an AI tool at all.
Would this create a problem if it became public?
Picture the prompt showing up on a competitor's desk, or in tomorrow's headlines. If that thought makes you uncomfortable, don't paste it into AI until you know it's allowed.
Does this include personal or regulated data?
Names, email addresses, financial records, and health information usually come with legal obligations attached. Even a secure AI tool doesn't erase your organization's policies or the regulations that still apply.
Can I remove the sensitive details?
Often you don't need the real data to get a useful answer. Swap a customer's name for a placeholder. Substitute sample numbers for actual financials. AI can usually give you the same guidance without ever seeing the sensitive part.
In practice: if you're in marketing, you don't need to upload an unreleased campaign brief to get stronger headlines. A summary of the goals is usually enough. If you're in IT, you don't have to paste an entire configuration file with passwords or API keys in it. A sanitized example usually gives AI everything it needs to help you troubleshoot. If you're a business leader, you can get help sharpening a strategic presentation without including confidential revenue projections or acquisition plans.
This doesn't make AI less useful, it just means AI only sees what it needs to help you.
The best AI users write smarter prompts, not just better ones. They know which details are essential and which ones should never leave their hands.
Which AI Tool Should You Use?
Not every AI tool works the same way. The same piece of information might be fine in one system and completely off limits in another.
This is where people get tripped up. They assume every AI tool offers the same privacy and security. It doesn't.
Your company may have an enterprise AI platform built to protect internal information, including stricter access controls, data retention policies, and contractual safeguards that public tools don't offer. If you have one, use it for approved internal business information.
But doesn't an approved enterprise tool already cover this?
Not entirely. Enterprise-grade security protects the platform. It doesn't decide what should be typed into it in the first place. A secure tool can still store or surface sensitive data in ways you didn't intend. That happens when someone puts it in without thinking it through first. The platform's security and your judgment about what to share are two separate jobs. You still need both.
Public AI tools deserve a different level of caution. They're great for brainstorming, improving writing, explaining concepts, and working with information that's already public. They're not the place for confidential business plans, customer data, financial records, source code, or anything your organization treats as sensitive, unless you've been explicitly told it's fine.
When you're not sure, don't guess. Ask your manager. Talk to IT or security. Check your organization's AI policy. Confirming what's allowed takes a few minutes. Explaining why sensitive information ended up in the wrong place takes a lot longer.
AI doesn't always need your actual data to help you, either.
Instead of uploading a confidential sales report, describe the trends and ask for recommendations. And rather than sharing proprietary source code, build a simplified example that shows the same problem. Swap in fictional placeholders instead of including real customer names and account details.
You'll often get the same quality of response while cutting your risk way down.
Match your data to the right platform, strip out sensitive details when you can, and AI becomes a tool you trust instead of a risk you're managing. That's what lets people move quickly because they know where the boundaries are, not because nobody's told them where the boundaries are.
How Do You Make This Second Nature?
Knowing what data is safe to put in AI is a habit, not a decision you make once. You build it every time you open an AI tool.
At first, you'll stop and think through each choice. That's normal. Before long, you'll recognize risky information almost automatically. You'll know when to strip out sensitive details, reach for an approved platform, and leave certain information out altogether.
That's when AI gets more useful.
If you're in marketing, campaigns move faster because you're not second-guessing every prompt. In IT, you spend less time cleaning up preventable security incidents and more time helping the business use AI well. And if you're a business leader, you can push for more AI use without worrying your team is leaking confidential information along the way.
Everyone benefits when the rules are clear:
- Employees trust new tools because they understand where the lines are.
- Managers spend less time fielding the same questions.
- Security teams get to focus on real threats instead of preventable mistakes.
Organizations move faster, too. When people know what they can share, they stop hesitating. They write better prompts, try new ideas, and solve problems faster because they're not constantly wondering if they're breaking a rule.
More than 80% of employees already use AI tools their company hasn't approved, according to UpGuard's 2025 State of Shadow AI report, and 70% say they're aware of sensitive company data being shared with those tools. A policy document sitting in a shared drive won't fix that. What works is practical training that helps people recognize sensitive data, avoid common mistakes, and make good calls in real situations, in the moment, not after the fact.
AI SkillsBuilder® Essentials is built to do exactly that. It gives marketers, IT professionals, business leaders, and employees across every department a practical way to use AI responsibly: how to recognize sensitive information, write better prompts, protect your organization's data, and steer clear of the mistakes that slow adoption down and drive risk up.
Every prompt you write is a decision. You're deciding what deserves protection, what AI needs to see, and how much risk you're willing to accept. Those decisions get a lot easier once you follow a simple process: identify the type of data you're working with, measure what happens if it gets out, and choose the right tool for the job. Repeat that until it's second nature.