Using AI Safely at Work: A Practical Guide
AI tools are powerful and your team is already using them. The question is whether they're using them in ways that put your business at risk. This guide covers the practical do's and don'ts of using AI safely at work, written for business users by a cybersecurity firm.
Why This Guide Exists
Most AI content focuses on what AI can do. This guide focuses on what AI shouldn't do with your business data.
That matters because most AI mistakes at work aren't malicious. They're well-meaning. An employee pastes a contract into ChatGPT to get a summary. A salesperson drops a customer list into Claude to draft outreach. A new hire connects an AI plugin to their work email "to be more productive." None of them are trying to cause harm. They're trying to do their jobs faster.
But each of those moments creates risk that didn't exist before. Customer data flowing to a third-party platform with terms of service nobody read. Internal documents living in a chat history that may be used to train future models. Permissions granted to AI tools that quietly extend across systems.
This guide is the conversation every team should be having before AI adoption gets ahead of governance.
The Real Risks of AI at Work
Data Exposure Through AI Tools
The most common AI risk has nothing to do with sophisticated attacks. It's an employee pasting sensitive information into a consumer AI tool whose terms of service permit the platform to retain and use that input. Customer names. Financial figures. Source code. Contracts. Once pasted into the wrong tool, the data is out of your control.
Shadow AI
Shadow AI is the AI-tool version of shadow-sm IT: tools that employees adopt without IT or security review, often because they save real time. Browser extensions, AI-powered note-takers that join every meeting, plugins quietly connected to email, calendar, and cloud storage. You can't govern what you can't see.
Vendor and Supply Chain AI Risk
Your SaaS vendors are embedding AI into their products. Your payroll provider, project management tool, accounting software, and email platform are all adding AI features — often enabled by default. Every vendor that adds AI introduces a new data flow you may not have mapped.
AI-Generated Output Trusted Without Verification
AI is excellent at generating plausible-sounding content. It is not authoritative. Citations that don't exist, statistics that are made up, legal language that misstates regulations — all have shown up in business deliverables because someone trusted an AI output without verifying it. The risk isn't that AI is wrong. The risk is that AI is wrong in ways that look right.
AI-Augmented Social Engineering
The same AI tools your team uses to write better emails are being used by attackers to write better phishing. Voice cloning, video deepfakes, and AI-generated business email compromise are no longer theoretical — they're showing up in real fraud cases.
What to Do When Using AI at Work
Consumer-tier AI tools (free ChatGPT, free Claude, free Gemini, free Copilot) have weaker data handling than their enterprise counterparts. Most enterprise tiers commit in writing that inputs are not used to train future models. Most consumer tiers do not. The cost difference is small. The risk difference is large.
Is my input retained? Is it used to train the model? Who has access on the platform's side? How long is it stored? What happens if I cancel? Is it geographically restricted? If you can't answer these for an AI tool your team uses, that's the problem to fix first.
Replace specific names, financial figures, customer identifiers, and internal system names with generic placeholders. A draft contract becomes "[Vendor]" and "[Customer]" rather than the real names. The AI's output is usually just as useful, and the exposure risk drops dramatically.
Treat AI output the way you'd treat output from a smart, fast intern. Useful, worth your time, but never the final answer. Always verify citations, numbers, legal claims, code, and quoted names. Verification takes 30 seconds. Cleaning up a public mistake takes weeks.
Most AI tools let you set behavior preferences and context that persist across conversations. A well-configured system prompt or personalization profile dramatically reduces common AI failures. For Claude, use Org instructions and Projects. For Copilot, use personalization settings and approved agents.
If a message feels off — overly urgent, asking for unusual access, requesting fund transfers, claiming to be from leadership — treat it as potentially AI-generated until verified. Verify through a known second channel. "I got an email from the CEO" is no longer sufficient confirmation.
If you start using a new AI tool for work, tell your IT or security team. Not because they'll say no, but because they need to know what's in the environment to govern it. Adopting AI tools quietly is how shadow-sm AI builds up over time.
What Not to Do When Using AI at Work
Customer names and contact details, financial figures, source code, contracts, internal strategy docs, employee performance data, anything covered by HIPAA / PCI DSS / NDAs — none of it goes into consumer-tier AI. Period.
AI plugins often request broad permissions — read your email, access your files, integrate with your CRM. Each extends the AI tool's reach into systems that contain sensitive business data. Route them through IT or security before granting access.
AI generates plausible text. Plausible is not the same as correct. The cost of trusting hallucinated output ranges from embarrassing (wrong stat in a public post) to serious (wrong regulatory claim in a customer comm).
If your company has an approval process for vendor contracts, don't use AI to draft and send a contract that bypasses it. AI accelerates work. It doesn't replace the controls that exist to protect the business.
If a customer, partner, or third party shared something with you under confidentiality, don't paste it into AI even if your own platform is enterprise-tier. You may have agreed not to share that information with third parties, and the AI vendor is a third party.
Hiring and firing, performance evaluations, customer escalation responses, disciplinary actions, legal positioning, crisis comms — anything with real consequences for real people. Use AI as a thinking partner. Make the actual decision yourself.
The Safe AI Quick Checklist
A 10-second mental check before any AI interaction at work.
- 1Am I on an enterprise tier of this AI tool? If no, am I working with non-sensitive data?
- 2Is the data I'm about to paste sensitive? If yes, have I sanitized it first?
- 3Has this AI tool been approved by my company? If no, should it be reviewed before I use it?
- 4Will I verify the output before acting on it? If no, what's the worst case if AI is wrong?
- 5Am I about to grant this tool access to other systems? If yes, do I understand the scope?
Five questions. Ten seconds. They prevent the majority of AI-at-work mistakes.
When to Escalate to IT or Security
Some AI situations warrant immediate escalation. Don't try to handle them yourself.
- You suspect sensitive data was pasted into a consumer AI tool
- You suspect an email, call, or message is AI-generated impersonation of a colleague or executive
- A new AI tool wants access to systems containing customer or financial data
- An AI tool you've used unexpectedly references information it shouldn't have
- You're being pressured to take an action by a message that could be AI-generated
- You've received output from an AI tool that suggests a data leak (other companies' data, unexpected internal info)
- A vendor has added AI features to a product handling your data without giving you the option to disable them
When in doubt, escalate. IT would rather investigate a false alarm than discover an issue weeks after the fact.
Rolling Out AI Safely in Your Team
Make Approved Tools Easy
The biggest cause of shadow-sm AI is that the approved tools feel slower or more cumbersome than the consumer alternatives. Friction drives bad behavior.
Train on Specifics, Not Generalities
"Be careful with AI" is useless training. "Here's what not to paste. Here's what to paste instead. Here's how to verify output." That's training.
Build a Reporting Path That's Used
If your team can't easily report "I think I may have pasted something I shouldn't have," they won't. Most data exposure incidents are recoverable if surfaced quickly.
Lead by Example
If you, as a manager, paste sensitive data into consumer AI tools, your team will do the same. The fastest way to build a strong AI culture is for senior people to model the behavior they want.
This Guide Is a Starting Point, Not a Substitute for Governance
The do's and don'ts above are a strong foundation, but they're not a substitute for a real AI governance framework. As your organization's AI use grows, you'll want structured policies, approved tool lists, vendor reviews, training programs, and incident response procedures.
See AI Governance Templates for sample acceptable use policies, governance framework templates, and approval-process templates.
Explore Governance TemplatesWhere to Go From Here
Build governance: AI Governance Templates
Master your tools safely: Claude Tips & Tricks · Copilot Tips & Tricks
Use prompts responsibly: The AI Prompt Library
Understand the foundations: How Does AI Work
Want Help Rolling Out AI Safely Across Your Organization?
Defensible's AI Readiness Assessment evaluates how your team is using AI today, where the risks live, and where AI can deliver real business value without exposing your environment. Practical, fast, and grounded in cybersecurity best practices — aligned to NIST AI RMF, NIST CSF, and other applicable frameworks.