Guide to responsible AI use in the workplace

AI can be a powerful collaborator, but it doesn't have your real-world experience, context, or common sense. Maintaining a human-in-the-loop approach enables you to catch issues early and deliver work you can stand behind. It’s not about memorizing a rigid set of rules. Instead, it's about building the judgment that enables you to use AI responsibly.

Take steps to ensure data privacy

The foundation of security is data privacy: Safeguarding and preventing unauthorized access to personal information and other sensitive data.

It’s important to know what data is collected and how it might be used before you accept terms of service for a website or app. Avoid entering confidential or sensitive information into public-facing tools. Before entering any prompt, it’s a good idea to pause and ask yourself, “Am I including data in my prompt that someone else might expect me to keep private?”

As the user, there are measures you can take to help protect your own privacy and security, as well as that of your organization, coworkers, and business partners. Here are a few practical ways to protect confidential information:

• Use generic placeholders when referring to people, projects, or places.
• Frame prompts around the task you need done, not the people involved.
• Input only relevant context needed to complete the task, rather than entire documents.
• Clear the memory to give the AI a clean slate, help protect privacy, and prevent bias from old prompts.
  

Check out how to manage your privacy settings and your conversation history in Gemini.

Some tasks can carry legal or compliance requirements that can limit or prohibit AI use, even when the technology seems capable.

Industries such as finance, legal, and healthcare operate under strict regulations about how decisions are made, how data is handled, and what must be documented. AI is not a substitute for a qualified professional, sound judgment, and accountability.

Before using AI for any work that might touch on regulated areas, ask yourself:

• Does this task have regulatory requirements? Managing medical records, financial reporting, legal documents, and compliance filings may have specific handling requirements. Consult your employer’s policies or a qualified professional to determine the limitations of using AI in these instances.  

• Are there licensing or credential requirements? Some work must be performed or verified by licensed professionals. AI is not a substitute for advice from a qualified professional. 

• Who is responsible? People are responsible for the inputs and outputs of AI assistants and should ensure they have the appropriate oversight when using the technology.

Consult your organization's compliance team, legal counsel, or regulatory experts and policies before using AI in these contexts.

Creating a trail that shows how you used AI responsibly can help stakeholders and others follow your process. This is an important part of maintaining professional integrity.

Here are some steps you can take to demonstrate how you’ve used AI in your work:

• Track your sources. Distinguish between AI-generated content and your own contributions so you can verify or explain your work later

• Make your process auditable. If someone asks "How did you reach this conclusion?," you should be able to walk them through which parts involved AI and how you validated them

• Document your verification steps. Keep notes on how you checked AI's work, especially for important deliverables

To effectively manage risks such as privacy and bias, you can rely on the ACT framework. This approach provides a clear structure to help you verify that you are using AI responsibly every time.

• Is AI right for this task? Don’t rely on AI for medical, legal, financial, or other professional advice. Always consult a qualified human professional.

• Is this data safe to use? Avoid inputting sensitive, confidential, personal, or proprietary data, like internal company data or customer personally identifiable information, into a public AI interface.

Am I following the rules? Always check your employer’s policies on using AI at work and make sure you’re using an approved enterprise solution versus a public one.

• For accuracy: Make sure you independently verify all information presented as fact (names, statistics, quotes, etc.), and watch out for subtle hallucinations and errors.
  
• For bias and objectivity: Always evaluate the output to make sure it does not contain unfair biases or present a one-sided argument.
  
• For appropriateness: Assess whether the tone and style of the output is right for your intended audience and purpose (like a company social media post versus a casual email).
  
• For originality and judgment: Edit, refine, and add your own expertise to improve the AI output.

• Be transparent: Clearly and appropriately disclose where and when AI was used to help generate content.
  
• Be compliant: Always follow your company’s specific guidelines on AI disclosure.

Safeguarding data, adhering to regulations, and applying the ACT framework are essential for responsible AI use. However, they focus on your responsibilities and don’t account for potential limitations of the technology.

A key component of responsible use is being able to effectively judge the output of an AI tool, but to do that, it helps to first understand what factors might contribute to inaccuracies. Specifically, it’s important to consider where the model might be biased, where its knowledge ends, and how its performance can change over time.

Data bias can be a foundational challenge for AI. This can happen when the data used to train an AI model is skewed, incomplete, or reflects historical or societal biases. Because the model’s output is directed by its training data, it can sometimes reproduce existing biases in its response, like connecting specific activities with certain age groups. For example, if the training data contains only examples of children skateboarding, the model may incorrectly extrapolate that adults never skateboard.

Part of following a responsible AI approach is being aware that AI models may create stereotypes or bias in their outputs. For example, if you ask AI to generate an image of an office, it might consistently produce an image of a high-rise building. This may occur because its training data comes from urban business hubs. As a result, it may struggle to generate images of creative workspaces, home offices, or rural business settings. Your role as a responsible user is to guide the model toward a fair and impartial output.

To avoid unfair bias in outputs, you can use these techniques in your prompts:

• Be specific about the output you want: Add important context about your intended audience and their needs. You can also provide fair and balanced references for the model to follow.
  
• Use follow-up prompts to correct outputs that seem biased or inaccurate: If AI provides a biased response, point out the stereotype when you iterate on your prompt and ask the model to correct the bias. 

Knowledge cutoff is the point in time when a model’s training data ends. This means the model lacks information on events, discoveries, or data that occurred after that date.

While some models can provide information about very recent events, they may do this by performing a live web search to supplement their answers. It's helpful to think of this as the difference between what the model knows from its training versus what it can look up in the moment. The model's core knowledge is not continuously updated, which is why the concept of a knowledge cutoff remains a critical limitation to bear in mind.

Responsible AI use requires you to verify time-sensitive information. Always use a search engine or other reliable sources to fact-check statistics, news, or any information about recent events.

To work effectively with a model's knowledge cutoff, you can use these techniques:

• Looking up the cutoff: You can search online for the knowledge cutoff date of a specific AI model. This helps you understand the boundary of its internal knowledge.
  
• Verifying time-sensitive information: For any statistics, breaking news, or details about recent events, always cross-reference the AI's answer with a reliable external source, like a search engine or an official report.

• Specifying your timeframe: When asking about a topic that changes over time, state the timeframe for what you need. For example, instead of "What was the biggest song of the summer?" ask “What was the biggest song of the summer in 2025?”

Refining with follow-up prompts: If an answer seems outdated (like mentioning a "new" product that is several years old), use a follow-up prompt to ask for more recent alternatives or clarification.

Drift is the gradual decline in a model’s accuracy and relevance as the real world changes. You might observe drift in two ways:

• Factual drift: This is when AI becomes less accurate over time because of its knowledge cutoff. For example, an AI model's advice on current fashion trends may become less useful the further you get from its training date.
  

• Behavioral drift: This refers to changes in AI’s behavior over time. As developers update models, the formatting, tone, or conversational styles of an AI model may change, even when you use the same prompts.
  

Here are a few ways to manage and mitigate both kinds of drift:

• Provide accurate and up-to-date context in your prompts, especially for topics that change quickly, like market trends or technology.
  

Keep chats focused by starting a new conversation for each specific task. This also helps to reset the context window if a conversation becomes too long or the output starts to feel off topic.

• Be explicit with clear and specific instructions in your prompts.

As AI models become standard in our daily workflows, your professional judgement is key to using them safely and responsibly. This requires a keen awareness of the technology’s limitations, from potential bias to factual inaccuracies. Responsible AI use helps you turn AI assistants into reliable, professional assets.

This article is a curated excerpt from the Google AI Professional Certificate. This certificate is your path to AI fluency, built by Google experts. You’ll move beyond the basics with hands-on practice, gaining the in-demand skills and confidence to apply AI to your job from day one.

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