Use AI Responsibly
Responsible use is a core component of AI literacy, not an advanced topic. Workers must understand the boundaries of appropriate use: recognizing the limits of AI authority, protecting sensitive data, complying with workplace or legal requirements, and maintaining accountability for outcomes.
"Use AI Responsibly" is the fifth foundational content area of the DOL AI Literacy Framework (TEN 07-25). It covers protecting sensitive information, following workplace AI policies, avoiding misuse and harm, managing context-specific risk, and maintaining accountability, and the framework treats it as core literacy, not an advanced topic.
Last verified against the DOL source: August 20, 2026. What changed
Score your program on these five checkpointsWhat the framework says
Paraphrased: as AI embeds in daily workflows, workers must understand "the boundaries of appropriate use", safeguarding information, applying outputs ethically, "recognizing the limits of AI authority, protecting sensitive data, complying with workplace or legal requirements, and maintaining accountability for outcomes."
The five example content areas:
- 5.1
Protecting sensitive information
Workers should understand what types of data should not be entered into AI tools and how to prevent accidental disclosure of confidential information.
- 5.2
Following workplace policies and rules
Users must be aware of and follow any organizational policies around AI use, including guidance related to specific tools or contexts.
- 5.3
Avoiding misuse or harm
Workers should be aware of how AI tools can be used inappropriately, whether for plagiarism, impersonation, or harm, and know how to report issues.
- 5.4
Managing context-specific risks
Workers should understand how risk varies across different tasks, audiences, or sectors and apply greater scrutiny or caution in higher-stakes settings.
- 5.5
Maintaining accountability
Workers remain responsible for the decisions and outputs they produce with AI tools and should avoid treating AI responses as final or authoritative without review.
Reproduced from the public framework text under 17 U.S.C. section 105. Read the source on dol.gov.
What it means in practice
This area converts literacy into conduct. Its load-bearing insight is the accountability marker: in the framework's architecture, responsibility never transfers to the tool. A worker who pastes confidential client data into a public chatbot has failed marker 1; a worker who ships unreviewed AI output has failed marker 5, and both failures land on the employer, which is why this area is the one procurement committees and counsel care about most. For regulated and education contexts (student data, patient data, personnel records), marker 1's "what should not be entered" lesson needs organization-specific specifics layered on the framework's baseline.
How programs fail this provision
Policy-instead-of-training: the organization publishes an AI policy PDF, declares Area 5 covered, and never trains the behaviors the policy requires. The framework demands instruction and practice, recognizing misuse, applying graduated scrutiny by stakes, reporting issues, not just document distribution. Second failure: teaching responsible use as a scary compliance module detached from the hands-on work, instead of embedding it into every lab (where it belongs). Inspection question: can learners state what data is prohibited from AI tools in your organization, from memory?
The five checkpoints this provision scores on
Learners are taught what types of data must never be entered into AI tools, and how to prevent accidental disclosure of confidential information.
Framework marker: Protecting sensitive information
Learners are trained on the organization's AI-use policies, including tool- or context-specific rules.
Framework marker: Following workplace policies and rules
Learners are taught to recognize misuse, plagiarism, impersonation, harm, and how to report issues.
Framework marker: Avoiding misuse or harm
Learners are taught that risk varies by task, audience, and sector, and to apply greater scrutiny in higher-stakes settings.
Framework marker: Managing context-specific risks
Learners are taught that they remain accountable for decisions and outputs produced with AI, and that AI responses are never final without review.
Framework marker: Maintaining accountability
How GAGE addresses it
In AI Literacy and Professional Conduct, this provision sits in Ethical and Responsible AI and Operational Governance and AI Security Fundamentals. Every topic in the program ends in a mastery assessment, and the teach-back gate asks the learner to explain the material back in their own words before it counts as understood, which is the difference between a program that covers a checkpoint and one that can evidence it.
Module titles come from the program registry, so this cannot drift away from what the program contains. GAGE is a private company: the program is built to the DOL framework, and DOL neither certifies nor endorses it or any other program.
Questions about this provision
Does the framework make responsible use a legal requirement?
No, the framework is voluntary guidance. But it instructs programs to teach compliance with "workplace or legal requirements" that independently bind the organization (privacy law, sector rules, internal policy).
Why does DOL call responsible use "core" rather than advanced?
Because a worker who can prompt and evaluate but leaks sensitive data or disowns accountability is a liability, not a literate worker. The framework sequences conduct as part of the baseline.
Does your program actually meet this provision?
Answer the five checkpoints above and the other 55, and get a dated report that scores your program provision by provision. Free, and your answers stay in your browser.
What this means for hr & l&d leaders
TEN 07-25 for HR & L&D: The Federal Reference for Corporate AI Literacy Training
GAGE (Global Academy of Generative-AI Education) is a private education company, not affiliated with or endorsed by the U.S. Department of Labor. DOL does not certify or endorse training programs. Framework summaries are drawn from the public TEN 07-25 document. Read TEN 07-25 on dol.gov. Last verified: August 20, 2026.
Last verified against the DOL source: August 20, 2026. What changed