Individuals can take sensible steps to protect their information, avoid spreading harmful AI-generated claims, and report concrete concerns. Those steps may help reduce harm in particular situations, but they cannot control model capabilities, system security, or how organizations deploy AI. The most consequential safeguards remain the responsibility of developers, providers, employers, institutions, and public authorities.
What counts as AI misuse?
AI misuse is broader than fake images or misleading text. NIST’s final Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, announced March 24, 2025, includes evasion, poisoning, privacy, and misuse attacks involving generative AI. These categories describe different ways AI systems or their outputs can be targeted or abused; no single user habit addresses all of them.
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NIST describes that report as voluntary guidance for identifying, addressing, and managing adversarial machine-learning risks. Its taxonomy is a reminder that risks can arise at different points in a system’s development and use, not only when an individual encounters an obviously false output.
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Practical steps an individual can take
Protect information you submit
Before entering information into an AI service, consider whether it is confidential, identifying, or sensitive. Avoid submitting it unless you have a legitimate reason and understand how the service handles the data. Check the service’s applicable privacy information and any rules set by your employer, school, or other organization. This is a privacy-preserving precaution; it should not be treated as a way to prevent broader AI misuse.
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Verify consequential claims before acting or sharing
If an AI-generated claim could affect your health, finances, safety, reputation, or civic decisions, check it against independent, trusted sources. Look for original documents or authoritative organizations where possible, and distinguish a claim that is supported by evidence from one that is merely repeated. UNESCO’s guidance on media and information literacy supports building these habits, but does not quantify how much any one person’s fact-checking changes misuse rates.
Pause before amplifying suspected harmful material
Do not forward an unverified claim or synthetic image simply because it is alarming, persuasive, or widely shared. If there is a concrete concern—such as a suspected privacy violation, impersonation, or dangerous falsehood—use the relevant platform, employer, school, or authority reporting route. Avoid reposting the material to call attention to it if doing so could further expose a person or spread the claim.
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Build AI and media literacy without treating it as a detector
Learn enough about AI to ask what a system can and cannot establish, where an answer came from, and what evidence would confirm it. UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence calls for public understanding of AI and data through education, civic engagement, digital skills, and media and information literacy. Literacy can support better judgment, but it cannot reliably identify every synthetic item or stop malicious actors.
Ask organizations about the controls they own
When an employer, school, or service provider uses AI in a consequential setting, ask how it handles privacy, human oversight, system evaluation, and incident response. NIST’s AI Risk Management Framework and its January 15, 2025 announcement of the second public draft of AI 800-1 frame risk identification, measurement, and mitigation as work across the AI lifecycle and supply chain. The January 2025 announcement described that draft as voluntary guidance; it does not establish the publication status of a later final version.
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How these actions differ—and where their reach ends
| Action | What it primarily protects | Who can take it | Reach and limit |
|---|---|---|---|
| Limit sensitive information submitted to AI services | Your own privacy | Individual user, alongside the service’s data practices | A precaution for information you choose to disclose; it does not address every privacy risk or prevent broader misuse. UNESCO’s 2021 Recommendation supports privacy protection across the AI lifecycle. |
| Check consequential claims with independent sources | Your information quality and decisions | Individual user | Can inform whether you act on or share a claim; no quantified population-level effect is established in UNESCO’s literacy guidance. |
| Pause sharing and report concrete concerns | Potentially affected people and the response to a specific incident | Individual user, service, institution, or relevant authority | Can bring a concern to an appropriate channel. CISA’s January 14, 2025 JCDC playbook describes voluntary sharing of AI cybersecurity incident and vulnerability information among partner organizations, not a universal public reporting channel. |
| Evaluate, secure, and govern AI systems | System users and the wider public | Developers, providers, deployers, organizations, and public authorities | Requires institutional capabilities and lifecycle controls; an individual cannot operate these controls alone. NIST’s risk-management materials address this organizational responsibility. |
What individuals cannot reliably do alone
Careful prompting, refusing one service, fact-checking, or reporting a concern cannot be assumed to meaningfully reduce aggregate AI misuse on its own. NIST’s taxonomy covers multiple attack classes, while its risk-management guidance focuses on organizations’ ability to evaluate and mitigate risks across a system’s lifecycle. The cited guidance does not rank personal actions by effectiveness or provide an individual-level estimate of how much any one action lowers misuse.
That limit is not a reason to do nothing. It is a reason to treat personal precautions as bounded harm reduction rather than a substitute for provider security, responsible deployment, institutional oversight, or public policy.
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Why collective and institutional action matters
AI risks often depend on decisions an ordinary user does not control: how a model is built and evaluated, what protections surround a service, who is allowed to use it, and how incidents are handled. NIST’s January 2025 AI 800-1 draft announcement describes risk-management practices across lifecycle stages and actors in the AI supply chain. UNESCO’s Recommendation addresses privacy, safety, security, public understanding, and education. Together, these sources place many consequential controls with the organizations that create, deploy, and govern AI.
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A realistic way to think about personal impact
Use personal actions to reduce avoidable exposure and harm in the situations you can influence: what you disclose, what you rely on, what you amplify, and where you report a concrete concern. Treat system-wide prevention as a responsibility shared by the organizations and authorities with control over AI design, deployment, security, evaluation, and oversight. No percentage or ranking of individual actions’ effectiveness is established by the cited guidance.
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