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You can reduce some risks to yourself and help limit the spread of harmful AI-generated material: protect sensitive information, verify consequential claims, avoid amplifying suspected abuse, and report concrete concerns through the right channels. These are sensible precautions, not a way for one person to control AI misuse at scale. Major safeguards—such as system security, testing, oversight, and incident response—depend largely on the organizations that build and use AI.
What does AI misuse include?
AI misuse is broader than fabricated images or misleading text. NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, published as AI 100-2 E2025 on March 24, 2025, describes attack classes that include evasion, poisoning, privacy attacks, and misuse attacks for generative AI. Different attacks target different parts of a system and can have different goals, so no single personal habit can address them all.
NIST describes the taxonomy as voluntary guidance for identifying, addressing, and managing adversarial machine-learning risks. Its scope is technical and spans threats that ordinary users cannot directly prevent or evaluate on their own.
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What can I do as an ordinary person?
Focus on choices within your control. The actions below are grounded in privacy, literacy, and information-sharing guidance, but the cited sources do not quantify how much any one person’s behavior reduces AI misuse overall.
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| Action | What it can help protect | Who controls it | Evidence and limits |
|---|---|---|---|
| Be careful with sensitive information | Your own privacy | You, when deciding what to submit; the service, in how it handles data | UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence calls for privacy protection throughout the AI lifecycle. This is privacy-preserving practice, not established prevention of broader model misuse. |
| Check consequential claims before acting or sharing | Your decisions and the information you pass on | You, using independent trusted sources | UNESCO supports media and information literacy and access to trusted sources in its work on misinformation and disinformation. The guidance does not measure the effect of an individual fact-check. |
| Build AI and media literacy | Your ability to understand uncertainty and assess information | You, educators, and public institutions | UNESCO calls for public understanding of AI through education, civic engagement, digital skills, AI ethics training, and media and information literacy. Literacy cannot reliably identify every synthetic item or stop a malicious actor. |
| Do not amplify suspected harmful material; report concrete concerns | People who might otherwise receive the material, and organizations investigating an incident | You can pause or report; services and institutions handle their own response | This is a practical application of literacy and collective-response guidance. CISA’s 2025 playbook concerns voluntary sharing among partner organizations; the sources do not establish a universal public reporting channel or quantify an individual report’s effect. |
| Ask organizations using AI about their safeguards | Oversight and accountability in the organization’s use of AI | The organization controls its systems and practices; individuals can ask questions or participate in governance | NIST’s risk-management materials place identification, measurement, and mitigation across organizational AI lifecycle roles. Asking questions does not operate those controls. |
Protect information before entering it into an AI service
Do not submit confidential, identifying, or sensitive material unless you understand how the service handles it and have a legitimate reason to share it. Consider whether the task can be done without the sensitive details. UNESCO’s privacy principle applies across the AI lifecycle; it does not mean that withholding your information prevents misuse of a model by others.
Verify before relying on or forwarding important claims
If AI-generated information could affect health, finances, safety, someone’s reputation, or a civic decision, check it against independent, trusted sources before acting or sharing it. A confident tone or realistic-looking image is not, by itself, proof that the content is accurate or authentic. If you cannot verify a claim, avoid presenting it as established fact.
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Pause before sharing material you suspect is harmful
Avoid forwarding unverified AI-generated claims or media just to ask others whether they are real; doing so can spread the material further. If there is a concrete concern—such as a suspected scam, impersonation, privacy violation, or security incident—use the relevant service’s reporting route or the process set by your employer, school, or another affected institution. The appropriate channel depends on the incident; the cited sources do not identify one universal public route.
Learn what AI can and cannot establish
Use accessible AI and media-literacy education to understand that generated content can be uncertain, misleading, or manipulated. UNESCO’s Recommendation, adopted in November 2021, supports public understanding through education, civic engagement, digital skills, and media and information literacy. Treat literacy as a way to make more informed judgments, not as a reliable detector for every synthetic image, voice, or text.
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How much impact can individual action have?
Individuals have bounded influence. Personal precautions can help protect one person’s information and improve the quality of what they rely on or share. Reporting may contribute information to a service or institution’s response. But the available guidance does not provide a causal estimate for the effect of any one action, rank these actions by effectiveness, or establish that individual habits reduce misuse at population scale.
That limit follows from the breadth of the problem. NIST’s taxonomy covers multiple kinds of attacks, while its risk-management guidance addresses controls across an AI system’s lifecycle. Refusing one service, fact-checking a post, or reporting an incident cannot independently secure a model, prevent all attacks, or substitute for institutional safeguards.
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Who is responsible for the larger safeguards?
Developers, providers, deployers, and other organizations involved in AI have responsibilities ordinary users do not control: securing systems, evaluating risks, monitoring use, setting appropriate oversight, and responding to incidents. NIST AI 800-1’s January 15, 2025 announcement described a second public draft of voluntary practices for managing generative-AI risks across the lifecycle, including a marginal-risk framework and practices for multiple actors in the AI supply chain. That announcement establishes the document’s status at that date; it does not establish whether a final version has since appeared.
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Where an organization uses AI in a consequential setting, you can ask how it handles privacy, oversight, evaluation, and incident response, or participate in relevant public or institutional discussions. Those actions can support accountability, but the organization remains responsible for operating its controls.
Quick Recap
A practical way to keep your role in proportion
- Protect information you control, and check consequential claims before relying on or sharing them.
- Use literacy to recognize uncertainty, not to assume you can identify every misuse.
- Report concrete incidents through the appropriate service or institutional channel rather than expecting one report to solve a systemic problem.
- Expect providers and organizations to manage the technical and operational risks that individual users cannot control.
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