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AI risks include privacy exposure, biased or uneven outcomes, convincing but inaccurate content, and safety or security failures. They do not affect every AI system in the same way: the risk depends on the system, its data, how it is deployed and used, and what happens if it is wrong.
Why AI risk depends on context
“AI” covers many kinds of systems, from tools that classify or rank information to generative AI that creates text, images, audio, or video. A concern associated with one type of system should not automatically be treated as a property of all AI. For example, fabricated images are a generative-AI concern; an automated ranking system may create harm in other ways.
Risk also depends on the task and consequences. An incorrect suggestion in a low-stakes setting is different from an error that affects access to a service or another consequential decision. The relevant questions are who relies on the system, who may be affected, and whether someone can detect and address a mistake.
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Privacy: data can be exposed or used in unexpected ways
Privacy concerns can arise when AI systems collect or process personal or sensitive information, when information is shared during a human-AI interaction, or when a system’s output reveals information. A model trained on large datasets may capture and reproduce private or sensitive material, according to the OECD; NIST also identifies data leakage as an AI-related privacy and cybersecurity concern. This does not mean every model memorizes or exposes personal information.
Privacy review should cover more than the training data. Consider what information a person enters, what the service does with it, who can access it, and whether generated outputs could reveal sensitive material. Provider-specific data retention or training practices vary and should be checked in the relevant service’s current documentation rather than assumed.
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- As a precaution, do not submit confidential or sensitive information unless you have checked the service’s data practices and have authority to share it.
- For workplace use, follow the organization’s rules for handling personal, customer, employee, and proprietary data.
- Do not assume that a setting or opt-out eliminates every privacy or security risk.
Bias and unequal performance
Bias can enter through training data, design choices, evaluation methods, and deployment. A system may reflect stereotypes in its outputs, or perform differently across groups, languages, or dialects. If an organization uses such outputs to inform decisions at scale, disparities can contribute to unequal or harmful outcomes.
NIST’s Generative AI Profile notes that generative AI can increase the speed and scale at which harmful biases manifest. That makes evaluation in the actual use context important: performance should be examined for the groups and languages affected, not inferred from a single overall result. An unequal result is a reason to investigate and mitigate a problem; whether conduct is unlawful discrimination depends on the jurisdiction and case-specific evidence.
Misinformation and convincing errors
Generative AI can produce fluent answers that are factually wrong. The OECD describes such errors as hallucinations. Generative systems can also create fabricated images, audio, or video that look or sound realistic. When people share or rely on these outputs as if they were true, they can mislead audiences and damage information integrity.
An inaccurate output does not by itself show that someone intended to deceive. Misinformation can spread without deliberate intent; disinformation involves purposeful deception. Nor is all synthetic content false, or all misinformation generated by AI. The practical response is to verify consequential factual claims against reliable sources and treat realistic media as something that may need independent authentication.
Safety and security failures
Safety concerns involve harm caused by system failure, inappropriate use, or an output that is unsuitable for its context. Security concerns include attacks, compromise, and misuse. The two can overlap, but they are not interchangeable: a system may be insecure without a harmful outcome occurring, while a safety failure need not involve an attack.
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The OECD AI Principles call for systems to be robust, secure, and safe across their lifecycle, including under normal and foreseeable use or misuse and adverse conditions. They also support the ability to override, repair, or safely decommission a system where appropriate. Human oversight and intervention are parts of risk management, not a guarantee that harm will be prevented.
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Wider and emerging concerns
The OECD also discusses overreliance on AI, proliferation of synthetic content, concentration of AI resources, and possible longer-term systemic risks. These concerns differ in how established they are: some are present-day issues, while some future scenarios remain uncertain. They should be treated as possibilities under discussion, not predictions that a particular outcome will occur.
How to assess an AI tool or deployment
There is no single score in the cited frameworks that predicts whether an AI system will harm a particular person. When comparing tools or deciding whether to use a system, assess the factors that matter for the specific task:
- Data: What information does the system process, and what are the relevant retention and access practices?
- Accuracy and fairness: How well does it perform for the groups, languages, and conditions involved?
- Transparency: Are the system’s limitations and intended use clear to the people relying on it?
- Misuse exposure: How could outputs or system access be abused?
- Human intervention: Can a person review, correct, override, or stop the system?
- Impact of error: What could happen if an output is wrong, incomplete, or misunderstood?
These are practical comparison questions drawn from NIST’s trustworthiness framing and OECD principles, not a standardized ranking or official checklist.
How organizations can organize risk management
NIST AI RMF 1.0 groups voluntary risk-management work into four functions: Govern, Map, Measure, and Manage. In broad terms, these organize oversight and responsibility, understanding of the use context, evaluation of risks, and action to address them. NIST says the framework is being revised; its status may change, so organizations should check the current NIST overview before relying on a particular version. NIST’s Generative AI Profile, published July 26, 2024 and updated April 8, 2026, is a companion resource to AI RMF 1.0 for generative AI.
The OECD AI Principles, adopted in 2019 and updated in 2024, provide high-level guidance on human rights, fairness, privacy, information integrity, human agency, oversight, and safety. Neither these principles nor the NIST framework replaces applicable law, a system-specific assessment, or safeguards suited to the people and setting involved.
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