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Assess the specific AI tool for the job and data you plan to give it—not the company’s reputation or a badge in isolation. Look for relevant performance evidence, clear data-use terms, security and incident practices, accountable people, and a way to report or appeal problems. Trustworthiness is contextual: a product may be suitable for a low-stakes task and unsuitable for a decision that could seriously affect someone.
How do I know if an AI company is trustworthy?
Start by defining the task, the people who will use the tool, the information it will process, who may be affected by its output, and what happens if it is wrong. Consider whether AI is appropriate at all; a simpler non-AI option may carry less risk. The National Institute of Standards and Technology (NIST) describes trustworthy AI in terms that include validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness. Their relative importance depends on the system’s context, and strength in one area does not establish trustworthiness overall. NIST: AI Risks and Trustworthiness
Use a consistent set of questions for each candidate. The comparison below is useful for a personal tool or a workplace procurement review; increase scrutiny when data is sensitive or a wrong output could cause meaningful harm.
| Area | What to establish |
|---|---|
| Evidence | How performance was tested, whether conditions resemble your use, known limitations, and monitoring after release. |
| Data | What is collected, who can access it, whether it is shared or used to improve models, and how long it is retained. |
| Security | How access is controlled, vulnerabilities and incidents are handled, and service behavior can be monitored or disabled. |
| Accountability | Who owns risk decisions, how users report problems, and whether review or appeal is possible. |
| Fit and impact | Whether the tool suits the task and whether the likely benefits justify the risks compared with alternatives. |
These factors can conflict. For example, greater interpretability may come at the expense of some accuracy, and privacy choices can affect explainability. Ask how the provider or deployer has handled trade-offs for your use and whether the reasoning is documented. NIST: AI Risks and Trustworthiness
#1 Best Overall
Does the tool perform well for my task?
Ask what the provider means by words such as “accurate,” “safe,” or “reliable.” A score is useful only when you understand how it was produced and whether the evaluation resembles your conditions. NIST recommends representative, defined test sets and documented methods; results may also need to be broken down by relevant groups or conditions. A benchmark result alone does not predict performance on your own task.
- What was tested, and were the test examples representative of the inputs and situations you expect?
- What metrics and evaluation methods were used? Were results independently assessed or reported only by the provider?
- What are the documented limitations, including situations outside the conditions in which the system was developed?
- How does the provider monitor performance after release, and what happens when it degrades or changes?
- Where outputs could affect people, are relevant subgroup results and safeguards available?
NIST’s AI Risk Management Framework calls for documented test sets, metrics and tools, evaluations under conditions similar to deployment, and ongoing production monitoring. Ask for evidence tied to the exact product and configuration you would use, rather than a general claim about a company’s AI. NIST: Measure
Is it safe to put my data into this AI tool?
Read the current privacy notice, terms of service, product settings, and—if applicable—enterprise or API agreement for the exact tool and account type. Establish what happens to prompts, uploaded files, connected-app data, telemetry, and feedback. Terms may differ by product, plan, geography, or agreement, so do not assume that a policy for one version applies to another.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Collection: What information is collected through prompts, files, integrations, usage data, or feedback?
- Access and sharing: Can employees or contractors review inputs and outputs? Are they shared with service providers or other parties?
- Model improvement: Are inputs used to train, fine-tune, or otherwise improve models? Does the answer depend on settings, product tier, or contract?
- Retention and deletion: How long is information kept, what steps delete it, and are there backup or abuse-monitoring exceptions?
- Location and exit: Where is data processed, and what happens to it when you close an account or stop using the service?
Do not upload confidential or sensitive information until the applicable terms and security protections are clear and acceptable. The U.S. Federal Trade Commission (FTC) says companies must honor privacy and confidentiality commitments wherever they make them—including terms, privacy materials, promotions, or marketplaces—and that material omissions can matter. Its advice is U.S. guidance, not a complete account of laws in every country or sector. FTC: AI Companies: Uphold Your Privacy and Confidentiality Commitments FTC: Privacy and Security
What should I check about security and failures?
Ask for specifics rather than accepting “secure” as a conclusion. Find out who can access inputs and outputs, how access is controlled, what security evidence is available, how vulnerabilities and incidents are managed, and how you can monitor or disable the tool if its behavior changes. For model-based products, ask how the provider addresses prompt injection or other adversarial inputs, data poisoning, and attempts to extract data or intellectual property through system endpoints. NIST identifies these as security concerns and describes resilience as the ability to withstand adverse events or changes, recover, or degrade safely. NIST: AI Risks and Trustworthiness
Match the depth of evidence to the sensitivity of your data and the possible harm from a failure. Security documentation is evidence to evaluate, not proof that every product configuration or deployment is safe. For high-impact uses, check relevant local and sector-specific requirements as well as general vendor materials.
Who is accountable when the AI gets something wrong?
Look for clear responsibility, ongoing oversight, and a usable route to report a problem. A provider or deployer should explain the tool’s intended use and limitations at a level you can act on. Ask who monitors the system, who decides what risks are acceptable, what users should do after an error, and whether a human review or appeal is available when outcomes affect people.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →NIST’s AI Risk Management Framework calls for documented roles, risk tracking, ongoing monitoring, feedback, appeals, and response planning. Transparency matters when it helps users understand an output and obtain redress—not merely when a company publishes a general statement. NIST: Manage NIST: AI Risks and Trustworthiness
Best Value
Does a SOC 2 report or AI framework claim prove an AI tool is safe?
No single framework reference, certification, security report, benchmark, or policy settles whether a tool is suitable for your use. Ask what product, deployment, version, controls, and exceptions a claim covers, and request the underlying evidence. A report may be relevant to one part of your assessment, but it cannot by itself establish performance, privacy, fairness, or fit for your task.
NIST’s AI Risk Management Framework is voluntary guidance, not a blanket approval. NIST says version 1.0, released January 26, 2023, is being revised; its current framework page also lists a Generative AI Profile issued July 26, 2024. A provider’s statement that it “follows” a framework does not show what it assessed or whether the result fits your circumstances. NIST: AI Risk Management Framework
How to compare providers before you choose
Compare candidates using the same task, data sensitivity, and deployment conditions. Record the evidence and unresolved questions rather than reducing the choice to a single trust score.
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- Define the use: Write down the task, users, affected people, data involved, and consequences of a wrong result. Decide what level of human oversight is needed.
- Check performance evidence: Request relevant evaluation methods, conditions, limitations, and post-launch monitoring details.
- Trace the data: Identify collection, access, sharing, model-improvement use, retention, deletion, and contractual commitments for your exact plan.
- Review security and recovery: Establish access controls, incident handling, resilience measures, and a way to disable or limit use if needed.
- Confirm accountability: Identify responsible people or teams, feedback channels, and any review or appeal path.
- Decide on fit: Weigh the remaining risks and trade-offs against benefits and simpler alternatives. If essential answers are missing, treat that as an unresolved risk rather than assuming the best.
The FTC’s statement that “There is no AI exemption from the laws on the books” is a reminder that using AI does not erase existing privacy and confidentiality commitments. The details still depend on the jurisdiction and the particular use. FTC: AI Companies: Uphold Your Privacy and Confidentiality Commitments
Quick Recap
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