How to Evaluate an AI Company’s Safety Monitoring

This guide explains how to assess whether an AI company has robust safety monitoring practices in place. You’ll learn what to look for in their documentation, technical controls, and organizational processes, so you can make an informed decision as a customer, investor, or partner. Before you begin, gather any public materials the company has published, such as safety policies, model cards, system cards, and incident reports.

What You’ll Need

• Access to the company’s public safety documentation (website, blog, white papers)
• Any model or system cards they have released
• Their incident disclosure or transparency reports, if available
• A basic understanding of AI terminology (model, training, deployment, etc.)
• Time to review and cross-reference documents

Step-by-Step

1. Start with their public safety policy. Look for a dedicated page or document that outlines their commitment to AI safety. A serious company will have a clear, written policy that names specific risks (e.g., bias, harmful outputs, misuse) and how they plan to mitigate them. If you can’t find any policy, that’s a red flag.
2. Check for model cards or system cards. For each major model, they should publish a card that describes the model’s intended use, training data, evaluation results, and known limitations. Scrutinize whether the card includes safety-specific evaluations, such as tests for harmful content, bias, or jailbreak resistance. A good card will be detailed and honest about weaknesses.
3. Look for a red-teaming or adversarial testing program. The company should mention that they actively try to break their own systems. Look for terms like “red team,” “adversarial testing,” “penetration testing,” or “jailbreak testing.” They should describe how they recruit testers (internal, external, diverse backgrounds) and how often they run these tests.
4. Review their incident reporting and response process. Do they have a public vulnerability disclosure program? How do they handle reports of misuse or safety failures? A good practice is to have a clear email or form for reporting issues, a stated timeline for response, and a policy for fixing and disclosing problems. Check if they’ve ever published an incident report—if they have, read it to see how transparent they were.
5. Assess monitoring during deployment. Find out if they monitor their models in production. They should have systems that track for unusual activity, such as spikes in harmful outputs or attempts to exploit the model. Look for mentions of “automated monitoring,” “anomaly detection,” or “human review” of flagged outputs. The more layers, the better.
6. Evaluate their governance structure. Who is responsible for safety decisions? Look for a dedicated safety team, a board-level AI safety committee, or an ethics board. The company should have clear lines of accountability. If safety is just a side project of a few engineers, that’s a warning sign.
7. Check for third-party audits. The most credible safety monitoring includes independent verification. Look for mentions of external audits, certifications (like ISO/IEC 42001), or partnerships with academic institutions for safety research. If they only self-assess, ask why.
8. Test their user-facing reporting mechanisms. If you use their product, try to find the “report a problem” feature. Is it easy to find? Do they ask for details? A well-designed reporting flow indicates they take feedback seriously. You can even submit a test report (non-malicious) to see how they respond.
9. Look for transparency in their training data and filtering. While they may not share proprietary data, they should discuss how they filter training data to remove harmful content and how they handle biases. If they are silent on this, they may not have a robust process.
10. Compare their practices to industry standards. Look at what leaders in the field (e.g., companies that have published detailed safety cards) are doing. If the company you’re evaluating is far behind that baseline, that’s a concern. You can also refer to frameworks like the NIST AI Risk Management Framework or the EU AI Act’s requirements as benchmarks.

Common Mistakes

• Taking marketing language at face value. Words like “responsible AI” or “safety-first” are meaningless without concrete details. Look for specifics: what tests, what metrics, what thresholds.
• Ignoring the difference between pre-deployment and post-deployment monitoring. A company may test extensively before launch but have no ongoing monitoring. Both are necessary.
• Overlooking the human element. Safety monitoring isn’t just about automated systems. Check if they have human reviewers, a process for escalating issues, and a culture that encourages reporting mistakes.
• Assuming more transparency is always better. Some details (like specific security vulnerabilities) shouldn’t be public. But a company should explain why they are withholding certain information.
• Not checking for updates. Safety practices evolve. A company that published a safety card a year ago but hasn’t updated it since may be falling behind.

Tips

• Look for concrete numbers. For example, “we ran 10,000 adversarial prompts” is more meaningful than “we test for safety.”
• Check if they publish safety metrics over time. Do they share trends in harmful outputs or false positives? That shows they measure and improve.
• Look for a bug bounty program. This indicates they welcome external scrutiny and are willing to pay for findings.
• See if they have a responsible disclosure policy that protects researchers. If they threaten legal action for good-faith testing, that’s a red flag.
• Talk to the company directly. If you’re a serious stakeholder, schedule a call and ask specific questions about their monitoring. Their willingness to answer is informative.

Troubleshooting

• If you can’t find any safety documentation: This is a major red flag. Try searching their website for terms like “safety,” “security,” “responsible AI,” or “trust.” If still nothing, contact them directly and ask. If they don’t provide anything, assume they have no formal safety monitoring.
• If the documentation is vague or generic: Ask for specifics. For example, if they say they “monitor for harmful content,” ask what tools they use, how often, and what triggers a response. If they can’t answer, they may be overstating their practices.
• If they have had a public incident: Look for how they responded. Did they acknowledge it quickly? Did they provide a detailed post-mortem? Did they implement changes? A company that handles incidents well is more trustworthy than one that has never had one (which is unlikely).
• If you’re evaluating a smaller company: They may not have the resources of big tech. That’s okay, but they should still have basic practices in place. Look for a simple but clear safety policy and at least one person responsible for safety.

Frequently Asked Questions

• What is the most important thing to check? The most important is whether they have a concrete, documented process for identifying and mitigating risks before and after deployment. A policy is good, but evidence of execution is better.
• Can I trust a company that doesn’t publish safety details? Not necessarily. Some details may be proprietary, but a company should be able to explain their approach at a high level. Total silence is a bad sign.
• What are red flags in safety monitoring? Red flags include: no public safety policy, no model cards, no incident reporting mechanism, no mention of red-teaming, and no one accountable for safety.
• Should I expect them to share internal monitoring logs? No, that’s usually proprietary. But they should share summaries or metrics that show they are monitoring and improving.
• How often should they update their safety documentation? At least once a year, or whenever they release a major model update. If they haven’t updated in over a year, ask why.
• Is this evaluation different for open-source vs. closed-source companies? Yes. Open-source companies may rely on community audits, while closed-source companies need to demonstrate their own rigorous testing. Both should have clear monitoring for deployed models.

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