OpenAI and Anthropic Safety Disclosures Explained
OpenAI and Anthropic safety disclosures show how the companies describe their efforts to identify and reduce risks from advanced AI, but they do not prove that their systems are safe. For builders, the main lesson is to treat safety as an ongoing, testable process—with clear thresholds, independent scrutiny, and plans for responding when safeguards fail.
What the disclosures are
OpenAI and Anthropic publish information about safety policies, evaluations, model releases, and governance. Their disclosures can include how they test models, what kinds of capabilities or misuse they worry about, and what actions they say they will take if a system crosses a risk threshold. The details and terminology differ, and documents may change as company policies evolve.
These are not the same as a regulator’s certification or a guarantee that an AI product will behave safely in every setting. They are company statements about methods and commitments. Their value depends on how specific, measurable, current, and independently checkable those statements are.
It is useful to distinguish three kinds of information: a policy says what an organization intends to do; an evaluation reports what a particular test found; and an external audit or regulator’s finding offers scrutiny from outside the organization. A disclosure can contain the first two without providing the third.
Why OpenAI and Anthropic use safety frameworks
Both companies have described structured approaches to managing risks as models become more capable. OpenAI has published a Preparedness Framework focused on tracking and preparing for severe risks, including risks associated with biological or chemical capabilities, cybersecurity, and AI self-improvement. The framework describes evaluations and risk thresholds intended to inform safeguards and deployment decisions.
Anthropic has published a Responsible Scaling Policy, which sets out how it plans to assess model capabilities and apply safeguards as risk increases. Its approach has included capability thresholds and requirements for stronger security and safety measures at higher levels. These frameworks are not identical: they use their own categories, evaluation methods, and decision processes, so a threshold in one should not be assumed to match a threshold in the other.
The shared idea is staged responsibility. Before and during a release, developers assess what a model can do, consider plausible harms, and decide whether safeguards are adequate. If testing indicates elevated risk, the company’s stated framework may call for more evaluation, stronger controls, restricted access, or a pause in deployment. The precise response depends on the framework and the evidence.
What safety testing can—and cannot—show
Evaluations try to measure particular abilities or failure modes. A team might test whether a model can assist with harmful cyber activity, provide dangerous technical instructions, resist attempts to override its rules, or reliably refuse a class of requests. Red-team exercises ask people to probe weaknesses, while automated tests can check performance across many repeatable prompts.
These tests are useful because they make some risks visible before release. They can also compare versions and reveal whether a mitigation reduces a measured problem. But no test suite covers every prompt, user, tool, or real-world situation. A model might behave differently when connected to the internet, given access to code execution, or used in a long chain of interactions.
- Benchmarks are indicators, not guarantees. High scores on selected tests do not establish that a model is safe in all applications.
- Results depend on test design. Prompt wording, access conditions, scoring rules, and the chosen sample can change what an evaluation detects.
- Capabilities can be hard to predict. New combinations of tools, instructions, and user expertise may produce behavior that earlier tests did not cover.
- Mitigations can fail. Refusal training, filters, monitoring, and access limits reduce particular risks but should not be treated as infallible.
What this means for people building AI products
For a product team, a safety disclosure is most useful as a model for disciplined practice—not as permission to rely on a provider’s assurances. The application builder knows its users, data, tools, and consequences better than a general-purpose model supplier does. A model that is acceptable for drafting internal notes may need much tighter controls when it can send messages, change records, or guide decisions with serious consequences.
A practical development process includes:
- Define the use and harm scenarios. Specify who can use the system, what it can access, and what could go wrong. Include misuse as well as ordinary mistakes.
- Set measurable acceptance criteria. Test the actual product workflow, not just the underlying model. Decide in advance which failure rates or behaviors require a fix or a release delay.
- Limit authority. Give the system only the data and tools it needs. Require human review for consequential actions, especially where errors are difficult to reverse.
- Test adversarially and repeatedly. Include attempts to bypass instructions, disclose sensitive information, misuse tools, and exploit unexpected inputs. Repeat tests after model or product changes.
- Monitor and respond. Provide ways to report failures, review incidents, disable risky functions, and update safeguards. Protect user privacy when collecting operational evidence.
These steps apply whether a team uses a frontier model or a smaller system. The level of effort should reflect the system’s reach and potential impact. An AI feature with no external actions has a different risk profile from an agent that can execute code or make changes in a live service.
How to judge a disclosure
Readers should look beyond reassuring language and ask what a document lets them verify. A useful disclosure identifies the risks being addressed, explains the evaluation conditions, reports limitations, and connects results to concrete decisions. It should also make clear who is responsible for approving deployment and what happens if new evidence changes the risk assessment.
Important questions include:
- Are the thresholds and responses specific enough to understand?
- Does the document explain what was tested, under what conditions, and what was not tested?
- Are findings independently reviewed, or are they solely the company’s own assessments?
- Does the company describe incident reporting, updates, and safeguards after release?
- Can users understand which claims apply to the exact model and product they use?
Disclosure itself has limits. Some testing details may be withheld because publishing them could help attackers, and some safety work may involve confidential information. That can be a legitimate tension, but it also means readers may not be able to reproduce or fully assess every claim. Clear summaries of methods, scope, and limitations can still improve accountability without publishing sensitive operational details.
Common misconceptions and remaining gaps
A frequent misconception is that a model passing a safety evaluation means it cannot cause harm. Passing means only that it met a defined standard on a defined test under specified conditions. Another is that safety is solely the model provider’s responsibility. In practice, risk also comes from product design, permissions, deployment choices, user behavior, and the organization operating the system.
There is also a difference between preventing every harmful output and reducing risk to an acceptable level. No complex software can promise zero failures. The practical goal is to identify serious failure modes, lower their likelihood and impact, detect problems promptly, and make it possible to correct or suspend the system. Public disclosures help readers see parts of that work, but cannot replace ongoing evaluation or external accountability.
Frequently Asked Questions
Do OpenAI and Anthropic disclosures prove their AI is safe?
No. They describe policies, tests, and commitments, but cannot guarantee safe behavior in every use or independently verify every claim.
What is the difference between OpenAI’s and Anthropic’s approaches?
They use separate frameworks, risk categories, tests, and thresholds. Both describe staged evaluation and stronger precautions as risks rise, but their approaches are not interchangeable.
Can a model pass a safety test and still cause harm?
Yes. Tests cover selected situations, and performance can change with different prompts, tools, users, or deployment conditions.
What should a small AI product team do with these disclosures?
Use them as prompts to define risks, test the complete product, restrict permissions, review consequential actions, and monitor failures after launch.
Why don’t companies disclose every safety test in full?
Some details could enable misuse, and some information is confidential. Readers can still look for a clear account of scope, methods, limitations, and oversight.
Related
Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.