Risk · R-140 · Generative Content & Information Integrity
Out-of-scope responses
A system answers questions outside its intended domain, where it has not been tested and the organisation has no authority or competence to assist.
- Severity
- Medium
- Audience
- Heads-of · Practitioner
- Frameworks
- Article 13 · Annex A.9 · LLM07
- Responsible AI domain
- Lifecycle stage
- Reviewed
- 2026-11-11
What it is
A general-purpose model will attempt anything it is asked. An assistant built to answer questions about a bank's current accounts can also be asked about tax, medication, a legal dispute or a competitor's products, and unless it has been constrained it will reply in the organisation's name. None of that was tested. Some of it falls into regulated territory, as unsuitable AI-generated advice or guidance describes. The risk is the gap between the purpose a system was approved for and the range of things it will actually do.
How it happens
- The system prompt describes the role and does not restrict the subject.
- Refusal behaviour was not tested at the edges of the domain.
- A user moves gradually from an in-scope question to an out-of-scope one.
- The organisation prefers an answer to a refusal, for customer satisfaction.
Why it matters
The organisation is taken to have said whatever its assistant says. Answers on untested subjects are more likely to be wrong, and the organisation may have no permission to give them. Screenshots of an assistant far outside its brief travel quickly.
Warning signs
- Transcripts show the assistant answering on unrelated subjects.
- The intended-use statement does not list what is out of scope.
- No test set probes off-topic requests.
- Refusals are counted as failures in the product's measures.
Mitigating controls
Primary controls
- Intended-use definition and use restrictions Directive
- Defines the domain the system may answer in. The approved purpose, users, population and conditions of use of each AI system are stated precisely, and any use outside them requires reassessment before it begins.
- Output validation and guardrails Preventive
- Detects and declines out-of-scope requests. Every model output is validated against a schema and sanitised by deterministic code before any downstream system, tool or user interface acts on it.
Supporting controls
- Escalation to a human Preventive
- Routes out-of-scope needs to a person. Automated channels offer a prompt, unobstructed route to a person, available on request and triggered automatically by defined signals, with the conversation handed over.
- Evaluation suite and regression testing Detective
- Tests refusal behaviour at the edges of the domain. A maintained set of test cases with expected results is run on every change to model, prompt, data or tools, and the results are compared with the approved baseline.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| EU AI Act | Article 13 | Transparency and provision of information to deployers Intended purpose is stated to deployers. |
| ISO/IEC 42001:2023 | Annex A.9 | Use of AI systems Use within intended bounds. |
| OWASP Top 10 for LLM Applications | LLM07 | Misinformation Unsupported answers. |
Sources
- EU AI Act: Regulation (EU) 2024/1689 European Union (EUR-Lex) · official text
- ISO/IEC 42001:2023 – AI management systems ISO/IEC · framework
- OWASP Top 10 for LLM Applications OWASP GenAI Security Project · framework
Related terms
How to cite
Axiom Verity (2026). Out-of-scope responses. Reference Library, R-140. https://axiom-verity.com/risks/out-of-scope-responses. Reviewed 2026-11-11.
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