AI threat modeling: Research dossiers

Organise a question into a dossier

AI threat modeling becomes clearer when the question is organised before the conclusion is written. A dossier defines the system, the behaviour of interest and the evidence needed to examine it. It is a structured editorial format, not a guarantee of a completed security audit. This page explains that format; it does not advertise invented dossiers or findings. The first useful result may be a sharper question rather than a dramatic verdict.

The planned scanner’s scope and limitations explain how a future tool could support these research questions. The scanner is planned and unavailable; this guide is not an invitation to upload proprietary code or a claim that automated assessment is already operating.

An AI threat modeling dossier should state one principal question and the system context needed to examine it. This keeps the account focused on evidence rather than turning a broad concern into an implied completed assessment.

Scope and threat model

State what is being protected and what kind of influence matters to the investigation. Identify the relevant components and boundaries at a conceptual level. Describe the assumed permissions and the decisions that could affect the outcome. Keep the scope narrow enough for the evidence to be assessed. If the assumptions do not match the environment, the conclusion may not apply. A threat model should make that dependency easier for the reader to see.

For AI threat modeling, record the assets, influences and assumed permissions relevant to the question. The reader should understand which boundary matters and why a different environment might require a different interpretation.

Continue with The editorial approach.

Source evidence

Collect sources that can support the specific question. Prefer original publications, official component documentation and dated advisories. Record the version or environment when it is material to the finding. A broad warning about AI cannot establish a detailed claim about a particular system. Source quality is about fit as well as reputation. The reader needs enough context to follow the evidence without receiving private logs or unnecessary operational details.

The AI threat modeling source list should distinguish original evidence from commentary. Retain dates and version context so that a reader can inspect whether the referenced finding applies to the selected system and task.

Observations versus hypotheses

Separate an observation from a hypothesis about why it happened. Explain which parts of the account were measured, reproduced or inferred. When several explanations remain possible, preserve that uncertainty rather than selecting the most dramatic one by default. A dossier can include an unresolved question when it is stated clearly. The conclusion should be no larger than the evidence, even if a broader story would be more memorable.

A clear AI threat modeling account labels what was observed and what was inferred. When the cause remains uncertain, explain the competing possibilities and the evidence that would help distinguish them rather than choosing a dramatic conclusion.

Limitations and unresolved questions

Describe what the investigation did not cover. A narrow result may leave other versions, tools, permissions or input types unexamined. Missing evidence is not a pass and not proof of a failure. Record the limitation in the same place as the finding so that readers do not inherit an unsupported assurance. A responsible dossier is specific about its boundaries and does not claim that every possible security concern has been eliminated.

Write limitations as specific unanswered questions. Identify the versions, inputs or permissions outside the scope, and keep those limits attached to the finding when the dossier is summarised elsewhere.

Continue with Dangerous assumptions.

Review and updates

An update should explain what changed in the evidence or system. A revised version, a new source or an improved test may alter the interpretation. Keep the earlier context understandable without pretending it applies indefinitely. This is an evidence-led editorial process, not a promise of continuous monitoring or a contracted response service. The contact route accepts public questions and corrections; it is not a channel for confidential incident response.

An update should state the new evidence and why it changes the account. A revised interpretation is more useful when readers can follow the earlier scope without being told that it established something broader.

Explore adjacent topics

Use AI architecture to understand the system map, technical stories to follow an interaction and dangerous assumptions to challenge the conclusion. Those routes connect the dossier format with practical reading questions. Follow the editorial approach for source and illustration handling. The planned scanner remains a separate future project with no current code-upload capability. A public research suggestion can begin with a clear question and a link to the evidence you want examined.

Choose related reading that addresses the dossier’s remaining uncertainty. A technical story may explain one interaction, while the architecture map can reveal a missing component or permission assumption that the investigation needs to consider.