What ATLAS contributes
ATLAS is a living knowledge base about adversarial activity involving AI. It connects tactics and techniques with mitigations and case studies. It is useful when a team needs a shared language for discussing how an AI system could be attacked or abused.
Start with an architecture
Draw the application’s data stores, model access, retrieval services, tools, identities, and external dependencies. Select relevant threat paths from that picture. Starting from the architecture helps avoid a checklist exercise in which every technique is marked relevant without considering whether the conditions exist.
Use a technique as a test hypothesis
For a document assistant, a hypothesis could ask whether hostile retrieved content can cause an unauthorized tool action. Define an isolated test, expected boundary, and evidence to collect. An ATLAS reference helps organize the hypothesis, while the actual test tells you about the local system.
What it cannot establish by itself
A catalog entry does not prove that a vulnerability exists in your environment or quantify its likelihood. A successful research demonstration also has a scope and configuration. Preserve those details when turning a case study into an educational example. Pair the threat view with risk ownership and control verification.
Try this with a familiar use case
Choose a document assistant or an AI feature your team understands. Read one of the official resources below, write down a question it raises, and identify the local evidence needed to answer it. Keep your own interpretation separate from the source’s stated requirements.
Official resources
Independent educational guide. SyruSec is not affiliated with or endorsed by the organizations discussed. Full standards and materials remain subject to their publishers’ access and licensing terms.