> For the complete documentation index, see [llms.txt](https://neurablock.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://neurablock.gitbook.io/whitepaper/system-architecture/workflow-and-interaction-between-the-threat-oracle-and-the-machine-learning-model.md).

# Workflow and Interaction Between the Threat Oracle and the Machine Learning Model

* The workflow begins with the threat oracle, which monitors the blockchain for new or modified contracts. Upon detecting a contract, the oracle extracts its details and passes them to the contract analysis module.
* The contract analysis module processes the contract, extracting and preparing the opcodes for analysis. This processed data is then fed into the machine learning model.
* The machine learning model analyzes the opcodes, assessing them for patterns or indicators that suggest malicious intent. The results of this analysis are then passed back to the threat oracle.
* Based on the analysis results, the threat oracle makes informed decisions on whether the contract poses a security risk and takes appropriate actions, such as blocking the interaction with the destination contract.


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