What are the key steps in developing an ontology-based internal audit framework?
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Developing an ontology-based internal audit framework involves several key steps. Firstly, it is essential to address privacy compliance through a "privacy by design" approach, utilizing semantic modelling and reasoning layers to integrate context information for effective compliance checking during audit trials . Secondly, the design and implementation of smart contracts should incorporate ontology engineering and model-driven engineering to ensure critical validity aspects are explicitly represented, facilitating audit through a clear separation of concerns . Additionally, automated compliance auditing solutions using ontology-based information models can verify adherence to regulatory standards, such as in the banking sector, streamlining the audit process and identifying discrepancies between security standards and actual practices . Lastly, enhancing and populating a compliance ontology through information extraction from existing GRC systems can facilitate semantic interoperability, reducing complexity and redundancy in compliance monitoring for enterprises .
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| Papers (5) | Insight |
|---|---|
| Key steps in developing an ontology-based internal audit framework involve utilizing Ontology Learning for semi-automation, creating OWL representations, and employing natural language for concept interpretation and semantic understanding. | |
01 Jul 2017 6 Citations | Enhancing compliance ontology through information extraction from a GRC system is crucial for semantic interoperability, reducing redundancy, and streamlining compliance monitoring in enterprises. |
Open access•Proceedings Article 24 Mar 2013 | Key steps in developing an ontology-based internal audit framework include establishing risk management context, identifying risky events, formulating risk responses, and monitoring risk action plans. |
Open access•Journal Article 1 Citations | Key steps in developing an ontology-based internal audit framework include semantic modelling, integrating context information, and implementing a semantic reasoning layer for compliance verification in healthcare data sharing. |
| Utilize ontology engineering and model-driven engineering to design and implement smart contracts, ensuring critical validity aspects are represented for auditability in the REA ontology framework. |
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