Abstract
Generally, the present disclosure is directed to techniques to automatically determine risks associated with a product. In some implementations, the techniques of the present disclosure can include or otherwise leverage one or more machine-learned models to determine if the release and continued sales of product has legal, privacy and/or business vulnerabilities based on identifying sensitive keywords in product-related documentation and product areas.
This disclosure applies machine learning techniques to automate product audits and improve product review quality. Machine learning techniques are applied to proactively identify risks associated with a product, e.g., for a software product or service, the techniques are applied to determine a risk of privacy failure or incidents when user privacy may be violated. Application of machine learning as described herein can automate product review for risks. The techniques can help reduce the time spent by employees on reviewing products. Further, the techniques can substitute or augment manual product review. Deploying automated product review techniques also reduces reliance on the limited number of subject matter experts that typically conduct product review. Product stakeholders can learn from the risks and vulnerabilities identified in the automated review. Applying the techniques described herein can help accelerate product launch and reduce risks associated with the product. The techniques can be applied for product review by companies and other entities, e.g., when the product is subject to regulatory review and/or public scrutiny.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"Applying machine learning techniques to determine product risks", Technical Disclosure Commons, (August 28, 2017)
https://www.tdcommons.org/dpubs_series/653