Compliance Governance: the blueprint For industries that require fail-proof compliance, a robust governance system is needed to mitigate risk and integrate audit, balance, and control the life cycle of machine learning development. The governance system should include assessments as gatekeepers for stage upgrades at each model development step.
Stepping up security standards in enterprise solution architecture Depending on their purpose, security standards vary across industries and their corresponding models. For instance, banks would have a highly regulated system. Insurance companies may focus on developing a system to address their process challenges. In regulated sectors such as life sciences, robust security is paramount. In contrast, lower controls may be more suitable for those sectors that emphasize commercial excellence or sales effectiveness, i.e., helping commercial sales effectiveness. Enterprise security and Machine learning governance complexity varies according to industry and Use case. For example, in the BFSI industry, higher-level control must always be upheld regarding credit risk scoring systems while allowing quick deployment of secure solutions with minimal effort. If we look at the consumer-packaged goods industry, it relies heavily on machine learning models to generate successful results. Yet, these models could be severely compromised if effective security measures are not
implemented. Organizations often focus on embedding security measures at every step in their models, which might cause complexities and delays in deployment. While enterprise financial data may call for maximum security, it is equally vital for other verticals to ensure data governance in their models. Enterprises should also avoid excessive safeguards to ensure smooth deployment progress. It is recommended that the teams ensure that only minimum requirements are met when constructing effective yet secure models. Tip-off for the future: tighten the grip of security in enterprise governance models For enterprise security and machine learning governance to function optimally, teams must transition from siloed working to collaboration. As enterprises transition into Industry 4.0, they can maximize the potential of their machine-learning governance and enterprise security by embedding robust security at every step of the models.
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