Upscale business performance through Enterprise Security and Machine Learning Governance Author: Sourabh Kumar, Principal Architect, AI@Scale, Machine Vision and Conversational AI
Search engines are now part of our everyday lives. Research shows that Google handles an incredible 40,000 searches per second – a staggering 3.5 billion daily! Businesses are developing advanced enterprise-scalable analytics products to harness this vast amount of data and derive actionable insights.
closing their accounts. Establish a comprehensive retention plan to delete obsolete information regularly and efficiently. Breaking the barrier of enterprise silos for better business impact and data security governance However, teams might face several challenges while developing efficient enterprise security and governance. Every enterprise has different units, verticals, products, and geographies. Each works in silos – running its models using its own set of tools, which may lead to an operational bottleneck and add more complexity to governance and security implementations. For instance, a CTO (Chief Technology Officer) and a CIO (Chief Information Officer) team may have distinct enterprise vision and priorities. Both teams may develop models to optimize the enterprise’s performance but in seclusion. Such situations often see the repetition of work and difficulty in integrating the models. Both security and governance could be at risk in such a scenario. An advanced solution becomes necessary if enterprise performance is to be optimized. And there must be systems in place that are leveraged across departments to make governing and monitoring easier. With advances in big data and analytics, enterprises are creating sophisticated data science models and applica- tions. When an enterprise has a smaller number of models, governance can be manual and straightforward. When the stakes increase with a rising number of models, automa- tion becomes essential to verify that the applications and models are functioning correctly to ensure data gover- nance, security, and safety. This is a challenging task. Hence, enterprises must break away from siloed thinking towards an integrated end-to-end view to ensure success- ful automation across many data science models.
Enterprise security and machine learning governance are thus vital for ensuring optimal performance and enabling stringent security protocols for analytics projects.
This concept involves embedding governance into artificial intelligence and how it can be embedded using the right technology, process, and people. It ensures a risk-free, sustainable, and scalable system. No matter how robust the technology developed, projects can only be stabilized and succeed with the correct enterprise security and governance mechanism. To effectively adopt Enterprise Security and Governance, enterprises must: Implement robust measures to protect and secure data. For example, enterprise teams should ensure encryption when data is at rest and while being transported and access controls on internal users and external parties with heightened scrutiny for those seeking access logs, all supported by up-to-date software systems. Develop a mechanism enabling individual users to request the erasure of personal information after
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