Upscale business performance through Enterprise Security and Machine Learning Governance

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.

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, project 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 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 applications. 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, automation becomes essential to verify that the applications and models are functioning correctly to ensure data governance, 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 successful automation across many data science models.

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Compliance Governance: the blueprint

For industries that require failproof 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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