The datasets used by data scientists to train the models, as well as the platforms where those models are created, are among the riskiest components of analytics, AI, and ML.
Fremont, CA: As businesses adapt to new ways of working, managing employees, and serving customers, they are looking for any technological advantage they can get. They are making larger investments in cloud computing, e-commerce, digital supply chains, artificial intelligence (AI) and machine learning (ML), data analytics, and other areas that can provide efficiency and innovation.
Simultaneously, businesses are attempting to mitigate risk, and the same digital initiatives that create new opportunities can also result in risks such as security breaches, regulatory compliance failures, and other setbacks. As a result, there is a constant conflict between the need to innovate and the need to reduce risk.
Multicloud or hybrid cloud infrastructures
More businesses are migrating to IT environments supported by multiple cloud services, often from various providers. This includes offerings such as software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS).
No matter the type of cloud used, hosting critical data and applications outside of an organization's defensive perimeter introduces significant risk, particularly when multiple locations, services, or vendors are involved. Aside from data loss or theft, businesses may face issues with data privacy regulations, not to mention the risk of cost overruns caused by poor cloud management practices.
Digital supply chains and sales channels
End-to-end digital connectivity, cloud services, blockchain, robotics, autonomous vehicles, and advanced analytics tools are among the technologies that employers continually rely on to improve and manage their supply chains.
This digital transformation of the supply chain can enhance efficiency and visibility, eliminate errors and costs, facilitate cooperation with business partners, and streamline processes. However, it can also introduce risks, such as data loss.
Parties involved in business-to-business (B2B) digital services can use various risk-mitigation techniques, including developing comprehensive business agreements with partners that address the multiple risks and responsibilities. Companies can also implement cybersecurity and data privacy controls to ensure secure data transmission and storage.
Automation and analytics
Companies are rushing to automate time-consuming and labor-intensive manual processes to speed up operations, eliminate errors, and save money. AI and robotic process automation (RPA) can help automate tasks like data entry, dramatically improving how business processes are managed, but they can also introduce risks. The datasets used by data scientists to train the models and the platforms where those models are created are among the riskiest components of analytics, AI, and ML.
Risk mitigation measures include:
Having well-crafted contracts to manage big data partnerships.
Limiting the data used in data sets to the bare minimum.
Using anonymized data whenever possible.
Some of the risks associated with automation can be attributed to an inability to scale quickly enough or meet expectations.
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