Making APIs Secure Demands Tracing and Machine Learning to Rapidly Limit Damage from Attacks

5 de may. de 2021 · 33m 35s
Making APIs Secure Demands Tracing and Machine Learning to Rapidly Limit Damage from Attacks
Descripción

The burgeoning use of application programming interfaces (APIs) across cloud-native computing and digital business ecosystems has accelerated rapidly due to the COVID-19 pandemic. Enterprises have had to scramble to develop...

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The burgeoning use of application programming interfaces (APIs) across cloud-native computing and digital business ecosystems has accelerated rapidly due to the COVID-19 pandemic.

Enterprises have had to scramble to develop and procure across new digital supply chains and via unproven business-to-business processes.Companies have also extended their business perimeters to include home workers as well as to reach more purely online end-users and customers.

In doing so, they may have given short shrift to protecting against the cybersecurity vulnerabilities inherent in the expanding use of APIs. The cascading digitization of business and commerce has unfortunately lead to an increase in cyber fraud and data manipulation.

Stay with us for Part 2 in our series where BriefingsDirect explores how APIs, microservices, and cloud-native computing require new levels of defense and resiliency.

To learn more about the latest innovations for making APIs more understood, trusted, and robust, we welcome Jyoti Bansal, Chief Executive Officer and Co-Founder at Traceable.ai. The interview is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions. [Sponsor: Traceable.ai.]
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Autor Dana Gardner
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