By Andy Nallappan, Chief Technology Officer and Head of Software Business Operations, Broadcom Software – Enterprises and operators of critical infrastructure have long been on the front lines of cybersecurity. Most recently, new threats have been identified through our Symantec Threat Hunter team, including Lazarus, Verblecon and Daxin. And of course, the previous attacks forcing major service interruptions on large infrastructure.
The case for Zero Trust security has never been clearer than it is today. Workflows are increasingly happening in the cloud, hybrid work is becoming the norm, and the number and impact of cyberattacks continues to escalate. According to the Cloud Collaboration Market 2022 by Mordor Intelligence, the Cloud Collaboration Market is growing at a calculated annual growth rate of 13.43% over the next five years. It seems the hackers have noticed.
Enterprises moving their artificial intelligence projects into full scale development are discovering escalating costs based on initial infrastructure choices. Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex.
The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. Because it’s common for enterprise software development to leverage cloud environments, many IT groups assume that this infrastructure approach will succeed as well for AI model training.
As more companies deploy artificial intelligence (AI) initiatives to help transform their businesses, key areas where projects can go off the rails are becoming clear. Many problems can be avoided with some advanced planning, but several hidden obstacles exist that companies don’t often see until it’s too late.
Protecting remote workers’ access to cloud applications, public cloud environments, and private access networks is crucial. While organizations are thriving in highly collaborative environments with globally dispersed teams, partners, vendors, and suppliers, the sharing of data comes with risk. It is now more imperative than ever to have a precise level of control and insight into the data sharing process.