CIO Think Tank

The biggest fear is not the technology, it is the potential of human error that could expose your organization to a cyberattack. The majority of CISOs agree that an employee carelessly falling victim to a phishing scam is the most likely cause of a security breach. Most also agree that they will not be able to reduce the level of employee disregard for information security. How do we guard against human error without limiting employee efficiency and productivity?

The terms "Artificial Intelligence" and "Advanced Machine Learning" are often thought of interchangeably. While there is a relationship between AI and AML, to say they are the same thing is an oversimplification and misclassification. Rather, one begets the other with AI being the basic principle upon which AML is developed. As AI begins to mature and migrate away from purely advanced mathematical operations into decision making paradigms, AML steps forward as the predictive ability of machines to process vast quantities of data. As data and analytics becomes foundational to the way every business operates, AI and AML will become foundational capabilities.

While new technology and solutions can help CIOs make better decisions for an organization, nothing will take the place of a highly talented workforce. CIOs need to be great recruiters of talent but even more importantly, be able to grow the skills and capabilities of their existing team. Competitive compensation is important in talent retention but so is a work environment where individual growth and development are top of mind. 

Bridging the gap between business and technology is not easy and requires discipline and balance between technology, people, and the business. For so many organizations today, technology is the business. Technology needs to be understood as a critical enabler in every part of the organization from the front line to the back office. It creates new value by crunching data to deliver new insights, it spurs innovation, and it disrupts traditional business models.

For business and technology leaders alike, new actions and behavioral changes can help their organizations make this shift. CIOs must take responsibility for problems, they should convey that when technology fails, many people typically share responsibility.

Data is a key enabler for all of the CIO’s plans to transform and should be at the heart of any organizational strategy. All of the digitization in our world today means we have enormous amounts of data available, and data has now become the number one business asset for every organization. The utilization of data has grown exponentially giving organizations the ability to better understand customers, research key trends, and much more. Enterprises are heading towards an environment of uncertainty and ambiguity that requires continuous flexibility, innovation and investment or reinvestment in data and analytics strategies.

Disruption alters, destroys, but also creates value. You realize the need to act once revenue starts to shift, which is happening at a faster pace than most would think. Disruption can be a great thing if you act upon it, but it is a threat if you watch idly. We will discuss how CIOs and CISOs can be proactive and act on disruption by figuring out how to identify, prioritize and respond.

In the post pandemic era, remote employment is the new status quo. Employers are forced to implement and improve the digital workplace by providing productivity tools and accessibility to company resources. In this session, we will share case studies of successful digital workplace implementations, including how to deal with the inherent security risks of expanded accessibility to company resources.

If digital transformation is a journey, then data is the fuel that transports your company from here to there. Comprehending and creating insight from data is transformational. Being able to gather and analyze data to generate a competitive edge, creates new opportunities and promotes innovation. However, to support a pipeline of up-to-date and competitive modern data and analytics, a sound foundation and architecture must be constructed.

Edge computing is computing topology in which the content collection and delivery are placed closer to their endpoints. The goal of edge computing is to keep the traffic and processing local to eliminate delays. Currently, edge computing is being driven by IoT and requires processing to be close to the end rather than on a centralized cloud server. However, rather than creating brand new architecture, cloud computing and edge computing will evolve into centralized servers, on-premises servers and on the edge devices themselves.