A guest writer of WEI, see Bill Frank’s biography and contact information at the end of this article.
Michael Lewis coined the term, Moneyball, in his eponymous book published in 2003 and made into a movie in 2011 starring Brad Pitt. Moneyball was about applying analytics to baseball. Billy Beane, the Oakland Athletics General Manager, was the first baseball executive to use analytics to increase the probability of winning games.
See Bill Frank’s biography and contact information at the end of this article.
[Note: This is an updated version of the original article posted on March 21, 2024. I replaced the term "Governance” Controls with “Performance” Controls to eliminate any confusion with the NIST Cybersecurity Framework 2.0 use of the term “Governance.”
I focus here on automated controls that monitor and measure the “performance” of “Defensive” controls that directly block threats or at least alert on suspicious activities.
How well are your cybersecurity controls performing? Measuring control efficacy is challenging. In fact, under-configured, misconfigured, and poorly tuned controls, as well as variances in security processes are the Achilles Heels of cybersecurity programs.
A mismatch between risk reduction potential and performance results in undetected threats (false negatives) as well as an excessive number of false positives. This leads to an increase in the likelihood of loss events.
All controls, whether people, processes, or technologies, can be categorized in one of two ways – Defensive or Performance.
Regardless of the industry, all enterprises are looking for strategies to improve their organizations and stay competitive in the market. However, when it comes to technological innovation, there’s one piece in particular that often blocks the way forward: the human element. While computers may still lack the creativity that sets the human mind apart, artificial intelligence (AI) has become an increasingly important part of modern computing, especially when it comes to processing and managing data.
These days, more enterprises than ever are undergoing digital transformations to modernize their operations and keep up with the competition. Technologies like cloud computing, mobile tools and various IoT (Internet of Things) solutions are being adopted to power these transformations.
Digital transformation is reinventing the IT infrastructure. Enterprises are shifting to software-defined data centers and the new technology that comes with them. AI and machine learning (ML), cloud utilization, and software-defined storage are just a few of the many new tools that organizations now employ to help them harness the power of their data. As a result, IT infrastructure management has become increasingly complex. IT staff can no longer be expected to manage every part of the infrastructure. However, the answer to this issue is simple: let technology manage itself. AI/ML intelligent data platforms can provide comprehensive data management throughout the infrastructure.
