Articles about 8 Steps to Better Security

Artificial intelligence (AI) and machine learning (ML) solutions are being adopted across every industry today. Quite often, these initiatives involve deploying ML models into operational settings where the model output ends up being a widget on the screens or a number on the reports that are put in front of hundreds, if not thousands, of front-line employees. These could be underwriters, loan officers, fraud investigators, nurses, teachers, claims adjusters, or attorneys. No industry is immune to these transformations.

These initiatives are typically driven from the top down. Management monitors and looks for ways to improve KPIs, and increasingly, AI/ML initiatives are identified as a means to this end. Certainly, there’s plenty of communication among executive, finance, data science, and operational leaders about these initiatives. Unfortunately, in many of the organizations I’ve worked with, the group of folks who are most commonly left out of the discussion are the front-line employees.

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As a wise man once said, a failure to plan is a plan to fail. This is especially true in the world of cybersecurity, where it is all but inevitable that an organization will face a security incident.

According to the 2024 Data Protection Trends report from Veeam, ransomware is the leading type of cyber crime, due to its lucrative nature. Cyber criminals have found that stealing, encrypting and selling data back to their victims is highly profitable, which has led to ransomware becoming a billion-dollar industry. Between ransom payments, maintenance, and lost business due to downtime, the average ransomware attack costs a business around £3.5 million.

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Advancements in technology within the last decade have sparked the increased use of digital biometric verification. The technology’s modern verification capabilities have outpaced traditional cybersecurity attack methods geared toward credentials theft -- making the technology an attractive enhancement for corporations seeking to provide a more secure, seamless experience for users to verify their identities. Now, users can leverage biometric technology for secure access to critical information, such as applications in financial and healthcare sectors.

However, recent pushback from the Federal Trade Commission (FTC) on the use of biometrics for identity verification, particularly age verification, highlights compliance concerns surrounding enterprises’ data collection and storage practices -- especially the collection of minors’ biometric information.

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Network management and security should go hand in hand. However, making these services work has become more complicated and riskier due to the growth of the public cloud, the use of software applications, and the need to integrate different solutions together.

This complex network security domain requires more skilled cybersecurity professionals. But as this need becomes obvious, so does the glaring skills gap. In the UK, half of all businesses face a fundamental shortfall in cybersecurity skills, and 30 percent grapple with more complex, advanced cybersecurity expertise deficiencies.

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The cybersecurity landscape is experiencing an unprecedented surge in vulnerabilities. In 2022 alone, a staggering 25,096 new vulnerabilities were added to the National Vulnerability Database (NVD). This number represents the highest count of vulnerabilities ever recorded within a single year and reflects a 25 percent increase compared to the 20,196 new vulnerabilities reported in 2021.

This escalating trend indicates that cybersecurity threats are not only on the rise but are also accelerating at an alarming pace. The reasons behind this surge in vulnerabilities are multifaceted, stemming from factors such as the increasing complexity of software and technology systems, the rapid pace of digital transformation, and the growing sophistication of cyber attackers.

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Machine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data.

This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space. It begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python -- by using open source datasets or instructing you to create your own.

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Harden your business against internal and external cybersecurity threats with a single accessible resource.

In 8 Steps to Better Security: A Simple Cyber Resilience Guide for Business, cybersecurity researcher and writer Kim Crawley delivers a grounded and practical roadmap to cyber resilience in any organization. Offering you the lessons she learned while working for major tech companies like Sophos, AT&T, BlackBerry Cylance, Tripwire, and Venafi, Crawley condenses the essence of business cybersecurity into eight steps.

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