Securing Tomorrow: Navigating the Cybersecurity Skills Shortage in Emerging Tech

Staying ahead of emerging threats requires a skilled workforce equipped to navigate the complexities of cutting-edge technologies. Recent data highlights a significant gap in expertise, particularly in the realm of artificial intelligence and machine learning (AI/ML), where 28% of employers cite these skills as among the most sought-after. Moreover, within the past year, a staggering 36% of cybersecurity professionals have identified risks associated with emerging technologies such as blockchain, AI, virtual reality (VR), quantum computing, and intelligent automation as their foremost challenges.

Among the myriad of emerging technologies, attention is particularly drawn to areas such as generative AI, machine learning, containerization, and microservices, reflecting the need for specialized knowledge in navigating these domains to fortify cyber defenses. In this context, addressing skill shortages in emerging tech becomes imperative for organizations striving to safeguard their digital assets amid an ever-expanding threat climate.

Despite the increase in the number of cybersecurity graduates from various institutions and the growth of skills and experience among recent graduates, there is still a significant gap in one particular area of the cybersecurity workforce—new and emerging technologies. According to ISC2’s Cybersecurity Workforce Study, 84% of professionals have no or minimal knowledge of Artificial Intelligence (AI) or Machine Learning (ML).

Technological advancements, such as generative AI, machine learning, containerization, and microservices, have exploded onto the scene, and businesses are eager to incorporate them into their tech stacks and take advantage of the capabilities and potential savings they offer. Unfortunately, the security implications of these emerging technologies are wide-ranging and complex, and security teams are struggling to find security professionals who have the skills and expertise to adequately address them.

Training and education have always lagged behind innovation and emerging technology, as new training materials must be created from scratch to fill the void, and this takes time—often years. This has a compounding effect, as many cybersecurity professionals are still trying to learn the previous generation of emerging technologies (cloud computing, etc.), and the industry is already moving into new areas. Additionally, companies often do not provide enough budget or time for professionals to invest in learning new technologies.

As new technologies continue to emerge, so too do the potential risks. These risks encompass a wide array of concerns, including vulnerabilities stemming from inadequate security measures, the potential for novel attack vectors exploiting technological advancements, and the inherent difficulty in securing nascent technologies whose threat landscape may not yet be fully understood. These are a few of the most prominent areas of emerging tech and the security risks they pose:

Generative AI

Not since cloud computing hit the mainstream has anything shaken the IT industry quite like generative AI has this past year. This new technology promises nearly unlimited possibilities and just as many risks. While it can produce a mountain of tremendous content given just a few prompts, it also empowers threat actors to scale up and automate their attacks at rates we’ve never seen before. It creates an avenue for unintentional data leakage, and factually incorrect responses, aka hallucinations, all of which can wreak havoc on your processes.

Machine Learning

Developments in Machine Learning (ML) also have several security implications, including model poisoning, where attackers inject malicious data during model training to compromise its performance or integrity. Challenges also persist in the difficulty of analyzing ML network traffic and logs, hindering the identification of anomalous or‌ malicious activities. Addressing these concerns requires a multifaceted approach encompassing robust data governance, rigorous model validation, and ongoing monitoring to fortify the security posture of machine learning systems.

Containerization

Containerization, a software deployment technology that allows developers to package software and applications in code and run them in isolated compute environments, can also introduce vulnerabilities, particularly when there is poor provisioning at the build layer. This causes containers to be granted root user access, increasing the risk of privilege escalation. Despite abundant information on container usage, there's a notable lack of training for developers and administrators on secure deployment practices.

Microservices

Microservices break down complex applications into smaller components that are independent of each other and more manageable. Security concerns arise in a microservice-based architecture due to insufficient expertise in identifying trust boundaries and implementing robust authentication and authorization mechanisms. Authorization, particularly focusing on the principle of least privilege, is often overlooked in microservices development, necessitating a greater emphasis on authorization practices to better control access to external systems and applications within both the development and security communities.

Within the next two years, roughly half (45%) of cybersecurity professionals believe that AI will overtake worker/skill shortages to become the biggest challenge faced by the industry. With the continuous emergence of new technologies, it is vital for security experts to consistently refresh their knowledge to keep up with the capabilities and potential dangers that come with these advancements.

Increasingly constrained cybersecurity budgets force security professionals to come up with more creative solutions than ever to stay up to date with emerging technologies. If formal education and top-tier training are out of reach for your budget, consider leveraging vendor-provided training on the solutions in use at your organization. Additionally, Coursera, Udemy, and LinkedIn Learning all offer a variety of low-cost training courses. Lastly, security operations organizations should be looking for vendors that offer solutions with simpler operational requirements and lower management overhead to limit the upskill burden placed on the security team.

Navigating the cybersecurity skills gap will always be a challenge with the introduction of new technologies. However, ensuring that your environment remains protected from the latest threats can be achieved by leveraging advanced AI-powered security solutions. These solutions proactively detect and respond to malicious activities, making them an effective tool in safeguarding your systems from potential attacks.

Unlike signature-based detection methods, which are reactive and always playing catch-up with attackers, Abnormal employs AI-driven approaches to stay ahead of the curve. By utilizing AI to analyze vast amounts of data and discern between malicious and benign activity, Abnormal can identify deviations from the norm, even in the absence of known indicators or tactics. This proactive stance is essential in countering the increasing sophistication of cyber threats, particularly those leveraging AI themselves. Organizations can better protect their digital assets by employing good AI to fight bad AI and stay one step ahead of adversaries.

Interested in learning more about how Abnormal stays one step ahead of advanced attacks? Schedule a demo today!