Bolstering Cyber Defenses: A Strong Case for Artificial Intelligence

In recent times, the conversation around utilizing artificial intelligence (AI) in the realm of cybersecurity has gained considerable traction. The key question is: can AI genuinely enhance a firm’s security profile? Discerning the genuine AI-based solutions from those merely amplifying their claims becomes crucial. A clear understanding of AI’s role and potential benefits can guide better decision-making.

Artificial intelligence, in its essence, endows computing systems with human-like abilities such as perception, cognition, and discovery. It is the theory and practice of designing computer systems that can perform tasks that generally require human intelligence, such as visual perception, decision-making, language translation, and more. Applied in cybersecurity, AI enables organizations to identify, anticipate, and react to cyber threats in real-time, employing machine learning and deep learning.

The realm of AI involves systems learning from data patterns over time, allowing them to focus on significant variables and specific outcomes. Various concepts under AI include machine learning, deep learning, and cognitive computing. Machine learning, a subcategory of AI, learns from data patterns with minimal human intervention. Deep learning, a subset of machine learning, allows algorithms in artificial neural networks to learn from large data quantities to solve complex problems. Meanwhile, cognitive computing employs computational models to solve problems that demand extensive structured and unstructured data, using a human-like approach in complex situations.

AI holds the potential to tackle some of the predominant cybersecurity challenges currently faced by many businesses. These include a shortage of skills, the escalating cost of security breaches, and alert fatigue.

Organizations are grappling with a growing risk of security incidents due to a dearth of skilled security operations and threat intelligence resources. This shortage exacerbates each year and often leads to an overburdened workforce, resulting in human errors and employee burnout. Meanwhile, the rising cost of security breaches is another concern, with costs considerably higher in organizations lacking security automation.

Analysts dealing with security often face alert fatigue due to the sheer volume of alerts they receive daily, with a majority of threats going unidentified and unaddressed. This results in analyst burnout and high turnover rates.

AI, however, can bolster a firm’s security posture by addressing these issues. AI can help bridge the skill gap in cybersecurity, taking on part of the load from security operation center (SOC) analysts, and significantly expedite investigations. It allows analysts to make data-driven decisions, leading to a quicker, more decisive escalation process. Consequently, quicker threat identification and containment can substantially lower breach-associated costs.

The use of AI often eliminates the need to outsource security investigations to managed security service providers (MSSPs), thereby reducing associated costs. AI can also greatly decrease alert fatigue by cutting down on the number of insignificant daily alerts and prioritizing alerts for analysts to review, allowing them to focus on the most critical alerts first. This streamlining and prioritization not only makes the alert investigation process more manageable but also helps alleviate the pressure on analysts, thereby lowering turnover rates.

An analysis by Forrester Consulting suggests that organizations leveraging AI see benefits like increased SOC analyst productivity, reduced outsourcing costs for investigations, improved organizational security, resulting in a return on investment (ROI) of 210 percent.

To effectively tackle the above-discussed issues, equipping SOC analysts with AI in their daily tasks can prove beneficial. AI can help boost analyst productivity and effectiveness, reduce the time taken to investigate and remediate threats, lower breach costs, and enhance the overall security posture. Before choosing and implementing a solution, it’s essential to understand how AI can meet specific business needs and where it can have the most significant impact.

For reference purposes:

1. Buczak, A. L., & Guven, E. (2016). “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection.” IEEE Communications Surveys & Tutorials, 18(2), 1153-1176. This paper discusses the use of machine learning methods for cyber security, particularly in intrusion detection.
2. Tjhai, C., Zhou, G., Papadaki, M., Clarke, N., Liang, K., & Gan, R. (2019). “A New Direction for Cybersecurity Incident Response: Lessons Learned from Cyber Breach Investigation Reports.” Security and Communication Networks, 2019. This article reviews lessons learned from cyber breach investigations and underscores the importance of timely response to security incidents.
3. The Future of Jobs Report 2020 by World Economic Forum. This report highlights the growing skills gap in various sectors, including cybersecurity.
4. 2019 Cost of a Data Breach Report by Ponemon Institute. This annual study reports the rising costs associated with data breaches and the impact of security automation on these costs.
5. Imperva. (2017). “Cyber Threat Landscape: Imperva Insider’s Guide.” This guide provides insights into the volume of daily alerts received by security analysts.
6. Casey, E. (2018). “Alert Fatigue: Desensitization and Decreased Awareness to Alarm Hazards in Healthcare.” Biomedical Instrumentation & Technology, 52(1), 28-34. Although this article is focused on healthcare, it discusses the concept of alert fatigue in a broader context.
7. The Total Economic Impact Of IBM’s Security Solutions, a commissioned study conducted by Forrester Consulting, March 2021. This study provides evidence of the economic benefits of using AI in cybersecurity.