Enhancing Security with AI: Risk Assessment in Focus

Enhancing Security with AI: Risk Assessment in Focus

Table of Contents

1. Introduction
2. Motivation for the Study
3. Research Methodology
4. Results of the Mapping Study
5. Overview of Security Risk Assessment Approaches
   • Gut Feelings and Guesses
   • Past Incidents
   • Overwhelmed Organizations
   • The Digitization of Society
   • The Need for Data-Driven Decisions
   • Real-Time Assessment of Risk and Trust
   • AI Techniques for Dynamic Scaling and Adaptation
6. Research Questions and Findings
   • Research Question 1: Activity in AI Supported Security Risk Assessment
   • Research Question 2: Domains of Application
   • Research Question 3: Security Risk Assessment Tasks
   • Research Question 4: Types of AI Methods Used
7. Conclusion
8. Future Research Directions

AI Supported Security Risk Assessment: A Systematic Mapping Study

AI (Artificial Intelligence) has gained significant Attention in the field of security risk assessment. In this article, we present a systematic mapping study that focuses on approaches for AI supported security risk assessment. The study aims to provide an overview of the use of AI techniques for identifying, estimating, and evaluating cyber risks. It also explores the activity, domains of application, specific tasks, and types of AI methods used in security risk assessment.

Introduction

The increasing complexity and aggressiveness of the cyber risk landscape have compelled organizations to Seek data-driven and real-time approaches to security risk assessment. This need is further accentuated by the digitization of society, where interconnectedness and complex digital value chains pose new challenges. The capability of AI techniques to dynamically Scale and adapt has made them a potential solution in addressing these challenges.

Motivation for the Study

Traditional security risk assessment methods often rely on gut feelings, guesses, and past incidents. This practice is no longer sufficient in the face of evolving cyber threats. Furthermore, organizations find themselves overwhelmed by the dynamic nature of the risk landscape. To bridge this gap, there is a need to explore AI-supported approaches that can provide more accurate and efficient risk assessment.

Research Methodology

The systematic mapping study followed standard procedures to identify and analyze Relevant papers. A review protocol was created, which included criteria for inclusion and exclusion of papers. A search strategy was developed to Gather a comprehensive set of papers on AI-supported security risk assessment. A classification scheme was used to categorize the papers and extract the necessary information for answering the research questions.

Results of the Mapping Study

A total of 33 primary studies were identified after applying filtering steps to the initially identified potential papers. The results showed a gradual increase in activity in AI supported security risk assessment since 2010. The peak number of papers was observed in 2020. The majority of the papers came from academia, with a smaller portion being joint collaborations between academia and industry. The domains of application were diverse, with network and industrial control systems receiving the most attention. Different security risk assessment tasks were supported by AI methods, including risk identification, analysis, and evaluation. Bayesian networks and neural networks were found to be the most commonly used AI methods in security risk assessment.

Overview of Security Risk Assessment Approaches

Traditionally, security risk assessment has been Based on gut feelings, guesses, and past incidents. However, in the age of digitization and complex interconnected systems, data-driven decisions and real-time assessment of risk and trust have become crucial. AI techniques offer the potential to address these challenges through dynamic scaling and adaptation.

Research Questions and Findings

Four research questions were addressed in the study:

1. Research Question 1: Activity in AI Supported Security Risk Assessment - The results showed a gradual increase in activity with a growth rate of 133% from 2010 to 2020.
2. Research Question 2: Domains of Application - The domains of application were diverse, with a focus on network and industrial control systems.
3. Research Question 3: Security Risk Assessment Tasks - Different tasks such as risk identification, analysis, and evaluation were supported by AI methods, with risk identification and analysis receiving the most attention.
4. Research Question 4: Types of AI Methods Used - Bayesian networks and neural networks were the most commonly used AI methods, followed by genetic algorithms and support vector machines.

Conclusion

AI-supported security risk assessment has shown increasing activity in recent years. However, further research is needed to gain more insights into this emerging domain. The use of AI techniques has primarily been focused on risk identification and analysis, with risk evaluation receiving less attention. Bayesian networks and neural networks have been the preferred AI methods in security risk assessment. This study highlights the need for more research and development in AI-supported approaches to address the dynamic and complex cyber risk landscape.

Future Research Directions

The study also suggests several future research directions, such as exploring risk evaluation in more depth, investigating the use of AI methods in other domains, and examining the effectiveness of hybrid AI approaches. These research directions can contribute to advancing the field of AI-supported security risk assessment and enhancing the resilience of organizations in the face of evolving cyber threats.

Highlights

• AI techniques offer the potential to address the dynamic and increasingly aggressive cyber risk landscape.
• The systematic mapping study revealed an increasing trend in AI supported security risk assessment since 2010.
• Network and industrial control systems are among the domains that have received the most attention.
• Risk identification and analysis have been the primary focus of AI-supported security risk assessment.
• Bayesian networks and neural networks are the most commonly used AI methods in security risk assessment.

FAQ

Q: What is the motivation behind the study on AI supported security risk assessment? A: Traditional approaches based on gut feelings and past incidents are no longer sufficient to address the dynamic and complex cyber risk landscape. AI techniques offer the potential for data-driven and real-time assessment of risk and trust.

Q: Which domains have been the focus of AI supported security risk assessment? A: The domains of application are diverse, with network and industrial control systems receiving the most attention. Some approaches also focus on malware detection in mobile technologies and the use of information from social networks or forums.

Q: What types of AI methods have been used in security risk assessment? A: The most commonly used AI methods are Bayesian networks and neural networks. Genetic algorithms, support vector machines, convolutional neural networks, and decision trees have also been utilized.

Q: Are there any limitations to the study's findings? A: The study identified 33 approaches, indicating that the domain is still in its early stages. More research is needed to draw stronger conclusions. Additionally, the study focused on papers available until October 2020, so there may be more recent developments in the field.