Published on December 24th, 2024
Introduction
The concept of an attack surface—the sum of all potential entry points an unauthorized user can exploit to compromise a system—has evolved significantly over the years. With the rapid adoption of new technologies, including remote working, the Internet of Things (IoT), and artificial intelligence (AI), corporate IT environments are facing a rapidly growing number of vulnerabilities. As a result, Attack Surface Management (ASM) has emerged as a critical practice for identifying and securing these exposure points before cybercriminals can exploit them. This article explores the challenges of managing the modern attack surface and highlights the role of AI in both enhancing security and complicating the landscape for organizations.
The Growth of Attack Surfaces
The Changing Landscape of Cybersecurity
The attack surface of a typical organization is no longer confined to a handful of web servers or physical devices. In the current environment, the attack surface has grown exponentially due to several factors. The shift to remote working during the COVID-19 pandemic significantly increased the number of devices and networks that could be targeted by cybercriminals. The rise of connected devices further expanded this exposure, as reports suggest that 18.8 billion connected devices will be in use by the end of 2024, with that number expected to grow to 41 billion by 2030.
In many organizations, the integration of operational technology (OT) and information technology (IT) has also posed significant challenges for securing attack surfaces. Devices ranging from manufacturing equipment to office appliances now need to be considered potential entry points for attackers, making the need for a comprehensive Attack Surface Management strategy even more critical.
The Role of External Attack Surface Management (EASM)
In 2018, Informer, an Attack Surface Management service, was launched to address the growing need for organizations to monitor and secure their external-facing assets. The platform scans and maps an organization’s digital footprint—including domains, subdomains, IP addresses, and cloud services—tracking these assets for potential vulnerabilities. The service also conducts penetration testing to simulate real-world attacks and provide valuable intelligence for securing an organization’s infrastructure.
Since its inception, ASM has seen tremendous growth. According to Straits Research, the ASM market was valued at $0.5 billion in 2021 and is projected to reach $1.4 billion in 2024. The market is expected to grow at a 27.7% compound annual growth rate (CAGR) through 2032, reaching $9.1 billion. This growth reflects not just the increasing complexity of attack surfaces but also the rising importance of ASM as a crucial part of modern cybersecurity strategies.
The Impact of AI on Attack Surface Management
AI as Both a Tool and a Threat
Artificial intelligence is rapidly changing the cybersecurity landscape. On one hand, AI is being used by cybersecurity professionals to enhance the effectiveness of ASM tools and better predict and mitigate threats. On the other hand, AI is also being leveraged by cybercriminals to accelerate their attacks and craft more sophisticated, targeted cyberattacks.
According to the 2024 Bugcrowd survey, 77% of ethical hackers are already using AI to assist in their hacking efforts, and 82% believe that the AI threat landscape is evolving too quickly to secure adequately. While AI can help automate many tasks, making penetration testing and vulnerability discovery faster and more efficient, some hackers believe that AI will never replace the creativity of human hackers. The ability to think outside the box and improvise remains a critical skill that AI cannot easily replicate.
However, AI can significantly speed up certain tasks. As one respondent from the Bugcrowd survey noted, AI is excellent for understanding error conditions in binary protocols, especially in areas where the hacker may not be as familiar. This makes AI a powerful tool in both offensive and defensive cybersecurity strategies.
AI’s Role in Shrinking the Attack Surface
As the landscape of cyber threats continues to evolve, ASM tools are increasingly incorporating AI to help organizations identify and mitigate vulnerabilities faster than cybercriminals can exploit them. With AI’s ability to analyze massive datasets quickly, security teams can gain a clearer, real-time understanding of their organization’s attack surface. AI-powered tools can also continuously monitor for changes in the digital footprint and rapidly detect new vulnerabilities that might otherwise go unnoticed.
By leveraging AI in the ASM process, companies can not only shrink their attack surface but also enhance their ability to predict and respond to emerging threats. This dynamic approach is crucial for staying one step ahead of adversaries who are also using AI to their advantage.
The Future of Attack Surface Management
The Growing Importance of EASM
As the number of potential entry points for cybercriminals continues to rise, external attack surface management (EASM) will become even more critical for businesses. The traditional focus on securing internal networks and systems is no longer enough. Companies need a comprehensive view of their entire digital footprint, including external assets such as cloud services, third-party providers, and mobile applications. By understanding and securing these external-facing assets, organizations can significantly reduce their exposure to attacks.
The increasing complexity of IT environments, driven by IoT, remote work, and AI, will continue to push the demand for robust ASM solutions. As cybercriminals become more sophisticated in their attacks, businesses must adopt more proactive, AI-powered tools to stay ahead.
Building a Resilient Cybersecurity Strategy
For organizations to effectively protect their digital infrastructure, they need a comprehensive cybersecurity strategy that incorporates not only traditional defense mechanisms but also cutting-edge ASM tools and processes. This includes ongoing vulnerability assessments, penetration testing, and continuous monitoring of external-facing assets. By leveraging AI and crowdsourced security (through ethical hacking programs), businesses can gain a more nuanced understanding of their attack surface and make data-driven decisions about how to allocate their cybersecurity resources most effectively.
Conclusion
The growth of the digital attack surface presents a significant challenge for modern organizations, but it also opens new opportunities for innovation in Attack Surface Management. As businesses continue to adopt new technologies and expand their digital footprints, they must remain vigilant in securing their assets and minimizing vulnerabilities. AI, when used effectively, can play a crucial role in both identifying and defending against emerging threats, but it is important for organizations to balance AI-driven tools with human creativity and expertise. The future of cybersecurity lies in the integration of advanced technologies with proactive, continuous monitoring to stay ahead of evolving threats and safeguard against the ever-growing attack surface.
