Understanding Attack Surface Management and AI in Modern Cybersecurity

The Rising Importance of Attack Surface Management (ASM) in Modern Cybersecurity

In today’s digital landscape, attack surface management (ASM) has become one of the most critical elements of a strong cybersecurity framework. In 2024, the ever-evolving threats from criminal hacker groups have pushed companies to adopt advanced tools and frameworks to prevent, detect, and respond to cyber risks. Managing the attack surface is now fundamental for organizations aiming to mitigate risks across complex and dynamic environments.

Let’s dive into why ASM is crucial and how AI-driven solutions, such as those from Darktrace, can significantly improve an organization’s ability to respond to emerging threats.

Understanding the Attack Surface

The attack surface of a business includes all possible entry points where attackers could gain unauthorized access to systems and data. As companies adopt more digital tools, move resources to the cloud, and increase their use of APIs for third-party integrations, the attack surface expands.

Here’s where ASM becomes a key player. By understanding and controlling all potential entry points, organizations can protect assets more effectively. But with each addition to the tech stack or change in business operations, an attack surface grows and changes, making ASM a highly dynamic and often complex responsibility.

The Role of NIST in Attack Surface Management

To address the importance of ASM, the NIST Cybersecurity Framework provides a structured approach to securing information systems. This well-known framework outlines five key functions: Identify, Protect, Detect, Respond, and Recover, which serve as a guide for building a solid foundation of security practices.

For organizations looking to maintain robust ASM, NIST’s standards offer a clear path to follow. For example:

• Identify: Catalog all digital assets and potential vulnerabilities.
• Protect: Implement safeguards to secure these assets.
• Detect: Use monitoring tools to detect threats promptly.
• Respond: Develop processes for an effective response to incidents.
• Recover: Outline steps for recovery to maintain resilience.

Utilizing NIST as a benchmark, companies can create structured, effective plans to manage their attack surfaces actively.

AI’s Role in Modern Attack Surface Management

Artificial intelligence (AI) has emerged as a powerful tool in the realm of ASM. With its ability to handle and analyze vast amounts of data, AI allows cybersecurity systems to “think like an attacker,” according to Justin Fier, Vice President of Tactical Risk at Darktrace. By using AI, organizations can better predict, prevent, and mitigate attacks before they reach critical systems.

Key Areas Where AI is Beneficial in ASM

Darktrace’s approach to AI-driven cybersecurity offers valuable insights into several critical areas of ASM, such as:

• API Protection: APIs are often a prime target for attackers. With AI, companies can monitor and restrict access to APIs, preventing unauthorized or malicious use.
• Shadow IT Detection: AI can identify and neutralize shadow IT—unapproved applications and devices that can introduce security risks.
• Supply Chain Security: Attackers increasingly target supply chains to gain indirect access to organizations. AI solutions can analyze and alert teams to suspicious activity from suppliers and partners.
• Enhanced DEVSECOPS: Integrating security into development pipelines becomes more manageable with AI, which can pinpoint potential vulnerabilities in code and improve overall software development efficiency.

To explore additional resources on securing software development and integrating security into business operations, visit 10 essential tips to protect yourself from phishing attacks and The role of human error in successful cybersecurity breaches.

Why Telemetry Data is Critical

Legacy cybersecurity systems often generate large amounts of telemetry data, providing real-time information about network performance and security status. This data, once used mainly for manual analysis, is ideal for AI to identify patterns, predict future attacks, and automate responses in real-time. As Fier pointed out, telemetry data enables AI to process and analyze large volumes of data, allowing security teams to focus on higher-level threats instead of getting bogged down by data management.

Attack Surface Management and the Need for Collaboration

With the increase in complex threats, ASM cannot remain siloed. It must be part of a collaborative security approach, bringing in expertise from IT, development, and other departments to maintain a holistic view of vulnerabilities. Tools like Darktrace’s AI-driven solutions help create this integrated approach, but human involvement is crucial.

Darktrace’s Approach: Thinking Like an Attacker

Darktrace uses machine learning to create tools that “think like an attacker,” constantly probing and testing an organization’s defenses. This shift in mindset helps cybersecurity teams focus on potential vulnerabilities that attackers might exploit. Tools like these are instrumental in simulating real-world attack scenarios and evaluating an organization’s preparedness.

Regular Testing and Simulations

Proactively testing defenses is key. Running phishing simulations and red team exercises allows teams to assess how well they can handle attacks. Products like the Phishing Simulator can help with these kinds of tests, helping employees recognize threats and react appropriately. For example, security awareness training enables organizations to better educate their employees on recognizing phishing attempts and other social engineering tactics.

The Future of Attack Surface Management

With advances in AI, ASM is on the brink of even greater innovation. Future tools are likely to include enhanced behavioral analytics to catch unusual activities more quickly, along with improved integration with systems like SIEM (Security Information and Event Management) for faster threat response.

As companies prioritize ASM and integrate AI-driven tools, they also need to invest in ongoing employee training to close the gaps in human risk. To explore how this can be achieved, consider human risk management platforms like the Keepnet Human Risk Management Platform, which assess and track user behaviors to improve security awareness.

Editor's Note: This blog was updated on November 18, 2024.