Can AI Enhance Penetration Testing?

Penetration testing, or pen testing, is a popular method used to assess the security of a network or system by simulating attacks from potential hackers. However, traditional pen testing methods can be time-consuming and lack accuracy.

With the rise of artificial intelligence, one question arises: can AI improve existing pen testing methods and make them more effective?

In this blog post, we will explore how AI can enhance cybersecurity through pen testing, and the risks of using AI in this way.

This blog post was created with the help of our Managing Director, Ty Miller, who is a CREST-certified pen tester with over a decade of experience in pen testing and cybersecurity.

Pen testing involves simulating cyberattacks on a computer system or network to identify vulnerabilities that malicious hackers could exploit. It is often a tedious process that requires a high level of skill, time, and consistency. Using automated and intelligent tools can help make this process more efficient and effective.

AI-powered tools can analyze vast volumes of data, identify patterns, and predict potential attack vectors. This capability empowers testers to prioritize their efforts, focusing on critical areas where vulnerabilities are most likely to be exploited. Moreover, AI facilitates the creation of sophisticated attack simulations that closely resemble real-world scenarios, offering organizations accurate insights into their vulnerabilities and the potential impact of attacks.

The agility of AI-driven tools could facilitate more frequent and thorough testing, reducing the risk of undiscovered vulnerabilities.

However, given the early development stage of most AI tools, these benefits may not be realized for some time.

In the next section, we'll uncover the primary benefits of using AI in pen testing and the potential areas where this technology can be implemented.

Penetration testing tools are still in their developmental stages and have not yet reached maturity within the field. However, advancements are anticipated in the coming years as organizations continue to explore and refine their utilization.

According to Ty, here are some of the use cases where AI shows promising potential and applications:

1) Productivity and Team Augmentation

AI tools act as knowledgeable assistants, offering contextual guidance to human testers and enhancing team productivity. These tools streamline the process by providing insights and answering queries, thereby augmenting the capabilities of the testing team.

2) Reconnaissance and Gathering of Information

AI tools excel in collecting comprehensive information about companies, systems, and domains. Their capabilities facilitate efficient reconnaissance, aiding in the initial stages of penetration testing by providing valuable data for testers.

3) SOC and SIEM Applications

In roles such as Security Operations Center (SOC) and Security Information and Event Management (SIEM), AI tools alleviate the burden of manual data sifting. Ty notes that AI efficiently analyzes vast datasets, making informed decisions and suggestions, thus enhancing the efficiency of these critical security functions.

4) Performing Advanced and Customized Attacks

AI can tackle advanced attacks, such as those involving access control and business logic flaws, which are typically complex for vulnerability scanners. Moreover, AI's contextual understanding enables the creation of customized attacks tailored to specific organizational contexts, enhancing the depth and accuracy of penetration testing. For example for a car rental company, AI could generate attacks to rent a car for free.

5) Reducing False Positives

AI tools offer insights into vulnerability exploitability, prioritizing real business risks and providing contextual recommendations for patching vulnerabilities.

Very often, automation and AI are used interchangeably, but they are not the same.

We asked Ty to elaborate on the difference between AI and automation, and he provided insightful perspectives on how these technologies function and their respective roles.

"When you look at AI, most of it is around data analysis. And with LLMs, it's about asking a question and getting an answer back in a human, consumable way. Most tools that leverage AI are about providing insights in a human, interactive way. It allows us to drill down and ask questions which is a good use case for these tools."

"They don't tend to take action often but can give you the information you need to take action."

"Automation is separate from AI.", Ty clarified.

"It's about taking those manual tasks away from a human and doing those repeatable tasks or automating certain tasks to take the load off the human.

"AI helps make humans more productive with faster insights and acting as an assistant to the human while automation helps to take the tasks off the human. In the end, they make human testers more efficient but in different ways," concluded Ty.

While the integration of AI holds immense promise for enhancing pen testing practices, it also presents notable challenges and limitations that warrant attention. Understanding and addressing these concerns are crucial for maximizing the efficacy and reliability of AI-driven pen testing initiatives. Here are some of the key challenges:

1) Inaccurate and Erroneous Results

AI-driven pen testing tools may yield inaccurate or erroneous results, raising concerns about their reliability and trustworthiness. Ty cautions that Language Models with Memory and Search (LLMS) may occasionally "hallucinate" or generate false information, highlighting the need for cautious interpretation of AI insights. Given the current state of AI technology, blind reliance on AI-generated outputs may lead to suboptimal decision-making and pose risks to organizational security.

2) Financial Constraints

The development and deployment of custom AI tools tailored for pen testing purposes can be time-consuming and financially burdensome. The potential costs associated with utilizing these tools, may deter organizations with limited resources from fully embracing AI-driven pen testing solutions. Balancing the benefits of AI with the financial investments required remains a significant consideration for organizations seeking to leverage AI in their security practices.

3) Lack of Expertise

Penetration testing demands a high level of expertise and experience to effectively assess and mitigate security risks. AI tools while powerful, lack the nuanced judgment and contextual understanding inherent to human pen testers. The absence of human expertise may lead to AI tools returning information from untrusted sources or websites, potentially exposing organizations to unforeseen vulnerabilities. For instance, AI tools may lack the discernment to differentiate between trustworthy and unverified exploits, posing risks to client systems. Addressing this challenge requires ongoing efforts to bridge the gap between AI capabilities and human expertise, ensuring that AI-driven pen testing initiatives complement rather than substitute for human insight and judgment.

As we look toward the future of AI and cybersecurity, it is evident that the intersection of these two fields holds immense potential for enhancing digital defense mechanisms.

"At the moment, the value that's coming out of AI is primarily around additional guidance and efficiencies in context." says Ty.

In the future, as AI products get better, we're going to see that AI starts replacing some of the commodity penetration testing within the industry and the human penetration testers will take on more human-focused pen tests."

These human-focused pen tests are the ones that require human engagements like sitting down and having discussions with the SOC team, or being physically present on-premises for the test. Some examples include:

• Red-team and purple-team engagements, instances where it might be necessary to physically break into a building
• Wireless penetration tests
• Testing high-security environments

As time goes on, pen testing will get more and more automated and it'll provide better quality results for customers and greater coverage across their environment. At the moment, testing every single device and system within the environment is very difficult and costly.

While AI holds promise in enhancing penetration testing practices, it's clear that its integration must be approached cautiously. The combination of AI-driven tools and human expertise presents an opportunity to bolster cybersecurity defenses, offering efficiency and insights. However, challenges such as inaccurate results, financial constraints, and the lack of human expertise highlight the importance of careful supervision and management. The future of pen testing likely involves a hybrid approach, where AI complements human testers rather than replaces them entirely.