How to Leverage Machine Learning for Improved Penetration Testing
How can integrating AI and ML into the penetration testing process can boost the sustainability of your business's cybersecurity?
As we have entered the digital age with the advent of high-speed internet, cyber hacking has tailgated us stealthily. Even though most cyber hackers continue to operate in a clandestine manner, the impact of their actions is starting to get tremendous mainstream attention. This added time in the spotlight is primarily due to the steep rise of cyberattacks over the past few years, leading to revenue loss and reputational damages amongst brands.
Whether it’s a small business or a big corporation, the looming threat of cyberattacks is ever present. There can be multiple reasons for this increase in cyber security incidents in the last few years. So, businesses are trying to find out a way to reduce the frequency as well as the impact of these security breaches.
Machine learning can help in reducing cyberattacks
Typically, cyberattacks happen because companies fail to assess vulnerabilities in their systems and are unable to predict an attack. One of the primary reasons for this failure is the human factor, which is also considered a critical weak point in an organization’s information security.
When it comes to cybersecurity, using machine learning and AI to minimize the human factor can be a real game-changer. One way of doing it is by integrating AI and ML into the penetration testing process to boost the sustainability of your business’s cybersecurity .
How to leverage ML & AI to raise penetration testing standards?
Performing penetration testing is very crucial to ensuring a robust cybersecurity network for an organization. Cyber security experts use both manual and automated pen-testing tools to validate the resilience of a software application.
Manual testing is predominantly human driven. However, even in automated penetration testing, the human factor tends to be considerably high and is critical for ensuring that the process is a success.
In most cases, automation only eliminates redundancy and does not get into the cognitive aspect of testing. As a result, the test’s efficiency depends more on the testing experts’ skillset and conviction than on the effectiveness of tools.
However, the integration of ML and AI into the automated penetration testing process also brings cognitive automation into the picture, thereby considerably reducing human dependency. In addition, using machine learning for penetration testing can transform the endpoint by adding accuracy and contextual intelligence.
Here is how it is usually done:
Security testing follows a basic multi-stage methodology, from reconnaissance to post-exploitation reporting stage. Embedded AI and ML can help QA teams deliver better value in each stage, making the pen-testing process more reliable and secure.
Let’s have a look at how ML integration in penetration testing helps each of these stages:
1. Reconnaissance or footprinting
It is one of the initial stages, during which understanding the target is the primary goal. Several passive methods are employed to gather all publicly available information, thereby increasing the odds of successfully penetrating the system.
Traditionally, testers use manual methods to extract publicly available information about the target. However, combining AI and ML in this stage not only automates the process but also delivers better results and saves a lot of time and resources.
The amount of information that is accumulated in the reconnaissance stage is usually enormous. Moreover, all of this information also needs to be timely scanned to identify the potential vulnerabilities.
Performing this task manually can prove to be an impossible feat, which is why automating this scanning process is essential. However, automating it without ML and AI can only reduce the time, without much improvement in terms of its efficiency and reliability.
ML-driven security scans filter out irrelevant data from the massive chunks of information. Using AI and ML can, in this stage, also help an organization focus on smaller blocks of actionable data that can provide reliable results.
The efficiency of ML is based on the phenomenon of leveraging historical data to make logical predictions. So, if enough data is collated from previous scans to educate the system, your ML-backed scanning can give you more reliable and accurate results.
3. Threat modeling
Here, a threat model is devised in the form of a structured representation by capturing, organizing and analyzing all the available information that impacts the application security system.
This process involves churning out a lot of information and making sense of it based on its impact on mission-critical and sensitive aspects of the application. Using the manual method to perform this task can be very challenging, so automating the process is crucial. However, automating it using ML and AI can also bring cognitive abilities into the process, thereby improving its dependability and effectiveness.
Integrating ML and AI into the process also helps organizations identify and predict potential suspicious activities while correlating them to generate valid alerts proactively and accurately.
4. Vulnerability analysis
Just like some spilled drops of fruit juice attract colonies of ants, software vulnerabilities attract cybercriminals from all over the internet. Sometimes even organizations’ security teams are surprised at how fast their vulnerability is detected, no matter how trivial it was. It happens primarily because now even cyber hackers use AI and ML to identify weaknesses swiftly and accurately.
In this context, using AI and ML in the vulnerability analysis stage is the most crucial.
Here, all the available filtered-out information is used to identify potential vulnerabilities and segregate them based on their severity levels. Embracing ML-backed analysis, enterprises can implement context-based vulnerability risk scoring to prioritize their optimized remediation efforts.
With limited resources available with the organization, enabling ML and AI in the vulnerability analysis process can reduce cost while increasing reliability.
In this stage, the actual attack is initiated. Here QE engineers take control over multiple network devices and launch numerous attacks on the system.
Since there can be multiple vulnerabilities in a system, manually attacking it from several fronts can be very challenging. However, with ML and AI, these attacks can be swiftly executed using web application attacks such as SQL injection, cross-site scripting and backdoors to exploit the target software’s vulnerabilities.
QA testers capitalize on these vulnerabilities by escalating privileges, intercepting traffic, stealing data, etc., to check the amount of damage that can be done to the system.
The way ahead
With every passing day, the number, as well as the sophistication levels of cybersecurity threats are increasing. To cope up with the dynamic situation, businesses also need to modernize their complex security networks.
In this regard, AI and ML can take center stage even on the cybersecurity front, thereby alleviating the exploding burden of organizations’ security management teams. It also helps businesses combine the benefits of intelligent decision-making and automation, thereby significantly reducing the triage pain.
However, as compared to integrating AI and ML in the usual operations, combining them in the security testing process is a different ball game together. Therefore, enterprises usually need outsourcing their security testing process to expert quality engineers.
Contact us for assuring your software and application security to ensure a robust, quality-tested end-product.