AI-Powered Threat Intelligence_ Enhancing Penetration Testing Strategies
Penetration Testing

AI-Powered Threat Intelligence: Enhancing Penetration Testing Strategies

When we discuss proactively testing our environment or applications to look for vulnerabilities ahead of a hacker, we talk about penetration testing or “ethical hacking” exercises. This concept is quite old. When you’re trying to find deficiencies in your processes and controls through simulations or cyber attacks, then you are performing a penetration test and this entails hiring a penetration testing company. By incorporating AI Threat Intelligence, you can further strengthen your defenses by identifying emerging threats in real time. Evolution of Pen testing The penetration testing practice has evolved with time from an entirely manual and burdensome process of which only a few people knew the art to now being a rather automated and much-propagated process. This goes hand in hand with the evolution of technology.  In the early days, most processes were done with a lot of computers, so it was quite efficient to conduct manual penetration testing. Later on, as computers multiplied and processes began to get automated, penetration testers were forced to automate their tools in order to cover more ground in a shorter period of time, thus faster detection of vulnerabilities. Now, we have reached a point where companies possess different types of technologies and hundreds of thousands of IP addresses. Therefore, it becomes more challenging for pen testers to check everything within a reasonable amount of time with precise results. That is why the use of artificial intelligence and machine learning has started to help pen testers get past these barriers. Artificial intelligence is described as the ability of a machine to perform tasks that simulate human intelligence. A subset of artificial intelligence is machine learning, referring to the concept that a system can learn and adapt without following specific instructions but as an alternative through algorithms and statistical models studying statistics to draw conclusions. Related Read: Impacts of AI on Cybersecurity Challenges with Traditional Penetration Testing Even though pen testing is a crucial part of cybersecurity, the traditional methods are often highly challenged in the following ways: Explore: AI-Based Application Penetration Testing and Its Importance Is AI Used in Penetration Tests? So just how can AI and ML support penetration testing? Let’s take a look and analyze the different phases in a normal penetration test assessment and determine where AI and ML can be used. There are several well-known methodologies and standards that can be used to perform penetration tests such as OSSTMM (Open Source Security Testing Methodology Manual), OWASP (Open Web Application Security Project), NIST (National Institute of Standards and Technology), PTES (Penetration Testing Methodologies and Standards), ISSAF (Information System Security Assessment Framework). But for a better-streamlined analysis, we will only mention the four stages of penetration testing in which Artificial Intelligence and Machine Learning will be applied:  1. Information Gathering and Reconnaissance – Through this phase of pen testing, we try and gather as much information as possible about our targeted system by bringing information from easily accessible sources into light to derive the open ports and services during this phase. At the end of this phase, we would have a dossier of our targets including information such as domain names, target hosts, services enabled, technologies in place, employees’ names, employees’ emails, physical locations, pictures of the physical locations, potential usernames and passwords, etc. 2. Vulnerability Assessment / Scanning – In this penetration testing phase, we do more in-depth vulnerability scans trying to determine all the potential vulnerabilities that the targets could have. Here, AI and ML could aid the pen tester in understanding what the scans report by analyzing and filtering out whatever is not relevant or produces noise, considering all the information extracted from the first phase combined with threat intelligence drawn from social media, open records, the deep web, dark web, etc. This will also enable AI and ML to determine the best course of action for the attack phase by correlating all gathered information and knowledge. 3. Exploitation – This is the phase of pen testing where we put into action everything that was planned before. Here, we try, among other things, to gain access to the systems, perform lateral movements, escalate privileges, gather more information, and maintain persistent access. As I mentioned previously, AI and ML can support by determining what is the best possible course of action to penetrate the target, and they can carry out the exploitation simultaneously. Their results can feed back into the AI model such that it creates exploitation alternatives or new exploitation pathways not considered up to this time. 4. Reporting – At the end of this stage, a comprehensive report inclusive of all details regarding the issues discovered, the implications of these risks, and recommendations are provided to the penetration testing client. AI and ML can bolster the reporting by processing the data that has been gathered during the assessment and linking them to threat intelligence and knowledge obtained in previous engagements to produce actionable insights applicable to the organization undergoing review. AI-Driven Tools for Penetration Testing Several AI tools are being developed to accelerate penetration testing: These tools assist ethical hackers in uncovering vulnerabilities faster and more accurately, improving the overall security of the systems.  Advantages of AI-Enhanced Penetration Testing AI brings with it a host of benefits for the penetration testing process: AI makes the penetration testing process significantly faster as it automates all repetitive tasks such as scanning for vulnerabilities. The Future of AI in Penetration Testing As AI continues growing, so does its scope of work in penetration testing. AI futures may involve the autonomous generation of test cases, predicting new cyber attack techniques, and continuously improving the ability to detect existing ones. Along with these factors, the expertise of human professionals and AI together will continue to protect people from emerging threats in the realm of cyber attacks. Also Read: The Evolution of Penetration Testing: From Manual to AI-Driven Approaches Why Do Pen Testing Certifications Matter?  There are several penetration testing certifications that have been recognized. Most require previous experience in systems administration