AI and ML: Driving The Future Of Pen Testing/VAPT

The CAGR for the global pen-testing market is anticipated to be approximately 14.2% from 2018 to 2027. By 2027, it's anticipated to increase to about $2.6 billion.  - SourceThe predicted period will see growth in the global pen-testing market as a result of an increase in cyberattacks and a greater need to comply with compliance requirements. Therefore, a VAPT testing company must maintain a close eye on developing cybersecurity trends in order to defend against such hostile attempts. Some of the VAPT tools commonly used by companies are OWASP, WireShark, Nmap, Metasploit etc.

Penetration Testing, AI, & ML 

The fields of software development or security testing have seen a boom in the use of machine learning. In all honesty, you can find it in practically every software you find on the market. Many businesses are investing money in having software with inbuilt machine learning.  

We encourage you to reconsider your assumptions if you think that the field of software testing is immune to the wonders of machine learning. Many individuals are still unclear about use of machine learning in software testing.

We have some fascinating information for you if you are unclear about the function of machine learning in penetration testing. 

• Better Performance  

Since machine learning has the potential to improve software performance, it is now a component of all penetration testing software. As time goes on, it allows to pick up on new market patterns and past mistakes for improved selections. Once your testing tool or software has tested a few pieces of software, you can be confident that the following time, it will work on problems more effectively.  

• Efficient At Catching Issues  

VAPT tools that uses machine learning are much better at identifying bugs and other problems. Every problem discovered is recorded in the inventory (memory) allowing tracking of anything that goes wrong or doesn't behave as expected. By doing so, individuals develop the ability to identify problems quicker and implement effective adjustments, 9 out of 10 times.  

• A Decrease In Delivery Time  

It has been noticed that penetration testing organisations utilizing software and tools with machine learning embedded deliver projects far more quickly than businesses using traditional testing methods.  

Pen testing tools with machine learning and artificial intelligence skills can plan better, find vulnerabilities more quickly, and resolve issues much more quickly than standard tools. This makes it simple for these tools to finish the process more quickly so the business may concentrate on other initiatives. Thus, security testing pleases the customers and encourages the business to improve.  

• Improved Reports  

Making a quality report in the conclusion is one of the key tasks of penetration testing. The vulnerabilities discovered, the techniques taken to find the vulnerabilities, and the fixes should all be stated properly in this report.  

The development team can see and understand where they went wrong and what they can do to set things right with the help of all three of these. Nearly every second penetration testing business struggles to provide its clients with the best results, and the development team is dissatisfied with the reports they have gotten. However, these same businesses have reported increased customer satisfaction thanks to ML-enabled technologies.  

Applications Of AI And ML In VAPT 

Information Gathering And Reconnaissance 

During the information gathering and reconnaissance phase of penetration testing, testers attempt to learn by gathering data from publicly available sources while identifying the ports and services that are open.  

Following the procedure, a report is generated for containing details such as domain names, target hosts, services enabled, technologies in use, employee names, email addresses, physical locations, images of the physical locations, prospective usernames, and passwords, etc.  

AI and ML can assist the pen tester in gathering all the information automatically, analysing it, and choosing amongst several courses of action. Alternately, it might be used to pinpoint the target hosts that should be targeted first since they have a higher chance of succeeding. 

Vulnerability Assessment / Scanning 

In this stage of pen testing, we carry out more thorough vulnerability scans in an effort to find every potential weakness that the targets might have. Here, AI and ML feed on data gathered to help testers comprehend the scan results by analyzing & filtering.  

Exploitation  

In this stage of pen testing, the attempt to obtain access to the systems are made along with lateral moves while aiming at increased privileges and retain persistent access. Also, AI and ML can help by selecting the most effective strategy for breaching a target. The outcomes of these exploitations can be sent back to the AI model, enabling it to produce additional exploitation alternatives or previously unconsidered exploitation pathways.  

Deep Exploit  

It is one of the open-source solutions that combines the execution of the first three phases of this methodology, is already available on the market. 

This fully automated penetration testing programme makes use of machine learning to improve both the information-gathering process and the vulnerability-exploitation process.  

Reporting  

At reporting stage, all the details and data fetched during the test process is streamlined to create reports of: 

• Issues discovered 
• Risk Implications 
• Suggestions 

Furthermore, the data gathered is assessed using threat intelligence to derive actionable insights with the help of AI.  

AI /ML Collaboration With VAPT Platforms 

Both Artificial Intelligence and Machine Learning have potential to automate and streamline tasks that cybersecurity departments need to focus. Some of the most significant activities that AI technologies and machine learning products could help resolve includes: 

• Automated endpoint security 
• Patch management 
• Improved supply chain security 
• Added visibility on IoT operations 

More importantly, AI & ML delivers all the power to prioritize zero-trust security along with identity access management, micro-segmentation, and other important tasks.  

Also Read: Improving Quality Assurance Using Artificial Intelligence

AI & ML Reducing Cyberattacks 

AI & ML have become a significant part of the tech research and implementation. And therefore, they are being used quite impressively in reshaping the digital security and overcome the growing number of cyberattacks.  

Wondering, how AI And ML Are Boosting The Security Standards? 

To mention, some of the most widely adopted and implemented practices that AI & ML have showcased in strengthening security against cyber-attacks include: 

1. Fraud & Anomaly Detection to work on recognizing complicated scam patterns. 
2. Vulnerability management through identification of code vulnerabilities and attack prediction. 
3. Botnet detection to overcome sophisticated bot attacks. 
4. Anti-malware to identify any existing or new forms of malware. 
5. Data-Leak Prevention 

Concluding it all, AI/ML technologies have everything that is needed to gain security against cyberattacks. Though some ethical issues are often found circled around the technologies, taking legitimate actions could not create solutions that abide with defined regulations but can even improve the security of the virtual landscape.  

However, it is extremely necessary that an effective implementation of the AI and ML solutions must be done to strengthen the security practices. The process needs effective development, deployment, and most importantly creating solutions that are driven with security testing services.  

Find out how security testing could aid your long-term digital goals. 

Speak with a member of our cybersecurity team by calling at 8307547266.