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How Artificial Intelligence Influence the Future of Penetration Testing in IT Security

How Artificial Intelligence influence the Future of Penetration Testing in IT Security

What does it mean when you proactively test applications or enterprise environments for detecting vulnerabilities? Indeed, you are performing an ethical hacking experiment to identify threats and vulnerabilities before it surfaces an attacker’s vision. The process is otherwise known as cyber security penetration testing or simply a pen test. Similarly, while probing for deficiencies in security controls or testing the resilience by performing false attacks, you are pen testing it for betterment.

Penetration testing is a significant element of the cyber security house and is at the forefront of any cyber strategy. Pentest isn’t that simple to execute – it requires resources with immense skills, exposure to the latest cyber happenings, and a good track record. That is where Artificial Intelligence (AI) comes to the big picture. Before we get into how AI could influence pen testing, let’s have a quick look at typical pen-testing stages and their significance.

Quick thoughts on Penetration Testing

  • Why do you require penetration testing services?

Amidst the growing sensation of technologies, methodologies used by hackers or attackers are getting evolved on the other end. The prime intention behind 90 % of attacks is the financial benefit that it connects. It could be in the form of data or some other critical assets that prove value before the attacker’s eye. A breach converged could cause serious downfalls for business enterprises, including reputational damage.
Many businesses are still far to gather the significance and rely on conventional security mechanisms. Highly skilled hackers typically know your control sufficiency and invade your built frameworks. Therefore, every enterprise must consider getting their systems/networks strained with penetration testing services.

  • Typical stages in penetration testing service

Generally, a pen-testing process get performed in 5 stages: –

1. Planning

Planning is the initial phase of a pen test where the test objectives are clearly defined, and the scope gets well outlined. The stage explores complete information on the target environment, understanding the operational figures and connected risks.

2. Scanning

Scanning refers to the phase where the tester will interact with the target focusing on uncovering vulnerabilities. The target is scanned by using command line and automated tools. Apart from automated scans, manual test cases such as fuzzing, intrusion attempts are also performed on target. This stage gives you information on the loopholes present in the target

3. Acquiring access

Once the vulnerabilities are spotted, the next phase involves exploiting the founded by gaining access. Tests get conducted to unleash the vulnerabilities towards the extremity.

4. Maintaining access

The next phase involves maintaining the acquired access to determine the persistence. It displays how long vulnerabilities can get used to achieving a persistent presence.

5. Report and revalidation

The next step is to collect evidence of the exploited vulnerabilities, review them and deliver the testing report. It entails all prioritized risks and remediation roadmap connected to identified vulnerabilities. After efficient patching efforts, they are subject to a revalidation process inspecting all closures.

Artificial Intelligence and Pen Testing

So, the next thing to look about is how Artificial Intelligence can drive pen testing to betterment? Clearly, Artificial Intelligence and Machine Learning capabilities have a lot to offer to the pen testing sphere. In fact, AI-powered pen-testing looks more efficient method that blends threat intelligence, behavioural analysis, and machine learning to validate vulnerabilities.

Many penetration testing companies are on the road to AI, improving the success rate of testing & business productivity. Both AI and ML turn out as extra fuel for deeper vulnerability analysis and exploitation. They help extract worthy information from services running on target systems and networks. Moreover, the incorporation with AI helps drive improved metrics, uncover network infrastructure and report results correspondingly.

To better understand the influence of AI on penetration testing, let’s focus on different areas where the capabilities foster their control.

Areas where AI influences Pen Testing

  • Information fetching phase

The most vital stage in network penetration testing is the information fetching or gathering phase. It is a known fact that if more information gets gathered, the higher the testing success ratio. The stage requires a considerable amount of time for sketching all relevant and required information.
While introducing AI into the phase, testers benefit from the time spent and push more accurate results. Testers leverage AI and other language processing techniques, gathering stretchy information on the target environment. It could be detailed employee learning, gathering information related to enterprise security posture or even the software or hardware components of systems/networks.

  • Vulnerability assessment/scanning

The vulnerability assessment or scanning phase demands complete scope and coverage. Manual scanning has limitations, considering the number of systems and interconnected components. In such a case, result interpretation is also a mighty task. AI technology helps adjust scanning tools codes that help flexible interpretation of results.

AI integrated into the phase can significantly save the time of pen-testers and the overall effectiveness of the process. The specific feature also contributes to test management and the creation of test cases. Likewise, the integration can have a positive yield while inspecting how a system reacts to intrusions.

  • Gaining & maintaining access

Gaining access means taking complete control of the network devices to launch an attack or extract the required. So, the pen testers need to ensure that systems are free from any further exploit. To do this, they inspect password protection strength and associated credentials. An AI-based algorithm capability here extracts various password combinations. It helps to check if the system allows any potential break-in.

Also, AI-based algorithms can be developed and designed to scrutinise user data, analysing various password trends and patterns. While maintaining access, different mechanisms are needed to ensure the security of pen testers. AI-based algorithms are helpful when you require the primary path to devices get sealed. These algorithms aid in detecting pathways, encrypted channels, hidden accounts, network channels etc.

  • Reporting phase

The phase delivers a detailed analysis report, entailing complete vulnerabilities, prioritized risk implications and recommendations. AI and ML have a vital role in this end too. It helps improve the reporting process by analysing obtained data from the assessment, combining it with threat intelligence and reckoning with previously gathered knowledge.

One thing that security testers don’t need is the lack of evidence left after a breach. In order to make this happen, AI tools have been on the rise and call. It helps to find hidden backdoors alongside various access points that weren’t left open in the target environment. All these findings definitely help the report fly in the proper pathway.

Difference between manual, automated and AI-powered pen testing

  • While manual testing stays a bit far from accurate findings, automated tests are likely to produce false results as well. But AI-based penetration testing proves more fruitful when accuracy matters.
  • If time is the deciding aspect, AI-based pen tests take an edge over automated tests and way ahead to manual ones. These algorithms could work for multiple systems at a time which is definitely a value for worth.
  • Manual testing goes only applicable when the test cases get executed once or twice. Likewise, automated testing gets applicable when tools detect vulnerabilities out of programmable bounds. On the other hand, AI practicality sticks to inspecting thousands of systems within no time and with limited resources.
  • In manual testing, investment in human resources is high, whereas in automated it hangs on to the latest tools. However, AI-based pen-testing help save investments hung to extra resources and improve efficiency amongst the used resources.

Final Thoughts

It is clear that the future of penetration testing services firmly sticks to AI, making test results more accurate and efficient. With AI-powered tools into the play, pen testers can directly concentrate on the development cycle itself, with utmost security on the other end. However, vulnerabilities are growing on alarming stats, and if not patched on time, could end badly for businesses. When poorly designed web applications, unpatched network environments, exploitable passwords, sophisticated threats and many more shoot before the security eye, let us see how AI would outcome these challenges ahead.