What new trends are emerging in penetration testing methodologies?

AI and machine learning. Cloud and container testing. Purple teaming. DevSecOps integration. Bug bounty programs. Here's what else to consider.

Customers, specially in Telecom domain are asking about, real-time security vulnerability testing, simulating as much as possible a real situation. Using established regression test cases is no longer appealing them. Another trend is that these customers want to see penetration testing results categorised in to specific areas, such as IoT security testing, API security testing, container security testing, etc., Further, while no customer has asked it so far, some companies are making it as their competitive advantage talking about Behavioral Analytics and Threat Simulation and Bio-metrics security testing. The better we provide visibility to customers on robustness of our systems against security vulnerabilities, more their confidence

There are several new trends emerging in penetration testing methodologies. One of them is the increased use of automation and machine learning in the testing process, which can help identify vulnerabilities more quickly and accurately. Another trend is the focus on testing cloud-based systems, as more and more organizations are moving their data and applications to the cloud. Additionally, there is a growing emphasis on testing for security in the Internet of Things (IoT) and mobile devices. Finally, there is a trend towards more collaborative and integrated approaches to penetration testing, with teams working together to identify and address vulnerabilities.

One thing I have been facing recently is the extensive use of Generative Pre-Trained Transformers or Large Language Models which have been trained on specific business suggested datasets. This forces the penetration testers to invest in skills such as prompt engineering and jail-breaking AI prompts.

AI and ML can drastically improve the speed of certain penetration testing tasks. For example, vulnerability scanners enhanced with ML algorithms can sift through data faster than a human and can learn from past scans to improve future performance. However, AI/ML algorithms can sometimes be prone to false positives or negatives in their analysis. While they can process vast quantities of data, the accuracy of their conclusions can be as much about the quality of the data and the training as about the algorithms themselves. However, combine this information with the knowledge and skill of a penetration tester and the magic begins.

It is not trivial to learn (and keep up with) the specific features and settings of different cloud platforms and container tools Knowing who, what is responsible in the cloud security model. As a penetration tester, you already have to work hard to keep up. With the different cloud platforms, and their evolution, the challenge becomes greater.

Such generic answers don't help answer the question. Container testing is critical, but there are so many different solutions to this problem. I would try something more radical and suggest confidential computing. data exists in one of three states – at rest, in motion, or in use. there are ways to protect data in all three states, even "in motion". if a breach does occur, the haul will the useless to the attacker.

As a penetration tester and red teamer, my experience has been that purple teaming hinges on effective communication and collaboration between the red team (the attackers) and the blue team (the defenders). The goal is for both teams to openly share information about attack techniques, defense strategies and findings to improve the organization's security posture. This collaboration leads to better understanding of vulnerabilities and strengthening defenses against real threats. Therefore, to truly succeed, you must be able to admit that you don't understand or have overlooked something. If both teams can be truly sincere and honest, only then do you have a good foundation on which to start.

A Software Bill of Materials (SBOM) is essential to DevSecOps because it provides transparency and visibility into all components used in a software project. This enables teams to quickly identify vulnerabilities, respond to security incidents and meet compliance requirements. An SBOM allows for detailed assessment which is critical to ensuring the security of the software supply chain. In addition, an SBOM facilitates the integration of security into the early stages of the software development lifecycle by enabling automated scans and audits within CI/CD pipelines. This contributes to a "Shift Left" approach to security resulting in more robust and secure software deliveries. As such, it is a foundation for the success.

Think of your security as layers of defense. DevSecOps is thought of as shifting security left, but that is just one part of it. By doing code auditing during the development process rather than just your red team finding externally visible vulnerabilities, you’re more likely to catch problems deep within your app earlier rather than what floats to the surface. It also can preemptively catch issues before they surface. Both are necessary and complementary. Additionally, DevSecOps is a culture that encourages secure development from the beginning rather than making it an afterthought.

think outside of what everyone else is doing. the 10th man principle applies here. when everyone agrees on everything, it is the duty of the 10th man to disagree. there are steganographic methodologies which can completely cloak your entire infrastructure and only reveal itself to invited and trusted parties.