How AI Aims to Revolutionise the CyberSec Arsenal

As humanity advances, so does our reliance on an expanding array of devices and technologies. With each passing day, new devices, systems, and applications emerge, driving a relentless surge in demand for robust data storage solutions, efficient management systems, and user-friendly front-end applications. This rapid pace of technological evolution mirrors the exponential growth of the human population and our insatiable thirst for innovation and convenience.

From smartphones and wearables to IoT devices and cloud infrastructure, the breadth and complexity of our digital ecosystem continues to expand at an unprecedented rate. This necessitates continuous adaptation and innovation across various verticals, from data management and cybersecurity to software development and user experience design. As we navigate this ever-evolving landscape, the need for scalable, agile, and resilient solutions becomes increasingly paramount, ensuring that we can effectively harness the power of technology to address the challenges and opportunities of the modern world.

With all these areas spreading their poised feet into the digital era of human transformation, the number of vulnerabilities and open doors to bypass the devices to reach the backend servers, manipulate data, exfiltrate information, compromise systems, and harness all the critical information spread across the deep and dark web becomes prominent. Thus, the need for cybersecurity and leveraging artificial intelligence to generate stronger weapons for defending the ever-under-attack walls of digital systems.

Let’s talk about strengthening the four major pillars from an attacker’s perspective, as they form the core of any organisation’s security:

Source Code Analysis Tools

SAST is one of the most widely used cybersecurity tools around the world. Yet, a common issue faced with almost all of them (including commercial ones) is a super-high number of false positives. This is primarily due to factors such as:

• Lack of real-life data: The source code of most organisations is proprietary, and the tool itself is not allowed to collect any insights from it. Insights can be particularly useful, like which code snippet was falsely marked as vulnerable or which vulnerabilities were missed. The absence of real-life scenarios doesn’t let the tool evolve.

• Limited support of languages: While programming languages keep evolving with new versions, upgrades, and extensions, it is difficult for the OEMs of SAST to keep up with such progress. There are therefore a very limited number of languages supported, with even lower support of evolving packages.

• Non-curated solutions: The most challenging but lucrative feature of a SAST can be to evolve as per the patterns of an organisation’s code. Every organisation follows some coding practices and guidelines. Also, most of them have a set of secrets, variables, and redundant strings in the code. Having a SAST tool that identifies the common pattern of bugs in developer code and curates (let’s say) training sessions, or (even better) looks out for those vulnerabilities more thoroughly and with stricter rule sets, can very well prove to be a game-changer.

With Generative AI entering the arena, many practical applications, which seemed like a distant dream just a couple of years ago, are taking shape. SAST is no different. In fact, many organisations have internally acknowledged the challenges listed above and started to integrate supervised learning models with their offerings.

With a powerfully integrated AI model, the modern SAST can be expected to have:

• Company-specific rule sets and secrets detection. The model gets more refined and produces fewer false positives as it is used over time.
• Inclusion of further programming languages, with the ability to be trained by developers of each organisation with minimal effort.
• Submission of insights learnt from every model installed in every organisation, getting better every day without collecting the proprietary code.

Automated Application Scanning Tools

Again, a wide set of pen testing tools fall under this umbrella (both open source and commercial). These are sophisticated tools because of the number of tasks they need to execute, and based on the technologies with which they need to be compatible for fluent running. Some of the best automated security scanners have millions of lines of code and are always under development, bug fixes, and compatibility updates, since they need to match with ever-evolving technologies, platform advancements, language adaptations, and security guidelines.

Modern-day automated application security scanners can perform logins, record macros, do request throttles based on server responses, identify vulnerabilities, and exploit them via hundreds of different techniques. Yet, even if we run the same tool on 100 different applications, the tool hardly ‘learns’ from each test!

This is where AI is going to create an impact. With each application tested, the model will be made to learn the mistakes developers are repeatedly making, the capability to bypass CAPTCHAs/firewalls, reduce noise by eliminating impossible test cases as per the environment, tailor the payloads to suit the environment, and most importantly, learn from every assignment about what was a false positive and what was missed. Further, we can train the model to generate graphs and pointers for management to look at the most common vulnerabilities, their impact based on the severities, as well as financial impacts to the organisation.

This massive shift in the DAST sector of cybersecurity, while evolving the way the current tools work and generate reports, can change the complete lifecycle of development, generate a coding practice viable for all to adapt for adequate security for the organisation, and evolve with it to make all advancements secure.

Red Teaming Weaponry

Red teaming in cybersecurity represents a dynamic and comprehensive approach to assessing and enhancing an organisation's security resilience. It involves the simulation of sophisticated cyberattacks by skilled professionals, often referred to as red teams, who emulate the tactics, techniques, and procedures (TTPs) of real-world adversaries. Unlike traditional security assessments that focus on identifying vulnerabilities and patching them, red teaming goes beyond by examining the effectiveness of an organisation's people, processes, and technology in detecting and responding to cyber threats.

Talking about the tools used in red teaming by different organisations across the globe, there are plenty of red teaming tools, and interestingly, most of the good ones are open source. There are tools that help in lateral movement, mapping the directories/domains, privilege escalation, enumeration, or for any of the 2,000 possible attacks in red teams!

Amalgamated with the capabilities of AI, we can expect the tools to bypass AMSI and AV tools with greater ease, owing to the capabilities to create custom bypass scripts. We can also expect tools with even stealthier approaches in the near future, since detection simulation can be tasked to AI to continuously improve the ninja factor! Also, changing of script signatures, juggling function names, smuggling data out of machines, and tampering logs creatively are some jobs that we can reliably delegate to AI.

Reverse Engineering Tools

In the realm of software, reverse engineering typically involves disassembling or decompiling executable code to extract information about its source code, data structures, and algorithms. This practice is employed for various purposes, including understanding legacy systems, interoperability between different software components, identifying vulnerabilities, and detecting malicious behaviour.

Reverse engineering tools are used in the identification of application behaviour to create mods, malware detection, feature enhancements, and exploitations like overflows. Most of the commonly used tools in reverse engineering are free yet basic in terms of functionality and assistance. The challenge remains that every application has a different architecture and codebase, and that no static universal rule can be created for hacker assistance.

AI can be a game-changer by assisting in pattern detection to ascertain malware, applying breakpoints using best guesses on the behaviour of the application, finding overflows, and performing overflow simulation. AI-powered static and dynamic analysis tools can automatically identify functions, variables, and control flow within binary code, helping reverse engineers to understand the behaviour and structure of software applications more rapidly. By harnessing the power of AI, reverse engineers can accelerate the discovery process, uncover hidden insights, and ultimately enhance their ability to understand and reconstruct complex systems more effectively.

Conclusion

Thus, artificial intelligence is a game-changer that can help increase the robustness of cybersecurity and enhance detection and response capabilities to a high level. These advancements are going to reduce the time taken by individuals in manual analysis and help in automating many functional processes. 

However, human interactions with such tools will remain a must, since logical errors, business-critical vulnerabilities, false positives, enhancing the models, and reviewing each vulnerability will still require intelligent minds.