After countless attacks across a multitude of organizations, the cyber security industry has a fairly good grasp of how adversaries work, the vulnerabilities they take advantage of, and of course, how to prevent these attacks from causing serious damage. Despite all this information, the cybersecurity industry continues to rely on detect and respond – an endless game of cat and mouse where developers see the vulnerabilities in their code and spend countless development cycles attempting to address the issue before another one pops up.
Fortunately, with the advent of AI-based solutions, application security teams have a path forward and this begins at the code level. Today, security teams have access to some of the most powerful AI solutions but to be effective, it is critical teams leverage solutions that can contextualize cybersecurity vulnerabilities, look at code through a holistic lens and understand how vulnerabilities interact with the entirety of your application.
The power of context
For developers to get ahead of cyber threats, they must look beyond simply deploying a fix and understand how attackers take advantage of their application’s vulnerabilities. To retrace an attacker’s steps, developers need context – valuable information that reveals potential attack vectors and how deeply rooted an issue could be. Critical information such as data flows, control dependencies and other critical details can all be revealed with a Code Property Graph (CPG).
CPGs can be described as a unified representation of software systems that combines various aspects of code. Think of a CPG as a roadmap of your application’s code. Picture a map application on your phone and a destination you want to reach. When you plot your current location and the destination, you are presented with a map with rich contextual information. Information about accidents, roadwork, traffic and more. All of this enables you to take alternate routes and change your travel plans but none of this would have been possible without the contextual information that the map provides.
From an attacker’s point of view, they look at an application’s map to determine the best route to success. With a CPG, application security scanning tools can digest your application’s code, map it out and provide information on API points, points in your code that interact with caches and other points of access that can be abused. All this information enables application security teams to find weaknesses and classify parts of the map as vulnerabilities that need to be addressed.
Keeping developers in a flow state
What is exciting about the graph-based nature of CPGs is that you can easily apply machine learning and deep learning techniques. This turns scanning solutions into a powerful tool that can help uncover hidden patterns within code, predict vulnerabilities and provide context that legacy application security tools might overlook. But what does this all mean to a developer?
In a recent study of over 1000 developers, developers are spending up to a third of their time chasing vulnerabilities and fixing bugs instead of writing code. A staggering 38.5% also indicated that they spend up to 60 minutes a day searching for solutions. Cyber attacks continue to be a plague and developers are being asked to do more with fewer resources and greater time restraints.
For IT teams to remain competitive in this constantly evolving cyber threat landscape, they will need every tool at their disposal to help keep them focused on what matters most – writing secure code. So how do we immunize applications against emerging cyber threats while ensuring the productivity of IT Teams?
The perfect remedy could lie with the advent of AI solutions. By integrating AI capabilities into application security tools, organizations can dramatically increase productivity across development teams. With the appropriate AI tool, developers can take an issue that could have required two to three hours to fix into a simple 5-minute scan where the vulnerability is identified, contextualized against the entirety of your application and addressed with an automatic code suggestion leveraging generative AI.
AI and the develope
AI solutions are powerful but what is important to remember is that these solutions are not meant to replace the skills of developers and should be approached as complementary assets that assist your development teams to be more efficient and productive. A human element will still be required to not only guide the overall direction of code development but also ensure the final application coherently comes together while also supporting business goals.
About the Author
Chetan Conikee is the Founder and CTO of Qwiet AI. He is a serial entrepreneur with over 20+ years of experience in software engineering. His expertise includes building web-scale distributed infrastructure, virtualization and machine learning. He was most recently Chief Data Officer and GM Operations at CloudPhysics. Prior to CloudPhysics he was part of early founding teams at CashEdge (acquired FiServ), Business Signatures (acquired Entrust) and EndForce (acquired Sophos). Chetan earned his M.S. in Computer Engineering from Iowa State University and B.S in Computer Science and Engineering from Bangalore University.
Chetan can be reached online at X – @conikeec and at our company website https://qwiet.ai/
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