Securing the Cloud-Native Software Supply Chain with AI-Powered Application Security

If there’s one thing to know about cloud-native security, it’s that it’s tough because cloud-native technology is complex. Not only are cloud-native applications and environments deeply complicated due to their many moving parts, but the software supply chains they depend on also include many discrete components – and, by extension, many potential security risks.

Meanwhile, if there’s one thing to know about AI security tools, it’s that they excel at helping to mitigate complex security tasks – including the various processes necessary to identify, triage, and remediate vulnerabilities within cloud-native applications, hosting environments, and software supply chains. The ability to automate processes like these is critical for DevSecOps teams, who have often struggled to manage cloud-native security risks efficiently and at scale.

To prove the point, here’s a look at how AI security capabilities can help teams conquer the complexity surrounding cloud-native security, resulting in more secure environments and less tedium for DevSecOps staff.

The challenges of securing cloud-native software supply chains

When we talk about cloud-native software supply chains, we’re referring to the set of packages, modules, and other dependencies that developers use to build cloud-native applications – meaning applications that are deployed using scalable, composable architectures.

From a security perspective, cloud-native software supply chains have traditionally been deeply challenging due to their inherent complexity. That complexity arises from the following factors:

• Multiple dependencies: A typical cloud-native application might depend on dozens of different libraries, modules, and so on. Each of those supply chain components could be subject to vulnerabilities that make the app insecure.
• Diverse dependencies: Cloud-native supply chains include many types of dependencies – libraries, application packages, and container images. This means that teams must test a variety of distinct types of software resources to identify security vulnerabilities.
• Complex internal architectures: By their nature, cloud-native applications use complex internal architectures. Typically, they include multiple microservices that interact with each other on a continuous basis. Due to these complex internal relationships, understanding which software supply chain security risks impact which microservices within a cloud-native app can be deeply challenging.
• Constant change: It’s common for development teams to deploy new versions of cloud-native applications (or of specific microservices within an app) on a frequent basis. Each update could include new or modified dependencies, which means it could also introduce novel security risks through the application’s supply chain.

To manage this complexity and mitigate risks across all layers of cloud-native apps, developers and DevSecOps teams must address what we call the 4Cs of cloud-native security: Cloud, containers, clusters, and code. It’s only through a holistic, multi-layered approach that businesses can keep modern apps secure.

Why traditional cloud-native security tools can’t keep up

Cloud-native applications are not new; they have been around for about a decade. In that time, vendors have introduced a variety of tools designed to help manage cloud-native security.

These solutions can certainly help to identify vulnerabilities and other risks within cloud-native software supply chains and cloud-native apps themselves. But their major shortcoming is that, too often, they require significant amounts of manual effort – which means that even when tools are effective at discovering risks, they still burden DevSecOps engineers and slow down processes.

For example, consider the process of securing code during software development. The traditional strategy was for developers to write code, then – at a later stage in the development lifecycle – use a cloud-native security vulnerability scanner to check for security risks in the code.

This adds a step to the development process, which can delay software delivery pipeline velocity. It also increases the amount of work that developers need to perform.

Securing the cloud-native supply chain with AI security tools

Fortunately, the advent of agentic AI and generative AI security solutions has made a better approach possible. Today, cloud-native security tools need not be limited to basic functionality, like identifying vulnerabilities and notifying teams. 

They can also leverage AI features to automate complex processes, such as:

• Running security scans automatically and in real time, directly within Integrated Development Environment (IDE) tools, before code is even committed to a repository. This streamlines the process of securing testing, while also reducing manual effort on the part of software engineers.
• Automatically remediating security problems where possible, a capability that further reduces the burden placed on developers.
• Performing additional automated scans on code within repositories post-commit, adding another layer of security verification prior to application release.

Offloading tasks like these to AI delivers multiple benefits. Not only does it save time and reduce DevSecOps toil, but it also speeds up the process of remediating vulnerabilities, which in turn means that it reduces the risk of delays to application release cycles, and of disruptions to users while security risks are in the process of being mitigated. It also helps minimize the risk that insecure software will run in cloud-native environments because teams can’t patch it quickly enough.

How AI can help with AppSec 

To be sure, AI security solutions can’t solve every cloud-native security woe, at least not on their own. There will always be complex edge cases – like obscure vulnerabilities for which no patch is available within the supply chain – that engineers will need to address manually.

But on the whole, AI promises to do much to enhance cloud-native application security processes, including those that involve the most complex supply chains, application architectures, and hosting environments. Expect AI to become a key component of the future of cloud-native security.

Securing cloud-native supply chains with Checkmarx

Checkmarx’s latest AI capabilities provide the features DevSecOps teams need to protect cloud-native software supply chains at scale. Using the Developer Assist AI agent available through the Checkmarx One platform, businesses can automate security scans for both pre-commit and post-commit code.