appCD this week launched a namesake platform that analyzes an application about to be deployed to automatically generate the code needed to provision the IT infrastructure required.

Fresh from raising $6 million in seed funding, appCD CEO Sachin Aggarwal said that approach makes it possible to consistently create secure code for provisioning IT infrastructure versus requiring developers with little to no cybersecurity to manually write it.

Instead, appCD reviews Python or Java code to understand intent, identifies dependencies and then infers application programming interface (API), service configuration, ingress/egress and other environment variables. Terraform code or Helm charts are then automatically generated based on the static analysis of the code that has been reinforced using artificial intelligence (AI) learning techniques.

That approach also makes it simpler for platform engineering teams to centrally manage the provisioning of IT infrastructure using a set of pre-defined policies that are automatically implemented each time an application is deployed to ensure security by default, he added.

DevOps teams can also use appCD to visualize the deployment environment before and after an application is deployed, said Aggarwal.

While infrastructure-as-code (IaC) tools have accelerated the rate at which cloud infrastructure can be provisioned, they are also often the root cause of the security issues that plague cloud computing environments. Developers often misconfigure cloud infrastructure by, for example, inadvertently leaving ports open. The appCD platform reduces the potential such mistakes will be made, noted Aggarwal.

That capability also eliminates the need to attach cybersecurity specialists to a DevOps team because issues are automatically resolved by the guardrails enforced by appCD, he added.

In theory, at least, there may soon come a day when AI routinely improves the quality of the code being used to both build and deploy applications. The more the code used to train a large language model (LLM) is vetted, the more reliable the output becomes. In the meantime, platforms such as appCD are combining static analysis with AI reinforcement learning to achieve that same goal in a use case that is specific to generating code to provision IT infrastructure.

Each DevOps team will naturally need to determine the best path forward, but as regulations pertaining to securing software supply chains become more stringent, it’s only a matter of time before organizations will be required to embrace DevSecOps workflows. In the meantime, DevOps teams should assume that given the probability cloud infrastructure has been misconfigured, it’s all but certain there will be multiple cloud security incidents. In fact, cybercriminals are actively scanning for misconfigurations that they have already created playbooks to specifically exploit in a matter of minutes.

In the absence of being able to prevent all those issues from arising, the immediate priority becomes remediating as many issues as possible before cybercriminals exploit those vulnerabilities. However, it’s also important to draw a proverbial line in the sand to ensure that all cloud infrastructure being provisioned in the future is much more secure than it has tended to be thus far.