In my 1x1 conversation with an esteemed colleague today, we delved into a pivotal question: "Sunil, where should SAP invest in AI Security research?" My immediate response? The arena of code validation.
In the intricate landscape of software development, the tasks of checking and validating code present substantial challenges. Picture yourself as a team lead, adept at deciphering complex narratives and understanding the unique styles of different developers. However, when confronted with legacy or unfamiliar code, it's akin to navigating a maze – far from inviting.
Now, envision a future where machines generate thousands of lines of code in seconds, each infused with complex logic and algorithms. The pressing question: Can we review this machine-generated code with confidence?
I proposed three essentials: Clear annotations, Test-driven design, and Modularization by breaking down AI-generated code into manageable parts. Consider these as fundamental tools in a developer's arsenal, aiding in navigating the intricate landscape of machine-generated code. But, let's not stop there.
Reflecting on my early days as a product engineer in a CPU manufacturing company, I pondered the prospect of emulating structural tests, implementing scan-tests, and integrating built-in self-tests. Drawing inspiration from the integrated circuits domain, the integration of formal-verification methodologies emerges as another potent strategy to ensure the structural integrity of AI-generated code.
As our dialogue unfolded, it morphed into a brainstorming session. We need practical solutions that transcend the routine code-checking procedures and explore new realms where machine capabilities and developers' knowledge intersect. The trajectory of AI Security research calls for a meticulous examination of code validation intricacies. It's not merely about addressing today's problems; it's about ensuring that reviewing machine-generated code becomes straightforward and reliable in the future. The journey has commenced, leading us towards a realm where the blend of human insight and machine precision sparks exciting advancements in AI Security.