Traditionally, an audit independently examines records, processes and controls to verify compliance and assess financial and operational integrity.
In the modern world, such an approach should extend to the software development lifecycle (SDLC) – especially in the age of artificial intelligence (AI) or large language model (LLM)-assisted code. Chief Information Security Officers (CISOs) and their teams need proof that developers are producing protected products, because one in five organizations has experienced a serious security incident directly tied to AI-generated code. Getting to the root of the problems requires visibility into who is leveraging AI, what tools they are using and where AI-generated code is introduced into the SDLC. This is considered the ADLC: the agentic development lifecycle.
CISOs must feel confident that these tools are approved and safe. A thorough audit will identify specific AI-linked vulnerabilities, and which tools are causing the most issues. Even better, it will transform the information into action.
To be clear, AI/LLM-driven software development creates significant boosts in efficiencies and overall productivity. But it also introduces new, often unmanaged risks. Software vulnerabilities discovered “after the fact” will result in time-consuming fixes and reworks. Security and developer team leaders must work together to find an appropriate balance of efficacy, innovation and protection.
