According to an October 2024 report from Gartner, 33% of enterprise software applications will incorporate agentic AI by 2028—up from less than 1% in 2024. This rapid growth underscores the transformative potential of agentic AI to streamline inefficiencies, automate repetitive tasks, and enhance operations across the enterprise. When applied through a multi-agent approach to application security—particularly across the software development life cycle (SDLC)—the value these agents can deliver is substantial.
As application complexity grows—driven by widespread adoption of open-source libraries, microservices, serverless architectures, containerization, and now LLMs as a core building block—engineering teams and AppSec leaders face mounting challenges in securing scalable pipelines that protect the business.
The key insight behind the need for a multi-agent network approach is that developers, security teams, and executives are all part of the same workflow—and share the same challenges, though each is impacted differently when issues arise.
Specifically, developers focus on identifying and remediating security issues early in the SDLC, ideally within their IDEs, or as part of their pull-request (PR) process.
AppSec and security analysts are responsible for orchestrating processes and enforcing policies from the point that code is committed through runtime in the cloud.
Meanwhile, CISOs and executive leaders are primarily concerned with overall security posture, application-level insights, and efficient risk management. Security can no longer slow down the business or be a burden to development. Business velocity is a key element in driving innovation especially in the era of AI.
This reality calls for a modern approach—one where multiple AI agents not only automate tasks independently but also collaborate, with humans and with each other, to drive application security efficiency.
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