THE SecurityWeek article, dated 20 October 2026, argues that the security risk posed by agentic AI in software development isn’t simply a race for speed, but a need for maturity. It explains that as organisations deploy AI tools—from basic assistants to autonomous agents—the attack surface expands far faster than traditional controls can cope.
Gartner’s forecast that incident costs driven by agentic AI will quadruple through 2027 is cited to underline the scale of emerging risk, while SonarSource’s findings highlight that frontier large language models often produce the most severe security vulnerabilities when they generate code.
To manage these risks, the piece promotes governance as the central discipline, complemented by improved observability and mature security controls. It introduces a three-phase, eight-stage AI Adoption Model that maps progression from AI Assisted (phases one and two) to AI Native (phases three and four), and finally to Agentic (phases five to eight).
Each stage defines activity, risk level and developer skill requirements, with specific measures such as Model Context Protocol (MCP), over-trust detection, and audit-ready change tracking. The author stresses that many organisations are currently at stages two to four, and emphasises the importance of early governance, guardrails, upskilling, and developer-centric security practices to prevent risk from spiralling as AI tools gain autonomy.
Practically, organisations are advised to establish governance foundations early, tailor security controls to current stage, and build ongoing education for developers to manage the evolving Agentic Development Lifecycle. The article frames governance and learning as prerequisites to realising AI productivity without surrendering control.