SCW’s framework provides organisations a transparent entry level and sensible roadmap for each workforce — at each stage of the AI adoption curve.
Secure Code Warrior, a frontrunner in AI software program governance and developer safety upskilling, immediately launched its new SCW AI Adoption Model, a sensible framework that maps the total development of AI use in software program improvement, from minimal AI help or absolutely autonomous agentic orchestration. The framework provides CISOs a roadmap to determine the place their organisation sits immediately from an AI adoption perspective, the coaching builders want at every stage, and which governance controls are required as autonomy will increase — answering the query each safety chief is asking: the place will we begin?
Gartner’s 2026 Hype Cycle for Secure Software Engineering warns that AI-augmented improvement is ‘increasing the assault floor sooner than conventional controls can scale,’ and that AI coding instruments are making safe coding expertise extra vital than ever. AI adoption is not confined to engineering: non-developer staff constructing functions with no-code and vibe coding instruments nonetheless contribute to an organisation’s danger profile.
The SCW AI Adoption Mannequin organises AI improvement into three phases: AI-Assisted, AI Native, and Agentic. Every part carries distinct danger ranges, developer expertise necessities, and governance controls, giving CISOs a transparent technique to join AI utilization, developer functionality, and software program danger alerts. This permits safety management to measure, cut back, and govern danger whereas demonstrating progress in securing the Software program Growth Lifecycle (SDLC) because it transitions to the extra related Agentic Growth Lifecycle (ADLC).
“In our present AI-powered improvement, writing traces of code is sort of free, however builders are nonetheless on the hook for safe outcomes. Their safety expertise must evolve from code author to creator & orchestrator,” mentioned Pieter Danhieux, Safe Code Warrior Co-founder & CEO. “CISOs want an method to ADLC governance that’s as trendy because the methodology itself, one which follows an adoption mannequin designed for agentic AI’s evolving, adaptive method to software program improvement. We’ve constructed this framework to assist organisations flip safe AI adoption and AI governance from a reactive train right into a measurable, scalable self-discipline.”
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As safety leaders want data-driven insights to tell their AI value decision-making, this new framework supplies organisations the perception wanted to securely handle danger correlation. With the SCW AI Adoption Mannequin, organisations can:
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Determine their present AI adoption stage: Not all AI use carries the identical danger. The mannequin provides organizations a transparent map to assist them determine the place they’re, what coaching is required, and what governance controls should be in place at that stage.
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Ship coaching that meets builders the place they’re: Not each developer has the identical talent stage or makes use of AI the identical approach. SCW maps functionality, danger, and coaching to every adoption part — giving builders the particular AI safety expertise that apply to how they really work.
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Show governance ROI: Gartner predicts that by 2027, greater than 40% of agentic AI projects shall be deserted due to uncontrolled prices and poor danger controls. The reply is not extra AI catching AI errors — it is coaching builders to make use of AI accurately from the beginning, producing safe code, avoiding repeated vulnerabilities, and utilizing AI effectively sufficient to maintain prices underneath management. SCW supplies the instruments and coaching to show that behaviour change is making an affect.
The SCW AI Adoption Mannequin is now accessible. To discover the framework, go to: https://www.securecodewarrior.com/solution/scw-ai-adoption-model.
About Safe Code Warrior
Safe Code Warrior is a frontrunner in AI software program governance and developer safety upskilling, enabling enterprises to manage AI-driven software program improvement throughout the SDLC. Constructed on a decade of developer safety experience, it delivers AI visibility, coverage enforcement, and focused studying to stop vulnerabilities and strengthen software program high quality earlier than manufacturing.