Secure Code Warrior, a leader in AI software governance and developer security upskilling, has introduced its AI Adoption Model to help organisations navigate AI integration in software development. The framework maps AI use progression from minimal assistance to fully autonomous agentic orchestration. The model organises AI development into three phases: AI-Assisted, AI Native and Agentic. Each phase defines distinct risk levels, developer skills requirements and governance controls, enabling CISOs to connect AI usage with developer capability and software risk signals. The framework addresses growing concerns about AI expanding attack surfaces faster than traditional controls can scale. Gartner predicts over 40% of agentic AI projects will be abandoned by 2027 due to uncontrolled costs and poor risk controls, making developer training essential for secure AI adoption.

SCW AI Adoption Model: Secure AI for the Agentic SDLC. Secure Code Warrior has launched its SCW AI Adoption Model, a practical framework that outlines the full evolution of AI in software development - from minimal AI support to fully autonomous, agent-driven orchestration. The model helps CISOs understand their organization's current stage of AI adoption, determine the training developers need at each level, and define the governance controls required as autonomy grows, addressing the key question security leaders are asking: where to begin. Gartner's 2026 Hype Cycle for Secure Software Engineering warns that AI-augmented development is 'expanding the attack surface faster than traditional controls can scale,' and that AI coding tools are making secure coding skills more important than ever. Machine Learning & Artificial Intelligence AI adoption is no longer confined to engineering: non-developer employees building applications with no-code and vibe coding tools still contribute to an organization's risk profile. The SCW AI Adoption Model organizes AI development into three phases: AI-Assisted, AI Native, and Agentic. Each phase carries distinct risk levels, developer skills requirements, and governance controls, giving CISOs a clear way to connect AI usage, developer capability, and software risk signals. This allows security leadership to measure, reduce, and govern risk while demonstrating progress in securing the Software Development Lifecycle (SDLC) as it transitions to the more relevant Agentic Development Lifecycle (ADLC). "In our current AI-powered development, writing lines of code is almost free, but developers are still on the hook for secure outcomes. Their security skills need to evolve from code writer to creator & orchestrator," said Pieter Danhieux, Secure Code Warrior Co-founder & CEO. "CISOs need an approach to ADLC governance that is as modern as the methodology itself, one that follows an adoption model designed for agentic AI's evolving, adaptive approach to software development. We've built this framework to help organizations turn secure AI adoption and AI governance from a reactive exercise into a measurable, scalable discipline." As security leaders need data-driven insights to inform their AI cost decision-making, this new framework provides organizations the insight needed to safely address risk correlation. With the SCW AI Adoption Model, organizations can: * Identify their current AI adoption stage: Not all AI use carries the same risk. The model gives organizations a clear map to help them identify where they are, what training is required, and what governance controls need to be in place at that stage. * Deliver training that meets developers where they are: Not every developer has the same skill level or uses AI the same way. SCW maps capability, risk, and training to each adoption phase - giving developers the specific AI security skills that apply to how they actually work. * Demonstrate governance ROI: Gartner predicts that by 2027, more than 40% of agentic AI projects will be abandoned because of uncontrolled costs and poor risk controls. The answer isn't more AI catching AI mistakes - it's training developers to use AI correctly from the start, producing secure code, avoiding repeated vulnerabilities, and using AI efficiently enough to keep costs under control. SCW provides the tools and training to prove that behavior change is making an impact. To learn more about the SCW AI Adoption Model, explore the full framework at the website here. https://www.securecodewarrior.com/solution/scw-ai-adoption-model

Secure Code Warrior releases AI Adoption Model. June 23, 2026 Secure Code Warrior introduced its new SCW AI Adoption Model, a practical framework that maps the full progression of AI use in software development, from minimal AI assistance or fully autonomous agentic orchestration. The framework gives CISOs a roadmap to identify where their organization sits today from an AI adoption perspective, the training developers need at each stage, and which governance controls are required as autonomy increases - answering the question every security leader is asking: where do DevOps Digest start? The SCW AI Adoption Model organizes AI development into three phases: AI-Assisted, AI Native, and Agentic. Each phase carries distinct risk levels, developer skills requirements, and governance controls, giving CISOs a clear way to connect AI usage, developer capability, and software risk signals. This allows security leadership to measure, reduce, and govern risk while demonstrating progress in securing the Software Development Lifecycle (SDLC) as it transitions to the more relevant Agentic Development Lifecycle (ADLC). "In our current AI-powered development, writing lines of code is almost free, but developers are still on the hook for secure outcomes. Their security skills need to evolve from code writer to creator & orchestrator," said Pieter Danhieux, Secure Code Warrior Co-founder & CEO. "CISOs need an approach to ADLC governance that is as modern as the methodology itself, one that follows an adoption model designed for agentic AI's evolving, adaptive approach to software development. We've built this framework to help organizations turn secure AI adoption and AI governance from a reactive exercise into a measurable, scalable discipline." With the SCW AI Adoption Model, organizations can: - Identify their current AI adoption stage: Not all AI use carries the same risk. The model gives organizations a clear map to help them identify where they are, what training is required, and what governance controls need to be in place at that stage. - Deliver training that meets developers where they are: Not every developer has the same skill level or uses AI the same way. SCW maps capability, risk, and training to each adoption phase - giving developers the specific AI security skills that apply to how they actually work. - Demonstrate governance ROI: SCW provides the tools and training to prove that behavior change is making an impact. The SCW AI Adoption Model is now available. Industry news. June 23, 2026 The Linux Foundation, the nonprofit organization enabling mass innovation through open source, announced the intent to launch the Agent Name Service (ANS), a new open standard designed to provide trusted identity, verification, and discovery for AI agents operating across the internet. June 23, 2026 Secure Code Warrior introduced its new SCW AI Adoption Model, a practical framework that maps the full progression of AI use in software development, from minimal AI assistance or fully autonomous agentic orchestration. June 23, 2026 Snyk announced Evo Agentic Development Security (ADS), extending security coverage to the AI workforce powering modern software development. June 23, 2026 Momentic launched a major platform update that rethinks software verification for the AI era. June 22, 2026 Check Point(R) Software Technologies Ltd.(link is external) announced the use of OpenAI's frontier cyber capabilities into its customer-facing defenses. June 22, 2026 TypeMock announced the launch of TypeMock Test Review, a new solution that helps software development teams evaluate the quality of their unit tests by identifying duplicate, fragile, ineffective, and high-maintenance tests that wasn't required before AI generated tests. June 18, 2026 Check Point(R) Software Technologies Ltd.(link is external) announced its upcoming integration with Amazon Bedrock AgentCore. June 18, 2026 UltraViolet Cyber (UltraViolet) announced the launch of UltraViolet Solstice, its proprietary AI platform for application penetration testing. June 18, 2026 Infragistics launched three integrated AI development tools that make up its new Ignite UI Enterprise MCP toolchain. June 17, 2026 Check Point(R) Software Technologies Ltd.(link is external) and Illumio announced an expanded strategic partnership(link is external) to help organizations defend against a new category of threat: frontier AI models capable of autonomously executing full-scale attacks at machine speed. June 17, 2026 At the AWS Summit, Amazon announced a series of innovations focused on giving AI agents the governed context they need to deliver trusted decisions, including a new service called AWS Context, business context and semantic search for AWS Glue Data Catalog (preview), and the general availability of Amazon S3 annotations. June 17, 2026 Postman has achieved the Amazon Web Services (AWS) AI Competency in the Agentic AI Tools category. June 17, 2026 Azul launched a free JVM vulnerability risk assessment to address the blind spot that autonomous AI exploitation tools are increasingly able to find. June 17, 2026 Prismatic has open-sourced its entire library of pre-built Application Connector and Data Platform components under the Apache-2.0 license, making the code freely available for developers to read, fork and extend. June 16, 2026 Check Point(R) Software Technologies Ltd.(link is external) announced a major expansion of its Managed Service Provider (MSP) platform.