AI-Driven software development life cycle enhances compliance in regulated industries. Explore how Netguru's AI-assisted SDLC revolutionizes coding practices in regulated industries, balancing enhanced efficiency with rigorous compliance and human oversight. Key facts. * AI-assisted SDLC ensures compliance with audit requirements, enhancing trust in regulated industries. * 5 human decision points in the process mitigate risks, revealing vulnerabilities in AI autonomy. * Continuous telemetry measures code quality and costs, linking AI performance to financial outcomes. * Requirements as code improves traceability, indicating a strategic shift towards agile compliance. * The model's adaptability across tech stacks offers competitive advantage in diverse market segments. Summary. Netguru deployed an AI-assisted software development life cycle (SDLC) for a client in a regulated industry, utilizing Claude Code to write code, requirements, and tests. The solution maintained strict audit, traceability, and security controls while allowing the AI agent to operate under the same permissions as a junior engineer. As a result, the deployment ensured compliance with industry regulations while enhancing development efficiency. LearnAI Tools Background. Netguru's client operates in a regulated industry, managing product data for multiple tenants on a multi-tenant SaaS platform. Prior to the deployment, the client faced challenges in maintaining compliance with audit and security requirements while managing software development processes. The existing workflows were cumbersome and lacked the efficiency needed to keep pace with evolving demands. Challenge. The primary challenge was to enable an AI agent to write code on a regulated SaaS platform without compromising audit, traceability, or security controls. The platform required a controlled process for changes, reviews, and approvals to ensure compliance with stringent industry regulations. Solution. Netguru implemented an AI-assisted SDLC where Claude Code acted as a coding agent, writing specifications, requirements, and tests. The process included eight steps, with human approvals at five critical decision points. The AI agent operated within a structured workflow, ensuring that every change passed through three levels of continuous integration (CI) gates and was tied to specific requirements and tests. This setup allowed for comprehensive audit trails and maintained high security and data isolation. LearnAI Tools Results. The deployment successfully integrated AI into the development process while adhering to all regulatory requirements. The strict controls and audit trails ensured that every action taken by the AI agent was traceable, allowing auditors to reconstruct the development process. Specific metrics regarding efficiency improvements or time savings were not provided in the source material. Key insights. * Implementing AI in regulated environments requires stringent controls to maintain compliance and auditability. * Treating requirements as code enhances traceability and ensures that every code change is linked to specific business needs. * Continuous integration gates are essential for maintaining quality and security in an AI-assisted development process. Customer testimonial. No direct quote was provided in the source material. Discover more Learn AI Tools Find Therapists Learn Engineering Claude Code Technologies LearnEngineering TypeScript Key concepts. AI-assisted SDLC regulated industry audit trail requirements traceability human decision points security controls testing with AI Definitions. * AI-assisted SDLC - A software development life cycle where an AI coding agent performs tasks from specification to release, with human approvals at critical decision points. * CI/CD - Continuous Integration and Continuous Deployment, a method to automate the software delivery process, ensuring code changes are tested and deployed efficiently. * audit trail - A record that allows tracing of actions taken in the software development process, ensuring accountability and transparency. * requirements traceability - The ability to link requirements to their corresponding code and tests, ensuring that all requirements are met and verified. * human decision points - Stages in the development process where human approval is required before proceeding with changes or deployments. Use cases. * | Managing product data for multiple tenants * | Automating code writing and testing in regulated environments * | Ensuring compliance with audit requirements * | Implementing security controls in software development * | Facilitating collaboration between client and internal engineers * | Maintaining an up-to-date requirements traceability matrix Frequently asked questions. What is the role of AI in the SDLC? AI assists in writing code, generating requirements, and creating tests, while humans oversee and approve critical decisions to maintain quality and compliance. How does the AI ensure compliance with audit requirements? The AI operates under strict rules that mirror those of junior engineers, ensuring every action is documented and traceable, which facilitates audits. What technologies are used in the AI-assisted SDLC? The framework utilizes .NET for the backend, TypeScript and React for the frontend, and Azure for cloud services, allowing for a robust development environment. How are requirements managed in this model? Requirements are treated as code, versioned in the repository, and undergo a strict approval process to ensure they are locked and traceable to the corresponding code. Can this model be applied to other programming languages? Yes, the AI-assisted SDLC model is adaptable and can be implemented across various programming languages and frameworks by integrating specific rules and skills.

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