AI-Powered Software Development Life Cycle (SDLC) Training
Upon request
1–2 days
Online, in Munich, or in-house
Upon request
German or English
Digital Certificate of Participation
Develop software faster, ensure quality, and securely integrate AI into the development process. Artificial intelligence (AI) is transforming the entire software development lifecycle—from initial requirements through architecture and coding to testing, deployment, and operations. Companies face a balancing act: They want to make development more efficient, but at the same time must ensure security, compliance, data protection, and technical quality.
Our AI-powered Software Development Life Cycle (SDLC) training shows you how to use AI effectively and in a controlled manner throughout the entire development process. You’ll learn how to structure requirements, prepare architecture decisions, write code and create tests more efficiently, and safely integrate AI-powered tools into existing development environments and management processes. The focus is not only on productivity and automation but also on traceability, governance, and human responsibility.
Content and Structure of the AI-Supported Software Development Life Cycle (SDLC) Training Course
- Fundamentals of LLMs, Generative AI, and Agent-Based Software Development
- Strengths, Limitations, and Common Pitfalls of AI-Driven Development
- Analyze stakeholder input and translate it into epics, user stories, and acceptance criteria
- Refine, prioritize, and check requirements for consistency using AI
- Spec-Driven Development as a combination of an idea, a specification, and tested code
- Support system design, architecture, and component and dependency analyses
- Generate code, develop boilerplate code, integrate APIs, and support refactoring and performance optimization
- Integrating AI tools into IDEs and classifying agentic coding and vibe coding
Result
Structured requirements, transparent architectural decisions, and more efficient development workflows with AI.
- Generate unit, integration, and end-to-end tests
- Developing exploratory tests, edge cases, and synthetic test data
- Automate Test Maintenance and Identify Test Gaps
- Implement AI-powered code reviews, security scans, and quality checks
- Identifying New Error Classes and Security Risks in AI-Generated Code
- Check code for quality, security, maintainability, and traceability
- Automate documentation, FAQs, knowledge bases, and release notes
- Optimize CI/CD pipelines, builds, deployments, and risk-based testing
- Develop proposals for canary releases, rollbacks, and deployment tests
- Analyze deployment results and identify opportunities for improvement
Result
Reliable quality assurance and more efficient and secure software deployment with AI.
- Data Protection, Copyright, Information Security, and Compliance in the AI-Driven SDLC
- Risks Associated with Source Code Disclosure, Tool Access, Data Leakage, and Model Dependencies
- Identifying and Evaluating Hallucinations, Bias, and Faulty AI-Generated Code
- Develop policies, guidelines, approval processes, and quality standards
- Document AI-supported development contributions in an auditable and traceable manner
- Define human oversight and checkpoints for code generation, reviews, and deployments
- Define Roles, Responsibilities, and Secure Development Practices
- Develop a roadmap for the phased implementation of AI in the SDLC
Result
A secure and strategic framework for the sustainable adoption of AI in software development.
This continuing education program combines technological fundamentals with practical applications for requirements, architecture, development, testing, security, and deployment. All modules can be combined freely.
What You Can Put into Practice Right Away After the SDLC Training
After completing this training, you will be able to use AI effectively and safely throughout the software development life cycle.
- Structure, Refine, and Prioritize Requirements Using AI
- Create code, APIs, tests, and technical content more efficiently
- Using AI-powered debugging, refactoring, and code reviews
- Identifying Security Risks and Quality Issues in AI-Generated Code
- Optimizing CI/CD Processes, Deployments, and Release Activities
- Integrating AI Tools Securely into IDEs and Existing Development Processes
- Take data protection, compliance, and information security into account
- Define Checkpoints, Approvals, and Responsibilities in the SDLC
- Develop a Roadmap for Implementing AI in Software Development
Who is the AI-powered software development lifecycle training course intended for?
This continuing education program is intended for:
- Software Developers and Engineering Teams
- Software Architects and Technical Leads
- DevOps, platform, and security teams
- QA and Testing Managers
- Product Owners and Technical Project Managers
- IT Executives and Digital Transformation Leaders
- Companies that want to safely integrate AI into their SDLC
A basic understanding of technology and experience with software development processes are helpful.
Basic knowledge of software development, IT architecture, testing, or DevOps is recommended. Experience with AI tools is helpful but not required.
Basic knowledge of software development, architecture, or DevOps is recommended. Experience with AI tools is helpful but not required.
Custom AI SDLC Training Courses
- Customized Training for Development, IT, and Management
- Integration of custom tools, IDEs, repositories, and CI/CD pipelines
- Working with real-world requirements, code examples, and deployment scenarios
- Development of Customized Policies, Prompt Standards, and Control Processes
- Offered as a seminar, workshop, or in-house training session
About Us as a Training Provider
We are an experienced management consulting firm with over 20 years of expertise in digital transformation, software development, security, and new technologies, such as artificial intelligence. Our training programs combine technological fundamentals with specific requirements from engineering, IT management, and business practice.
We help companies safely integrate AI into their development processes, improve quality and productivity, and establish clear standards for the responsible use of AI in the SDLC.
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Expertise in AI and Software Development – Your Trainer
Jonas Kuhlmann helps companies implement modern AI technologies and refine their software development processes. He explains complex topics such as generative AI, agentic coding, testing, and AI-powered automation in an accessible way, drawing direct parallels to real-world development tasks.
His focus is on combining efficiency, quality, and security. He demonstrates how companies can effectively integrate AI into the SDLC, optimize development processes, and simultaneously ensure governance, compliance, and human accountability.
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- Targeted Use of AI Throughout the Entire Software Development Life Cycle
- Customized Professional Development for Development, IT, and Management
- Proven expertise in AI, software engineering, security, and compliance
- Practical Methods for Requirements, Coding, Testing, and Deployment
TISAX and ISO certification apply only to the Munich location
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TISAX and ISO certification apply only to the Munich location
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FAQ – Frequently Asked Questions About AI-Powered SDLC Training
A basic understanding of software development or technical IT processes is recommended. The training is not intended exclusively for developers, but also for architects, QA managers, IT managers, and technical project managers. A basic understanding of the software development life cycle facilitates the application of what is learned in practice. Each training session can be tailored to participants’ prior knowledge.
The training covers the entire lifecycle—from requirements analysis and architecture through development and testing to deployment, operations, and governance. This will help you learn to view AI not just as a standalone element, but as an integral part of an end-to-end development process.
Among other things, AI can formulate user stories, develop architectural variants, generate code, integrate APIs, and create documentation. It also assists with debugging, test case generation, code reviews, and release activities. However, all results must be reviewed by subject matter experts and integrated into existing quality processes.
In this training, you will learn how to validate AI-generated changes through reviews, tests, security scans, and defined approval processes. Additionally, maintainability, traceability, and technical debt will be addressed. The goal is a controlled development process that achieves speed without compromising quality.
The most significant risks include faulty or insecure code, data leaks, unclear licensing rights, and excessive reliance on individual models or tools. Hallucinations and incomplete context can also lead to incorrect results. The training course demonstrates how to identify and assess these risks and mitigate them through governance.
How are security and compliance taken into account?
Security, data protection, copyright, and compliance are central components of the training. You will learn how to protect source code, access credentials, and company information, as well as what requirements should be placed on tools and processes. The training also covers approvals, auditability, and human control points.
Yes. For in-house training, we can incorporate your IDEs, repositories, CI/CD pipelines, and existing AI tools. We can also integrate your organization’s own development processes or typical challenges into the exercises. This results in concrete standards and workflows tailored to your organization.
Many methods can be applied immediately, for example, when developing requirements, conducting code reviews, creating test cases, or preparing technical documentation. For productive use, these approaches should then be incorporated into standards, approval processes, and security policies. The training provides the methodological and organizational foundation for this.