This workshop is a practical, hands-on introduction to web application security for developers and testers. Participants learn how real-world attacks work by exploiting common vulnerabilities in an intentionally vulnerable web application and then mapping them to secure coding and testing practices. The session combines short theory blocks with live demos and interactive challenges using OWASP Juice Shop. Attendees will explore the OWASP Top 10 risks, perform attacks such as injection, broken access control, and cross-site scripting, and understand how these issues appear in real projects and test scenarios. Developers will learn how to write more secure code, while testers will learn how to identify, reproduce, and report security vulnerabilities effectively. By the end of the workshop, participants will understand the attacker mindset and how to integrate security into development and testing workflows.

Key takeaways:

• Learn How Real Web Vulnerabilities Are Exploited Participants experience real attacks like XSS, SQL Injection, Authentication Failures, and Broken Access Control, understanding how attackers find and exploit weaknesses.
• Learn some of the OWASP Top 10 Through Hands-On Challenges The workshop maps practical exploits directly to the OWASP Top 10, helping participants recognize the most critical web security risks in modern applications.
• Develop an Attacker Mindset for Better Testing and Coding Both developers and testers learn to think like attackers, improving threat modeling, test coverage, and defensive design.
• Apply Secure Coding and Security Testing Techniques Attendees learn prevention techniques (validation, authorization checks, secure headers) and how testers can verify fixes and prevent regressions.

Product Mindset for Developers is a full-day workshop for software engineers who've built too many features that nobody ends up using, or that get reworked halfway through the sprint, when requirements suddenly change.
We'll work through a real case together, from understanding the actual problem to building a prototype that tests our assumptions.
The workshop introduces three lenses that matter before you even start writing the first line of code: what problem are we solving, what outcome are we aiming for and what solution makes sense given both. You won't become a product manager, but you'll get better at:

• spotting bad requirements
• asking the right questions earlier
• collaborating with the people who write the specs

AI is accelerating software delivery to unprecedented speeds, but quality processes in many companies remain stuck as bottlenecks. To turn quality into a competitive advantage, organizations must treat it as a critical business capability woven through your entire value stream, not a final checkpoint. Quality is evolving from being 'the QA team's' or 'our team's QA person's' responsibility to being everyone's responsibility, supported by specialists. Just as DevOps didn't eliminate operations work or operators (it changed who does it and how), this evolution doesn't eliminate quality work or QA specialists. It changes the structure: moving from QA as a handoff destination and bottleneck (whether to a separate team or designated individual) to QA expertise embedded in, enabling, or providing platforms for stream-aligned teams. We will use Team Topologies patterns alongside systems thinking approaches to map quality as a capability that flows as a band weaving through your entire value stream (not just one pit-stop on the SDLC). Together we will identify constraints, flow bottlenecks, and waste. We will explore where quality becomes a bottleneck versus where it flows naturally, and design team structures and interactions that enable fast flow without sacrificing outcomes. Organizations that successfully make this shift gain significant competitive advantages: faster time to market, reduced risk, and quality as a strategic differentiator rather than a cost center. Through hands-on mapping exercises, case study analysis, and collaborative problem-solving, you will explore when dedicated quality teams enable fast flow and quality versus when they create bottlenecks. For example, what does a platform team providing quality infrastructure look like? When does an enabling team coaching quality practices make sense? We will also work through practical change management tools to help you build the business case and frame the transformation for different stakeholders. You will leave with concrete approaches to navigate the organizational shifts required to move toward quality as a system. All materials are designed for immediate use back at your organization: templates you can adapt, frameworks you can apply, and conversation guides you can use with your stakeholders.

Key Takeaways:

• Map quality as a system capability across your teams using Team Topologies interaction patterns, identifying bottlenecks, cognitive load issues, and opportunities for quality to flow through your value stream
• Design team structures and ownership boundaries that enable fast delivery and sustainable quality, including when platform or enabling teams for quality make strategic sense and when they create anti-patterns
• Frame and communicate organizational change toward quality as a system using practical change management tools, addressing the "what is the role of QA?" question and navigating the full organizational transformation including structure, roles, and incentives

Audience Level:

• Senior / Leadership (Tech Leads, Engineering Managers)
• Developers, QA/Test Leaders
• Product Leaders

Building microservices is no longer about writing more code — it’s about making the right design decisions and recognizing patterns that scale and patterns that break. In this full-day, hands-on workshop, participants will focus on mastering the core concepts of microservices architecture and code quality, while using AI to handle much of the implementation work. The emphasis is on understanding why a design works, when it should change, and how to guide AI through clear problem statements, constraints, and tests. Rather than coding everything manually, participants will practice: defining architectural boundaries, identifying quality risks early, expressing intent through tests and acceptance criteria, and using AI to generate and evolve code that follows those rules. Through guided exercises and realistic scenarios, we’ll explore how well-defined testing and problem-solving drive better outcomes than tool-driven coding and how AI becomes effective only when engineering thinking is clear.

Key Takeaways

• Recognize architectural patterns that support change — and anti-patterns that create friction
• Design microservices by reasoning about behavior, boundaries, and quality attributes, not frameworks
• Use tests and constraints to drive AI-generated code in a controlled way
• Translate business and technical problems into clear problem statements AI can work with
• Detect early signs of design erosion and technical debt
• Apply pragmatic techniques to evolve and refactor existing services safely

Many teams proudly report high test coverage and still struggle with outages, late defect discovery, and low confidence in releases. At the same time, AI tools and agentic LLMs are transforming how we generate tests and analyze code, but adding AI to a weak validation strategy only accelerates failure. This session explores how to improve the development and testing experience by shifting from coverage-driven testing to value-driven validation. The focus is not on adding more tests, but on designing effective quality practices that directly support business outcomes. Using delivery signals such as DORA metrics of deployment frequency, change failure rate, lead time to change, we’ll identify where current validation fails to protect real user and revenue-critical flows. We’ll then show how to rebalance validation across technology-facing checks and business-facing scenarios, and how AI can support risk analysis, targeted test generation, and faster feedback loops. The outcome is an Engineering flow aligned with business impact and actionable decision-making Attendees will leave with a practical approach to use AI responsibly, reduce change failures, and create a development flow centered on value, clarity, and effective action.

Key Takeaways

• Identify why high test coverage does not translate into release confidence, using real delivery signals such as risk assessment and fast feedback loops
• Design a balanced quality strategy that combines technology-facing checks (unit, integration, contract tests) with business-facing validations focused on critical user and revenue flows
• Define a Minimal Validation Set with clear go/no-go criteria that supports safe, predictable releases
• Improve feedback loops across environments to catch the right problems earlier without slowing delivery
• Measure quality improvement in business-relevant terms, not vanity metrics, using a small set of indicators reviewed continuously and on-demand