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Ensuring Code Quality and Security in AI assisted Software Engineering
- Categories:
- Software and DevOps Artificial Intelligence
- Level:
- Intermediate
- Code:
- QAAICQSE-BL
- Exam:
- Not Applicable
Overview
Prerequisites
- Experience writing code in at least one programming language
- Familiarity with software development practices such as version control and testing
- Basic understanding of application security concepts
- Awareness of AI-assisted tools such as GitHub Copilot or similar
- Target audience
- Software developers and engineers using or adopting AI-assisted coding tools
- Technical leads responsible for code quality and security standards
- DevOps and platform engineers integrating automation into development workflows
- Organisations adopting AI in software development as part of a wider transformation pathway
What's included
Select your preferred way to learn:
What is Virtual?
Live, instructor-led training delivered online
Interactive online sessions led by subject matter experts. Learners join live classes, take part in discussions, and complete practical exercises from any location, making it easy to fit collaborative learning into busy schedules.
If you prefer to connect to a course that is taking place in a physical classroom, you can choose our Remote Access option. .
Best for: Teams and individuals who want expert guidance, real-time collaboration, and flexible access.
What's included?
1 Day instructor led course
6 month free access to QA learning platform
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Best for: Organisations and teams looking to target specific business priorities and capabilities with QA subject matter expertise.
Find out more about the course:
Learning outcomes
- Use AI-assisted development tools while maintaining accountability for code quality and security
- Identify common defects and risks in AI-generated code, including logical errors and insecure patterns
- Apply testing strategies, static analysis, and automated quality checks to AI-assisted workflows
- Detect and remediate security vulnerabilities aligned to OWASP Top 10 risks
- Refactor AI-generated code to improve maintainability, performance, and robustness
- Evaluate when AI-generated outputs can be trusted and when additional validation is required
- Contribute to organisational governance frameworks for responsible AI-assisted development
Course outline
- Overview of AI-assisted development tools and capabilities
- Demonstration of model comparison tools and prompting approaches
- Productivity gains versus quality and security trade-offs
- Group discussion on current AI usage in development workflows
- Lab: evaluating AI-generated code quality
- Analyse AI-generated outputs against a structured checklist
- Identify correctness, maintainability, and security issues
- Annotate and prioritise findings based on risk
- Generate a task manager with dependencies and scheduling logic
- Detect circular dependencies and resource conflicts
- Evaluate implementation against business requirements
- Apply structured code review techniques
- Common failure patterns in AI-generated code
- Hallucinated APIs and incorrect assumptions
- Hidden complexity and over-engineering
- Missing edge cases and inconsistent logic
- Maintaining coding standards across human and AI contributions
- Integrating linters, formatters, and automated code review tools
- Discussion on pull request and review practices
- Generate and execute AI-created unit tests
- Identify gaps in test coverage and missing scenarios
- Validate behaviour across edge cases such as invalid inputs and concurrency
- Refactor code for clarity, modularity, and maintainability
- Implement logging, error handling, and performance improvements
- Build pricing logic with discounts, tax, and promotions
- Identify issues such as incorrect calculations and edge cases
- Improve test coverage and ensure deterministic outcomes
- Apply static analysis and quality gates
- Introduction to OWASP Top 10 risks in AI-generated code
- Common vulnerabilities in authentication, data handling, and APIs
- Security scanning and dependency analysis tools
- Aligning secure coding practices with AI workflows
- Analyse AI-generated user management system
- Identify vulnerabilities including:
- Broken access control
- Weak cryptographic practices
- Injection flaws
- Authentication weaknesses
- Compare manual review with automated security tool outputs
- Implement secure coding fixes
- Parameterised queries
- Strong password hashing
- Input validation and sanitisation
- Secure token handling
- Create test cases to simulate attacks
- Perform basic penetration testing scenarios
- Validate fixes against security requirements
- Extend system with secure authentication mechanisms
- Address edge cases and timing attack risks
- Ensure usability and security balance
- Code provenance and AI-generated content considerations
- Licensing risks and intellectual property concerns
- Data protection and organisational AI policies
- Establishing responsible AI development practices
- Define organisational standards for AI tool usage
- Create policies covering:
- Tool selection and approval
- Code attribution and IP protection
- Quality and security gates
- Developer training and competency
- Incident response and audit processes
- Financial services, healthcare, government, and retail contexts
- Identify regulatory and compliance requirements
- Balance productivity with risk management
- Present and critique team policies
- Consolidate best practices
- Define implementation roadmap and success metrics
- Reinforce critical evaluation of AI-generated code
- Align learning to real-world application
- Define next steps within AI in software development pathway
- Scenario-based labs using AI-generated code
- Real-world exercises focused on quality and security challenges
- Instructor-guided refactoring and secure coding practices
- Collaborative group workshops to apply governance frameworks
Good to know
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What our customers are saying
“I would say the secure software engineering programme QA built, is beyond training. It is more around making transformation in the mindset of people, and this was exactly what we are looking for.”
Emil Minev
Senior Consultant & Programme Manager, Paysafe Group
“QA provides the updated and the comprehensive theory in the domain of AI/ML/DL and DevOps. Being an AWS machine learning speciality enthusiast I don't have to look for different websites or online learning platform for the course materials.”
Authenticated G2 user
Information Technology and Services
Portfolio Director
Andy Smith
Portfolio Director – Software and DevOps
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