Generative AI is transforming the software industry, with tools like GitHub Copilot, Gemini, Claude or others, enabling developers to achieve unprecedented levels of efficiency. While this is exciting progress, it also raises important concerns, encouraging stakeholders to approach these technologies with care. Current AI tools often lack the nuanced understanding necessary to address subtle, yet critical aspects of software development, particularly in the domain of security.

This course provides a comprehensive insight into the responsible use of generative AI in coding. Participants delve into topics in software development that are most likely to be impacted by careless use of generative AI, including authentication, authorization, and cryptography. The curriculum also includes an analysis of how AI tools like Copilot handle secure coding practices related to key vulnerabilities outlined in the OWASP Top Ten, such as path traversal, SQL injection, or cross-site scripting.

Through hands-on learning and experimenting, participants will get a solid understanding of both the strengths and limitations of AI-assisted development. In addition, case studies of real-world incidents showcase the consequences of insecure code and demonstrate the dual nature of generative AI as both a resource and a potential risk.

By the end of the course, developers will be equipped with the knowledge and skills to integrate AI tools into the software development lifecycle responsibly, enhancing efficiency without compromising security or product quality.

• Coding responsibly with GenAI
• The OWASP Top Ten from Copilot's perspective
• Wrap up

• Understanding the essentials of responsible AI
• Getting familiar with essential cyber security concepts
• Understanding how cryptography supports security
• Learning how to use cryptographic APIs correctly in Java
• Understanding Web application security issues
• Detailed analysis of the OWASP Top Ten elements
• Putting Web application security in the context of Java
• Going beyond the low hanging fruits
• Managing vulnerabilities in third party components
• All this put into the context of GitHub Copilot

Generative AI is transforming the software industry, with tools like GitHub Copilot, Gemini, Claude or others, enabling developers to achieve unprecedented levels of efficiency. While this is exciting progress, it also raises important concerns, encouraging stakeholders to approach these technologies with care. Current AI tools often lack the nuanced understanding necessary to address subtle, yet critical aspects of software development, particularly in the domain of security.

This course provides a comprehensive insight into the responsible use of generative AI in coding. Participants delve into topics in software development that are most likely to be impacted by careless use of generative AI, including authentication, authorization, and cryptography. The curriculum also includes an analysis of how AI tools like Copilot handle secure coding practices related to key vulnerabilities outlined in the OWASP Top Ten, such as path traversal, SQL injection, or cross-site scripting.

Through hands-on learning and experimenting, participants will get a solid understanding of both the strengths and limitations of AI-assisted development. In addition, case studies of real-world incidents showcase the consequences of insecure code and demonstrate the dual nature of generative AI as both a resource and a potential risk.

By the end of the course, developers will be equipped with the knowledge and skills to integrate AI tools into the software development lifecycle responsibly, enhancing efficiency without compromising security or product quality.

• Coding responsibly with GenAI
• The OWASP Top Ten from Copilot's perspective
• Wrap up

• Understanding the essentials of responsible AI
• Getting familiar with essential cyber security concepts
• Understanding how cryptography supports security
• Learning how to use cryptographic APIs correctly in Python
• Understanding Web application security issues
• Detailed analysis of the OWASP Top Ten elements
• Putting Web application security in the context of Python
• Going beyond the low hanging fruits
• Managing vulnerabilities in third party components
• All this put into the context of GitHub Copilot

Generative AI is transforming the software industry, with tools like GitHub Copilot, Gemini, Claude or others, enabling developers to achieve unprecedented levels of efficiency. While this is exciting progress, it also raises important concerns, encouraging stakeholders to approach these technologies with care. Current AI tools often lack the nuanced understanding necessary to address subtle, yet critical aspects of software development, particularly in the domain of security.

This course provides a comprehensive insight into the responsible use of generative AI in coding. Participants delve into topics in software development that are most likely to be impacted by careless use of generative AI, including authentication, authorization, and cryptography. The curriculum also includes an analysis of how AI tools like Copilot handle secure coding practices related to key vulnerabilities outlined in the OWASP Top Ten, such as path traversal, SQL injection, or cross-site scripting.

Through hands-on learning and experimenting, participants will get a solid understanding of both the strengths and limitations of AI-assisted development. In addition, case studies of real-world incidents showcase the consequences of insecure code and demonstrate the dual nature of generative AI as both a resource and a potential risk.

By the end of the course, developers will be equipped with the knowledge and skills to integrate AI tools into the software development lifecycle responsibly, enhancing efficiency without compromising security or product quality.

• Coding responsibly with GenAI
• The OWASP Top Ten from Copilot's perspective
• Wrap up

• Understanding the essentials of responsible AI
• Getting familiar with essential cyber security concepts
• Understanding how cryptography supports security
• Learning how to use cryptographic APIs correctly in C#
• Understanding Web application security issues
• Detailed analysis of the OWASP Top Ten elements
• Putting Web application security in the context of C#
• Going beyond the low hanging fruits
• Managing vulnerabilities in third party components
• All this put into the context of GitHub Copilot

Generative AI is transforming the software industry, with tools like GitHub Copilot, Gemini, Claude or others, enabling developers to achieve unprecedented levels of efficiency. While this is exciting progress, it also raises important concerns, encouraging stakeholders to approach these technologies with care. Current AI tools often lack the nuanced understanding necessary to address subtle, yet critical aspects of software development, particularly in the domain of security.

This course provides a comprehensive insight into the responsible use of generative AI in coding. Participants delve into topics in software development that are most likely to be impacted by careless use of generative AI, including authentication, authorization, and cryptography. The curriculum also includes an analysis of how AI tools like Copilot handle secure coding practices related to key vulnerabilities outlined in the OWASP Top Ten, such as path traversal, SQL injection, or cross-site scripting.

Through hands-on learning and experimenting, participants will get a solid understanding of both the strengths and limitations of AI-assisted development. In addition, case studies of real-world incidents showcase the consequences of insecure code and demonstrate the dual nature of generative AI as both a resource and a potential risk.

By the end of the course, developers will be equipped with the knowledge and skills to integrate AI tools into the software development lifecycle responsibly, enhancing efficiency without compromising security or product quality.

• Coding responsibly with GenAI
• Memory management vulnerabilities
• Memory management hardening
• Common software security weaknesses
• Using vulnerable components
• Wrap up

• Understanding the essentials of responsible AI
• Getting familiar with essential cyber security concepts
• Correctly implementing various security features
• Identify vulnerabilities and their consequences
• Learn the security best practices in C++
• Managing vulnerabilities in third party components
• Input validation approaches and principles
• All this put into the context of GitHub Copilot

The course bridges the two worlds of cybersecurity and machine learning. Starting from the core cybersecurity principles, it highlights how ML systems are exposed to threats – both pre-existing threats from the world of software security affecting these systems in unexpected ways and completely new kinds of threats that require a deeper understanding of adversarial machine learning.

The first step of understanding the security of ML is to analyze the relevant threats. We synthesize a threat model (the assets to protect, the security requirements, the attack surface, potential attacker profiles, and the actual threat model represented via attack trees) based on the existing threat models of NIST, Microsoft, BIML, and OWASP. We then explore the relationship of security and ML, from ML-driven static analysis tools and IDS to a brief glimpse at ML-assisted attack tools used by hackers today. We look at the most significant threats against Large Language Models (LLMs), following the OWASP LLM Top 10 2025 (among others). The bulk of the course deals with adversarial machine learning, and a detailed discussion of the four main attack subtypes: evasion, poisoning, model inversion, and model stealing as well as practical aspects of these attacks. Various labs on adversarial attack techniques (model editing, poisoning, evasion, transfer attacks, model inversion, model extraction) offer practical insights into vulnerabilities, while a discussion of defense techniques such as adversarial training, certified robustness, and gradient masking provide the possible countermeasures.

In the rest of the course we discuss some common software security weakness categories, such as input validation, improper use of security features, time and state, error handling and using vulnerable components, putting them in the context of machine learning wherever relevant. Finally, participants are equipped with a solid foundation in cryptography, covering essential knowledge and skills every developer should have as well as techniques of special interest for machine learning such as multiparty computation, differential privacy, and fully homomorphic encryption.

• Cyber security basics
• Machine learning security
• Input validation
• Security features
• Time and state
• Errors
• Using vulnerable components
• Cryptography for developers
• Wrap up

• Getting familiar with essential cyber security concepts
• Learning about various aspects of machine learning security
• Attacks and defense techniques in adversarial machine learning
• Input validation approaches and principles
• Identify vulnerabilities and their consequences
• Learn the security best practices in Python
• Correctly implementing various security features
• Managing vulnerabilities in third party components
• Understanding how cryptography supports security
• Learning how to use cryptographic APIs correctly in Python