Mastering AI Security Boot Camp (TTAI820)

About This Course:

Artificial intelligence is transforming cybersecurity, both as a tool for protection and as a target for emerging threats. Mastering AI Security Boot Camp provides the hands-on skills needed to analyze AI-driven threats, secure machine learning models, and implement defense strategies that safeguard organizations from evolving attacks. This expert-led, interactive course is designed for cybersecurity professionals, data scientists, system administrators, AI engineers, and IT leaders who need to understand and mitigate the unique security risks associated with AI technologies. Technical managers, project leads, and compliance professionals overseeing AI security initiatives will also gain critical insights into risk management, ethical AI security practices, and incident response strategies.

Over three days, you will identify vulnerabilities in AI systems, apply intrusion detection techniques, and strengthen machine learning models against adversarial threats. You will develop practical skills to analyze security incidents, conduct forensic investigations on AI systems, and build response plans that minimize the impact of cyber threats. The course also explores differential privacy, ethical considerations, and the role of AI in cybersecurity automation, ensuring you can balance protection with responsible AI use.

With a 50 percent hands-on approach, this course provides real-world exercises where you will simulate AI security attacks, implement defense strategies, and assess AI-driven security risks in practical scenarios. Whether you are actively securing AI systems or guiding AI adoption within your organization, you will leave with the knowledge and skills to protect machine learning applications, strengthen cybersecurity postures, and respond effectively to AI-related security challenges.

Course Objectives:

By the end of this course, you will be able to:

• Detect and analyze AI security threats. Identify vulnerabilities in machine learning models, recognize adversarial attack methods, and assess risks to AI-driven systems.

• Implement AI-specific intrusion detection and defense strategies. Use AI-powered security tools to safeguard data, models, and networks from evolving cyber threats.

• Develop forensic analysis skills for AI systems. Investigate AI-related security breaches, trace attack vectors, and apply forensic techniques to compromised models.

• Design and execute AI incident response plans. Build structured response strategies to mitigate security threats and reduce the impact of AI-driven cyber incidents.

• Enhance AI privacy and ethical security practices. Apply differential privacy, encryption, and ethical guidelines to secure AI applications while ensuring compliance.

• Prepare for future AI security challenges. Stay ahead of emerging risks, such as deepfake manipulation, AI-driven cyberattacks, and AI security automation trends.

Audience:

This intermediate-level course is designed for cybersecurity professionals, AI engineers, system administrators, and data scientists who need to secure machine learning systems and mitigate AI-driven threats. Individuals in threat analysis, incident response, and IT security roles will gain essential skills to detect vulnerabilities and build AI-specific defense strategies.

Technical managers, compliance professionals, and project leads overseeing AI security initiatives will also benefit from this course by gaining insights into governance, ethical AI considerations, and incident response frameworks. Whether you are directly securing AI systems or ensuring AI security compliance, this course equips you with the expertise to manage risks, strengthen security strategies, and build resilient AI-driven security solutions.

Prerequisites:

To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:

• A foundational understanding of artificial intelligence, including the basic principles, applications, and types of AI.

• Familiarity with basic cybersecurity principles, understanding of threats, defense mechanisms, and incident response.

• Basic Python programming skills and / or a general comfort with coding

• Basic knowledge of computer networks, systems, and how they interact

• Some basic experience in data analysis or basic statistical concepts.