Certified Offensive AI Security Professional (C|OASP) Training in Russia

The Certified Offensive AI Security Professional (C|OASP) certification by EC-Council is the world’s leading hands-on program focused on offensive and defensive AI security.
It equips cybersecurity professionals with the knowledge and tools to identify, exploit, and defend against vulnerabilities in machine learning and AI systems.

Participants learn to perform AI Red Team operations, simulate real-world attacks on AI models, and develop robust security frameworks to safeguard AI-driven environments.

Upon successful completion, participants earn the EC-Council Certified Offensive AI Security Professional (C|OASP) credential — validating advanced expertise in AI penetration testing, adversarial security, and defense.

Bilginç IT Academy is an Official Accredited Training Partner of EC-Council, delivering EC-Council-authorized cybersecurity trainings and certification programs globally.

• Cybersecurity professionals and AI security specialists

• Red Team / Blue Team experts

• Machine learning engineers, data scientists

• Ethical hackers and penetration testers

By the end of this course, participants will:

• Understand the attack surface of AI systems

• Conduct adversarial, evasion, extraction, and poisoning attacks

• Perform offensive AI Red Team assessments

• Implement security controls for AI models and pipelines

• Apply Secure MLOps principles to protect model integrity

• Integrate ethical hacking methodologies into AI security practices

1. Introduction to AI Security

• Core concepts and threat landscape

• Attack surface analysis of AI and ML systems

• Common vulnerabilities in data, model, and API layers

2. Offensive AI Techniques (Red Team)

• Model inversion and data extraction attacks

• Data poisoning and model corruption

• Crafting adversarial examples and bypass methods

• Evasion and backdoor exploitation

3. Defensive AI Strategies (Blue Team)

• Secure model training and validation

• Model hardening and regularization

• Adversarial detection and mitigation

• Incident response in AI-driven environments

4. Tools and Frameworks

• AI Red Teaming toolkits

• TensorFlow, PyTorch, and ML security frameworks

• Explainable AI (XAI) for security evaluation

• AI security automation and testing tools

5. Legal, Ethical, and Regulatory Aspects

• Responsible AI security practices

• Compliance frameworks (EU AI Act, ISO/IEC, NIST AI RMF)

• AI governance and corporate security policies

6. Practical Labs

• Hands-on adversarial attack simulations

• AI model penetration testing

• Red Team operation exercises in controlled labs