Red Teaming AI Systems: Offensive Security for ML Models Training Course

This instructor-led, live training in Malta (online or onsite) is aimed at intermediate-level enterprise leaders who wish to understand how to govern and secure AI systems responsibly and in compliance with emerging global frameworks such as the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

By the end of this training, participants will be able to:

• Understand the legal, ethical, and regulatory risks of using AI across departments.
• Interpret and apply major AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Establish security, auditing, and oversight policies for AI deployment in the enterprise.
• Develop procurement and usage guidelines for third-party and in-house AI systems.

This instructor-led, live training in Malta (online or onsite) is aimed at intermediate-level to advanced-level AI developers, architects, and product managers who wish to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while incorporating security controls like input validation, human-in-the-loop oversight, and output guardrails.

By the end of this training, participants will be able to:

• Understand the core vulnerabilities of LLM-based systems.
• Apply secure design principles to LLM app architecture.
• Use tools such as Guardrails AI and LangChain for validation, filtering, and safety.
• Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

This instructor-led, live training in Malta (online or onsite) is aimed at beginner-level IT security, risk, and compliance professionals who wish to understand foundational AI security concepts, threat vectors, and global frameworks such as NIST AI RMF and ISO/IEC 42001.

By the end of this training, participants will be able to:

• Understand the unique security risks introduced by AI systems.
• Identify threat vectors such as adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models like the NIST AI Risk Management Framework.
• Align AI use with emerging standards, compliance guidelines, and ethical principles.

This instructor-led, live training in Malta (online or onsite) is aimed at advanced-level professionals who wish to implement and evaluate techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy in real-world machine learning pipelines.

By the end of this training, participants will be able to:

• Understand and compare key privacy-preserving techniques in ML.
• Implement federated learning systems using open-source frameworks.
• Apply differential privacy for safe data sharing and model training.
• Use encryption and secure computation techniques to protect model inputs and outputs.

This instructor-led, live training in Malta (online or onsite) is aimed at intermediate-level engineers and security professionals who wish to secure AI models deployed at the edge against threats such as tampering, data leakage, adversarial inputs, and physical attacks.

By the end of this training, participants will be able to:

• Identify and assess security risks in edge AI deployments.
• Apply tamper resistance and encrypted inference techniques.
• Harden edge-deployed models and secure data pipelines.
• Implement threat mitigation strategies specific to embedded and constrained systems.

This instructor-led, live training in Malta (online or onsite) is aimed at intermediate-level machine learning and cybersecurity professionals who wish to understand and mitigate emerging threats against AI models, using both conceptual frameworks and hands-on defenses like robust training and differential privacy.

By the end of this training, participants will be able to:

• Identify and classify AI-specific threats such as adversarial attacks, inversion, and poisoning.
• Use tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and test models.
• Apply practical defenses including adversarial training, noise injection, and privacy-preserving techniques.
• Design threat-aware model evaluation strategies in production environments.