Introduction to AI Trust, Risk, and Security Management (AI TRiSM) Training Course

This instructor-led, live training in Germany (available online or on-site) is designed for intermediate-level enterprise leaders seeking to master the responsible governance and security of AI systems. The course ensures alignment with key global regulatory frameworks, including the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

By the end of the program, participants will be able to:

• Evaluate the legal, ethical, and regulatory risks of AI integration across various business units.
• Apply and interpret major AI governance standards, including the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• Establish comprehensive security, auditing, and oversight policies for enterprise AI deployments.
• Create effective procurement and usage guidelines for third-party and internal AI systems.

This course offers a practical guide to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants will explore how AI security diverges from traditional web security, examine common AI-specific threats such as prompt injection, RAG poisoning, and agent abuse, and learn to protect AI systems through layered defenses including WAFs, AI gateways, API security, and guardrails. Through hands-on labs and real-world scenarios, students will develop the ability to identify AI attack patterns, secure LLM-based applications, and implement effective runtime defenses for production environments.

Designed for intermediate to advanced AI developers, architects, and product managers, this instructor-led live training in Germany (delivered online or on-site) focuses on identifying and mitigating risks associated with LLM-powered applications. It covers critical issues such as prompt injection, data leakage, and unfiltered output, while teaching the integration of security controls like input validation, human-in-the-loop oversight, and output guardrails.

By the end of the session, participants will be able to:

• Understand the core vulnerabilities of LLM-based systems.
• Apply secure design principles to LLM app architecture.
• Utilize 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 course instructs software developers on how to build AI-powered applications with security as a foundational design principle. Participants will learn to protect chatbots, copilots, RAG pipelines, and AI agents from AI-specific threats, including prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output. The curriculum covers secure prompt design, RAG security, least-privilege access, guardrails, and red-team testing, enabling developers to create AI features that are secure, reliable, and resilient in real-world environments.

This instructor-led, live training in Germany (available online or onsite) is designed for intermediate-level AI and cybersecurity professionals. It enables them to comprehend and resolve security vulnerabilities unique to AI models and systems, with a focus on highly regulated fields such as finance, data governance, and consulting.

By the conclusion of this training, participants will have the ability to:

• Identify different forms of adversarial attacks on AI systems and implement corresponding defense mechanisms.
• Apply model hardening techniques to protect machine learning pipelines.
• Secure data integrity within machine learning models.
• Comply with regulatory requirements concerning AI security.

This instructor-led, live training in Germany (delivered online or onsite) is designed for security engineers and compliance officers seeking to harden EXO deployments, manage model access, and govern AI workloads operating exclusively on-premise.

This live, instructor-led session in Germany (offered online or in-person) is tailored for junior IT security, risk, and compliance professionals seeking to master foundational AI security concepts, threat vectors, and global benchmarks such as the NIST AI RMF and ISO/IEC 42001.

By the conclusion of this training, participants will be prepared to:

• Analyze the unique security exposures associated with AI systems.
• Detect specific threat vectors, including adversarial attacks, data poisoning, and model inversion.
• Utilize foundational governance models such as the NIST AI Risk Management Framework.
• Align AI operations with emerging standards, compliance regulations, and ethical norms.

This instructor-led, live training in Germany (online or onsite) is designed for security and ML engineers who need to identify, test, and defend against attacks on ML models and LLM-powered applications.

Rooted in the most recent OWASP GenAI Security Project guidance, this module enables participants to detect, analyze, and address AI-specific threats through practical exercises and real-world case studies.

This live, instructor-led session in Germany (online or on-site) targets advanced professionals aiming to deploy and assess advanced privacy techniques—including federated learning, secure multiparty computation, homomorphic encryption, and differential privacy—within real-world machine learning pipelines.

By the end of the session, participants will be able to:

• Compare and evaluate primary privacy-preserving methods in ML.
• Develop federated learning systems using open-source tools.
• Apply differential privacy for secure data sharing and training processes.
• Protect model data using encryption and secure computation techniques.

This instructor-led session, available online or on-site in Germany, targets senior security professionals and ML experts who want to emulate attacks on AI systems, detect vulnerabilities, and improve the stability of deployed models.

By the end of the training, participants will have the ability to:

• Emulate real-world threats against machine learning models.
• Produce adversarial examples to test model robustness.
• Evaluate the attack surface of AI APIs and pipelines.
• Design red teaming approaches for AI deployment environments.

This instructor-led, live training in Germany (delivered online or on-site) targets intermediate-level engineers and security professionals aiming to protect edge-deployed AI models from threats such as tampering, data exfiltration, adversarial inputs, and physical attacks.

By the conclusion of the program, participants will have the capability to:

• Identify and evaluate 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 training, available online or onsite in Germany, is designed for intermediate-level machine learning and cybersecurity professionals. It provides a comprehensive approach to understanding and mitigating emerging AI threats through both theoretical frameworks and practical defenses like robust training and differential privacy.

By the end of the program, participants will be able to:

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