AI Security Training in Lille

This instructor-led, live training in Lille (online or onsite) is designed for IT professionals at beginner to intermediate levels who aim to understand and implement AI TRiSM within their organizations. <\/p>

Upon completion of this training, participants will be able to: <\/p>

• Comprehend the core concepts and significance of managing trust, risk, and security in AI. <\/li>
• Identify and mitigate risks linked to AI systems. <\/li>
• Apply security best practices specific to AI. <\/li>
• Understand regulatory compliance and ethical implications for AI. <\/li>
• Develop strategies for effective AI governance and management. <\/li> <\/ul>

This instructor-led training in Lille delves into governance, identity management, and red-teaming for agentic AI. It empowers advanced practitioners to architect secure deployments, enforce least-privilege access, and conduct adversarial testing to address real-world production threats.

This instructor-led, live training in Lille (online or onsite) is aimed at intermediate-level AI and cybersecurity professionals who wish to understand and address the security vulnerabilities specific to AI models and systems, particularly in highly regulated industries such as finance, data governance, and consulting.

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

• Understand the types of adversarial attacks targeting AI systems and methods to defend against them.
• Implement model hardening techniques to secure machine learning pipelines.
• Ensure data security and integrity in machine learning models.
• Navigate regulatory compliance requirements related to AI security.

This instructor-led, live training in Lille (online or onsite) is aimed at advanced-level security professionals and ML specialists who wish to simulate attacks on AI systems, uncover vulnerabilities, and enhance the robustness of deployed AI models.

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

• Simulate real-world threats to machine learning models.
• Generate adversarial examples to test model robustness.
• Assess the attack surface of AI APIs and pipelines.
• Design red teaming strategies for AI deployment environments.

This instructor-led session in Lille empowers advanced professionals to secure TinyML workflows on edge hardware. You will master the implementation of privacy-centric methods, reinforcement of models against adversarial threats, and the application of best practices for secure data processing in resource-limited environments.

This instructor-led, live training in Lille (online or onsite) is designed for intermediate-level engineers and security professionals who wish to protect 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 Lille (online or onsite) is designed for experienced professionals seeking to implement and assess techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within practical machine learning workflows.

Upon completion of this training, participants will be capable of:

• Grasping and contrasting essential privacy-preserving methodologies in ML.
• Building federated learning systems utilizing open-source frameworks.
• Employing differential privacy to facilitate secure data sharing and model training.
• Leveraging encryption and secure computation methods to shield model inputs and outputs.

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

Upon completing this training, participants will be able to:

• Grasp the legal, ethical, and regulatory risks associated with using AI across various departments.
• Interpret and implement key AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Establish security, auditing, and oversight policies for AI deployment within the enterprise.
• Create procurement and usage guidelines for both third-party and in-house AI systems.

This instructor-led, live training in Lille (online or onsite) targets intermediate to advanced AI developers, architects, and product managers who want to identify and mitigate risks associated with LLM-powered applications, such as prompt injection, data leakage, and unfiltered outputs. The course also covers implementing security controls like input validation, human-in-the-loop oversight, and output guardrails.

Upon completion of this training, 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 instructor-led, live training in Lille (online or onsite) is designed for intermediate-level machine learning and cybersecurity professionals who wish to understand and mitigate emerging threats against AI models. The course combines conceptual frameworks with 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, including adversarial attacks, inversion, and poisoning.
• Use tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and evaluate model resilience.
• Implement practical defenses such as adversarial training, noise injection, and privacy-preserving techniques.
• Develop threat-aware model evaluation strategies for production environments.

This instructor-led, live training in Lille (online or onsite) is designed for beginner-level IT security, risk, and compliance professionals seeking to grasp 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:

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

Guided by the latest OWASP GenAI Security Project recommendations, participants will learn to identify, assess, and mitigate AI-specific threats through hands-on exercises and real-world scenarios.

This course provides a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants learn how AI security differs from traditional web security, explore common AI-specific threats such as prompt injection, RAG poisoning, and agent abuse, and understand how to protect AI systems using layered defenses including WAFs, AI gateways, API security, and guardrails. Through hands-on labs and real-world examples, students gain the skills to identify AI attack patterns, secure LLM-based applications, and deploy effective runtime defenses for production environments.

This course teaches software developers how to build AI-powered applications securely by design. Participants learn to protect chatbots, copilots, RAG pipelines, and AI agents against AI-specific threats such as prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output. The course covers secure prompt design, RAG security, least-privilege access, guardrails, and red-team testing, helping developers build AI features that are secure, reliable, and resilient in real-world environments.

This instructor-led live training in Lille (online or onsite) is aimed at security engineers and compliance officers who wish to harden EXO deployments, control model access, and govern AI workloads running entirely on-premise.

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