Cybersecurity in AI Systems Training Course

AAISM represents an advanced framework designed for the assessment, governance, and management of security risks within artificial intelligence systems.

This instructor-led training, available in live online or onsite formats, targets advanced-level professionals seeking to implement robust security controls and governance practices for enterprise AI environments.

Upon completing this program, participants will be equipped to:

• Evaluate AI security risks using industry-recognized methodologies.
• Implement governance models that support the responsible deployment of AI.
• Align AI security policies with organizational objectives and regulatory requirements.
• Strengthen resilience and accountability within AI-driven operations.

Course Format

• Facilitated lectures augmented by expert analysis.
• Practical workshops and assessment-based activities.
• Applied exercises utilizing real-world AI governance scenarios.

Customization Options

• For training tailored to your organization’s AI strategy, please contact us to customize the course.

This instructor-led, live training in France (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.

Artificial Intelligence (AI) introduces new dimensions of operational risk, governance challenges, and cybersecurity exposure for government agencies and departments.

This instructor-led, live training (online or onsite) is aimed at public sector IT and risk professionals with limited prior experience in AI who wish to understand how to evaluate, monitor, and secure AI systems within a government or regulatory context.

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

• Interpret key risk concepts related to AI systems, including bias, unpredictability, and model drift.
• Apply AI-specific governance and auditing frameworks such as NIST AI RMF and ISO/IEC 42001.
• Recognize cybersecurity threats targeting AI models and data pipelines.
• Establish cross-departmental risk management plans and policy alignment for AI deployment.

Format of the Course also allows for the evaluation of participants.

• Interactive lecture and discussion of public sector use cases.
• AI governance framework exercises and policy mapping.
• Scenario-based threat modeling and risk evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in France (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, live training in France (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who aim to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output. The curriculum covers the integration of security controls such as 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 France (online or onsite) is designed for security engineers and compliance officers who want to strengthen EXO deployments, control model access, and govern AI workloads running entirely on-premise.

This instructor-led, live training in France (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.

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

This instructor-led, live training in France (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 France (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, live training in France (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 France (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.

TinyML represents a methodology for deploying machine learning models on low-power, resource-limited devices at the network edge.

This instructor-led, live training (available online or onsite) is designed for advanced professionals aiming to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be able to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI implementations.
• Secure TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data management in constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tools.

Course Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

This course provides comprehensive coverage of governance, identity management, and adversarial testing for agentic AI systems, with a focus on enterprise-safe deployment patterns and practical red-teaming techniques.

Delivered as instructor-led live training (available online or onsite), this program targets advanced-level practitioners seeking to design, secure, and evaluate agent-based AI systems within production environments.

Upon completion of this training, participants will be able to:

• Define governance models and policies to ensure safe agentic AI deployments.
• Design non-human identity and authentication flows for agents, enforcing least-privilege access.
• Implement access controls, audit trails, and observability mechanisms tailored to autonomous agents.
• Plan and execute red-team exercises to identify misuses, escalation paths, and data exfiltration risks.
• Mitigate common threats to agentic systems through policy, engineering controls, and continuous monitoring.

Format of the Course also allows for the evaluation of participants.

• Interactive lectures and threat-modeling workshops.
• Hands-on labs covering identity provisioning, policy enforcement, and adversary simulation.
• Red-team/blue-team exercises and an end-of-course assessment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.