Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training AI Red Teaming - Securing Adversarial AI Models training are: Knowledge of machine learning, Python, and cybersecurity basics. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training AI Red Teaming - Securing Adversarial AI Models training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training AI Red Teaming - Securing Adversarial A... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training AI Red Teaming - Securing Adversarial AI Models training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training AI Red Teaming - Securing Adversarial AI Models training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training AI Red Teaming - Securing Adversarial AI Models training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master AI red teaming techniques to test model robustness in enterprise settings

Develop professional skills in adversarial attacks on AI

Design certifying test scenarios to secure AI systems

Implement AI red teaming tools in real projects

Optimize AI defenses against advanced threats in certifying training

Evaluate AI model vulnerabilities for professional securing

Training program

Module 1AI Red Teaming Fundamentals: Methodologies and Evaluation Tools (Adversarial Robustness Frameworks, Python Libraries)

Dive into the principles of red teaming applied to AI, install environments like CleverHans and Foolbox to simulate threats, analyze concrete cases of attacks on image classification models, perform your first adversarial example tests on real datasets, produce an initial vulnerability report with practical recommendations to strengthen professional training.

Module 2Advanced AI Red Teaming Attacks: Adversarial Example Generation (FGSM, PGD, CW, Black-Box Attacks)

Explore the most sophisticated AI red teaming attacks, generate imperceptible perturbations using algorithms like Fast Gradient Sign Method and Projected Gradient Descent, test on NLP and computer vision models in enterprise conditions, refine attacks through interactive exercises on Jupyter Notebooks, evaluate the business impact of discovered vulnerabilities for immediate securing.

Module 3AI Red Teaming Defenses and Countermeasures: Robustness and Hardening (Adversarial Training, Detection Mechanisms)

Learn to counter threats using defensive AI red teaming techniques, implement adversarial training on your TensorFlow or PyTorch models, deploy anomaly detectors with isolation forests and autoencoders, simulate critical enterprise scenarios like AI fraud, produce optimized deliverables to certify resilience, transform weaknesses into sustainable competitive advantages.

Module 4Practical AI Red Teaming Simulations: Ongoing Projects and Evaluation (Red Team Operations, Certifying Reporting)

Culminate with a complete team-based AI red teaming simulation, attack and defend a deployed production AI model, use tools like the ART library for comprehensive audits, draft a professional report with success metrics and a securing roadmap, receive personalized expert feedback, leave with certifying skills ready for the enterprise.