Certified Professional in AI Security (CAIS)

The Certified Professional in AI Security and Machine Learning Defense (CAIS) is an intensive 2-month program that transforms cybersecurity professionals into AI security specialists capable of defending against sophisticated adversarial attacks.

You’ll master both offensive red team techniques and defensive blue team strategies specific to AI/ML systems, from adversarial attacks and model poisoning to runtime monitoring and privacy-enhancing technologies.

Key Features of the Program

Heisenberg Institute CAIS Professional Certification

Industry-recognized credential in AI/ML cybersecurity

Offensive AI Security Masterclass

30+ hours of hands-on adversarial attack techniques and red team methodologies

World-Class Faculty

Learn from former AI security researchers and penetration testing specialists

Practical Attack Portfolio

Execute 5+ simulated adversarial attacks and build 5+ defensive frameworks

Industry-Standard Tools Mastery

Hands-on experience with Adversarial Robustness Toolbox (ART), CleverHans, Foolbox, ModelScan, and AI security frameworks

CTF and Red Team Training

Participate in AI-focused Capture The Flag competitions and red team exercises

Career Acceleration Support

Dedicated career services for highly specialized AI security roles

Cutting-Edge Curriculum

Stay current with the latest AI security risks including strong data validation, model security, and ethical AI practices

15X Higher Practical Application

Unique hands-on program with adversarial attack simulation and defense implementation

Flexible Learning Options

Live online masterclasses on Friday’s and Saturday’s with recorded sessions and virtual lab access

WHO IS THIS PROGRAM FOR

CYBERSECURITY PROFESSIONALS

Security Engineers Specializing in AI/ML Defense
Penetration Testers Expanding to AI Attack Vectors
Security Analysts Adding AI Threat Intelligence
Incident Responders Handling AI Security Breaches

TECHNICAL & LEADERSHIP ROLES

Security Architects Designing AI Security Strategies
CISO Teams Building Enterprise AI Defenses
MLOps Engineers Securing ML Pipelines
AI Developers Building Inherently Secure Systems

ADVANCED SPECIALISTS

Red Team Specialists Targeting AI Systems
Blue Team Defenders Protecting ML Models
Government & Defense Security Professionals
Threat Researchers Studying AI Vulnerabilities

WHY HEISENBERG

50+

Partners across Universities, Research Institutes and AI Companies

Access unparalleled opportunities through our extensive network spanning academia and industry, providing diverse perspectives and real-world connections throughout your learning journey.

12 Weeks of Internship training at AI Partner Companies

Apply classroom knowledge immediately in structured internships with leading AI companies, where you’ll solve actual business challenges alongside industry professionals.

18 industry experts contributing to curriculum design and mentorship

Learn directly from AI practitioners who’ve implemented solutions at scale. Our experts shape curriculum content and provide personalized guidance throughout your training.

3x

faster skill acquisition rate compared to other training entities

Our immersive methodology accelerates learning through guided practice and immediate application, helping you master complex AI concepts and implementations in record time.

24/7

access to learning resources and support community

Never hit a roadblock with our always-available learning platform and vibrant community of peers, mentors, and alumni ready to help anytime.

Certification

Upon successful completion of the program, participants will receive the Certified Professional in AI Security and Machine Learning Defense (CAIS) credential from the Heisenberg Institute for AI and Quantum Computing

Professional Credential: validating hands-on expertise in adversarial attacks, model hardening, and AI threat defense
Elite Career Positioning: for specialized roles as AI Security Engineers, ML Security Architects, and Red Team Specialists

LEARNING PATH

The program is structured into three distinct phases to ensure a seamless learning journey.

Phase 1 (Week 1-2)

Foundations of AI Security & Threat Modeling

ML Lifecycle Security Framework: Vulnerability identification across data collection, model training, deployment, and inference phases.
AI Threat Taxonomy: Comprehensive classification of attacks targeting integrity, confidentiality, availability, and robustness.
Risk Assessment Methodologies: Enterprise-level AI security risk evaluation and threat modeling.
AI Security Tool Ecosystem: Hands-on introduction to ART, CleverHans, Foolbox, and testing environments.
Responsible AI Security Integration: Ethical considerations as security enablers in ML systems.
Threat Intelligence for AI Systems: Understanding attacker motivations, capabilities, and attack surfaces.

Phase 2 (Weeks 3-5)

Offensive AI Security - Red Team Techniques

Adversarial Evasion Attacks: Execute FGSM, C&W, PGD, and advanced perturbation techniques on ML models.
Data Poisoning & Backdoor Attacks: Training data manipulation and trojan implementation methodologies.
Model Extraction & Inversion: Intellectual property theft and data reconstruction attack techniques.
Generative AI Attacks: Prompt injection, jailbreaking, and LLM manipulation strategies.
Membership Inference Attacks: Privacy violation through training data extraction techniques.
Custom Attack Development: Building novel adversarial techniques for specific target architectures.
AI Penetration Testing: Systematic approach to ML system security assessment and vulnerability analysis.
.

Phase 3 (Weeks 6-8)

Defensive Strategies & Model Hardening

Adversarial Defense Techniques: Adversarial training, gradient masking, and input transformation for model resilience.
Data Security & Validation: Anomaly detection, source verification, and feature store protection systems.
Model IP Protection: Watermarking, fingerprinting, and unauthorized usage detection mechanisms.
Runtime Security Monitoring: Baseline establishment, drift detection, and anomaly alerting for production AI.
Secure Inference Architecture: API security, container hardening, and secrets management for ML systems.
Privacy-Enhancing Technologies: Differential privacy, federated learning, and homomorphic encryption implementation.
AI Incident Response: Playbooks for adversarial attacks, model failures, and data breach scenarios.

Program Eligibility Requirements

This is a highly selective program with only 20 candidates accepted per cohort through a rigorous evaluation process. Applications are reviewed holistically, considering motivation, and potential for growth in the AI field. Given the limited seats available, early application is strongly encouraged

Selection Criteria

Statement of purpose demonstrating genuine interest in AI and career goals
5+ years of experience in Information Security or related field (OR)
A bachelor’s degree or final year (undergraduate or postgraduate students) with an average of 50% or higher marks in Cybersecurity or related field (OR)
Candidates certified in CISA, CISSP, CISM certification
Personal interview assessing motivation, learning ability, and cultural fit

Cohort Commencement Dates

The CAIG program admits a limited number of candidates per cohort. Review the intake schedule below and apply early to secure your seat in the next session.

Cohort	Application Closes	Program Starts
Winter Cohort	December 15, 2025	January 15, 2026
Spring Cohort	March 15, 2026	April 15, 2026
Summer Cohort	June 15, 2026	July 15, 2026
Fall Cohort	September 15, 2026	October 15, 2026

Note:
Applicants are encouraged to apply 6–8 weeks prior to the deadline.

Full Program Fee: USD 1,499

Invest in elite AI security expertise with an intensive certification combining offensive red team techniques and defensive strategies that positions you at the forefront of the most critical cybersecurity specialization

Your Value Includes:

Certified Professional in AI Security and Machine Learning Defense (CAIS) — industry-recognized credential awarded by the Heisenberg Institute for AI and Quantum Computing
Offensive & Defensive Security Masterclasses — live expert-led training in adversarial attacks, model poisoning, and ML system defense strategies
Real-World Attack Simulation Portfolio — hands-on experience executing penetration tests and building security frameworks for AI/ML systems