This course provides foundational knowledge and skills for professionals seeking to understand and manage security risks related to artificial intelligence (AI) systems. It focuses on AI threat modeling, governance, data security, adversarial resilience, and compliance considerations. The course is structured into six modules designed to build progressive expertise.
Module 1: Introduction to AI Security
Objectives
- Understand the intersection between AI and cybersecurity.
- Identify common AI attack surfaces and vulnerabilities.
- Explore the principles of responsible AI and risk mitigation.
Topics
- Overview of AI systems and machine learning lifecycle
- Key components of AI models: data, algorithms, and infrastructure
- Threats unique to AI: data poisoning, model inversion, prompt injection
- The role of AI in cybersecurity defense and offense
- Security standards and frameworks for AI (e.g., NIST AI RMF, ISO/IEC 23894)
Practical Exercise
- Map AI components to potential security vulnerabilities in a sample model.
Module 2: Data Security and Privacy in AI Systems
Objectives
- Learn methods to secure data throughout the AI pipeline.
- Understand privacy-preserving techniques for data used in training and inference.
Topics
- Data lifecycle in AI systems
- Data collection risks: bias, provenance, and integrity
- Secure data storage and access controls
- Techniques for privacy preservation: differential privacy, homomorphic encryption, federated learning
- Legal and regulatory aspects: GDPR, CCPA, and AI data governance
Practical Exercise
- Evaluate anonymization methods applied to a sample dataset.
Module 3: AI Threat Modeling and Risk Assessment
Objectives
- Develop AI-specific threat models.
- Understand how to quantify and prioritize AI risks.
Topics
- Traditional threat modeling frameworks (STRIDE, MITRE ATT&CK) adapted for AI
- Identifying AI-specific threat actors and attack vectors
- Adversarial machine learning overview
- Model extraction, evasion, and poisoning attacks
- Tools and techniques for red-teaming AI systems
Practical Exercise
- Conduct a mini threat model assessment of a machine learning pipeline.
Module 4: Model Security and Adversarial Defense
Objectives
- Explore methods to protect AI models against manipulation and exploitation.
- Learn techniques to detect and mitigate adversarial inputs.
Topics
- Model robustness and explainability as security mechanisms
- Adversarial training and defensive distillation
- Monitoring model drift and anomaly detection
- Model access management and deployment security
- Secure inference environments and sandboxing
Practical Exercise
- Implement a simple adversarial attack and defense using a public ML model.
Module 5: AI Governance, Ethics, and Compliance
Objectives
- Understand governance frameworks and ethical principles in AI security.
- Align AI system design with compliance and accountability requirements.
Topics
- AI governance structures and roles (CISO, Chief AI Officer, compliance teams)
- Ethical implications of AI security decisions
- Regulatory landscape: EU AI Act, U.S. Executive Orders, OECD AI Principles
- Auditing and documentation best practices
- Transparency, interpretability, and human oversight
Practical Exercise
- Create a governance checklist for AI project approval and review.
Module 6: Secure AI Operations and Incident Response
Objectives
- Learn how to integrate AI systems into secure operational environments.
- Develop an AI-focused incident response plan.
Topics
- Secure MLOps and DevSecOps integration
- Continuous monitoring of AI systems and pipelines
- Detection and response to AI-based threats
- Recovery and post-incident analysis for AI failures or breaches
- Best practices for securing AI APIs and model endpoints
Practical Exercise
- Design an incident response workflow for an AI-driven service.
Tonex offers Certified AI Security Fundamentals (CAISF), a 2-day course where participants gain proficiency in assessing and enhancing AI system resilience as well as learning best practices for security AI models and data.
Attendees also learn the fundamentals of AI security and how to identify and mitigate potential risks in AI applications.
Upcoming Training:
Certified AI Security Fundamentals (CAISF) Certification Course by Tonex
- Public Training with Exam: Dec 11-12, 2025
Tonex is the leader in AI certifications, offering more than six dozen courses, including in the Certified GenAI and LLM Cybersecurity Professional area, such as:
Certified AI Compliance Officer (CAICO) certification
Certified AI Electronic Warfare (EW) Analyst (CAIEWS)
Certified GenAI and LLM Cybersecurity Professional (CGLCP) for Professionals
Certified GenAI and LLM Cybersecurity Professional for Data Scientists
Certified GenAl and LLM Cybersecurity Professional for Developers Certification
Additionally, Tonex offers even more specialized AI courses through its Neural Learning Lab (NLL.AI). Check out the certification list here.
For more information, questions, comments, contact us.
