Certified AI Threat Modeling Engineer (CAITME)

Certified AI Threat Modeling Engineer (CAITME) Certification Program by Tonex

The Certified AI Threat Modeling Engineer (CAITME) Certification Program by Tonex prepares professionals to systematically identify, analyze, prioritize, and mitigate threats affecting Artificial Intelligence (AI) systems throughout their lifecycle. The program addresses traditional security concerns as well as AI-specific attack surfaces involving data, models, prompts, inference pipelines, application interfaces, third-party components, autonomous agents, and supporting infrastructure.

Participants learn structured approaches for defining system boundaries, identifying assets and trust relationships, developing abuse cases, analyzing adversarial behavior, evaluating attack paths, and selecting appropriate security controls. The program also examines risks associated with generative AI, Large Language Models (LLMs), agentic AI, model supply chains, data poisoning, prompt injection, model manipulation, information disclosure, and unauthorized actions.

Effective AI threat modeling strengthens cybersecurity by identifying weaknesses before adversaries can exploit them. It helps cybersecurity teams connect technical vulnerabilities with business impact, operational risk, and realistic attacker objectives. Strong cybersecurity threat modeling also improves security requirements, architecture decisions, control selection, testing priorities, and continuous AI assurance.

Learning Objectives

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

• Explain fundamental concepts, terminology, and lifecycle considerations associated with AI threat modeling.
• Identify AI assets, attack surfaces, dependencies, trust boundaries, threat actors, and security assumptions.
• Develop structured threat scenarios, abuse cases, misuse cases, and adversarial attack paths for AI-enabled systems.
• Analyze threats affecting training data, models, prompts, inference services, APIs, agents, tools, and supporting infrastructure.
• Prioritize AI threats using likelihood, impact, exposure, business criticality, and available security controls.
• Integrate cybersecurity requirements and defensive controls into AI architectures, development processes, deployment workflows, and operational environments.
• Create, maintain, communicate, and continuously improve professional AI threat models and associated risk documentation.

Audience

This certification program is designed for:

• Cybersecurity Professionals
• AI Engineers and AI Architects
• Security Engineers and Security Architects
• Threat Modeling Practitioners
• AI Security and Assurance Professionals
• Application Security Professionals
• DevSecOps and Platform Security Teams
• Generative AI and Agentic AI Developers
• Risk Management Professionals
• AI Governance and Compliance Professionals
• Security Operations and Incident Response Professionals
• Technical Program and Engineering Leaders

Program Modules

Module 1: Foundations of AI Threat Modeling

• Principles and objectives of threat modeling for AI-enabled systems
• Differences between traditional application threats and AI-specific threats
• AI system lifecycle stages and associated security considerations
• Identification of business objectives, security objectives, and critical assets
• Understanding adversaries, capabilities, motivations, access levels, and attack opportunities
• Establishing assumptions, constraints, dependencies, and threat modeling scope
• Connecting AI threat modeling with organizational risk and security engineering processes

Module 2: Mapping AI Assets and Trust Boundaries

• Identifying models, datasets, prompts, applications, APIs, services, and supporting assets
• Mapping data flows across training, development, deployment, and inference environments
• Defining trust boundaries between users, applications, models, services, agents, and external systems
• Identifying privileged components, administrative functions, credentials, secrets, and sensitive information
• Evaluating third-party models, datasets, libraries, plugins, tools, and external service dependencies
• Documenting entry points, communication paths, interfaces, permissions, and security assumptions
• Developing system representations that support repeatable threat identification and analysis

Module 3: Identifying Adversarial AI Attack Paths

• Developing threat scenarios based on realistic attacker objectives and capabilities
• Analyzing data poisoning, model poisoning, evasion, extraction, and inference-related threats
• Identifying prompt injection, indirect prompt injection, jailbreak, and instruction manipulation risks
• Assessing unauthorized access, privilege abuse, sensitive information disclosure, and model misuse
• Examining attack chains that combine AI-specific and conventional cybersecurity techniques
• Developing abuse cases and misuse cases for high-value AI capabilities and workflows
• Tracing attack paths across data, models, applications, infrastructure, users, and external dependencies

Module 4: Engineering Controls for AI Threats

• Translating identified threats into measurable security and engineering requirements
• Applying identity, access control, authorization, isolation, segmentation, and least-privilege principles
• Protecting datasets, models, prompts, credentials, configurations, and sensitive AI artifacts
• Designing input validation, output controls, content handling, and secure interface protections
• Applying monitoring, logging, detection, alerting, and incident response considerations
• Evaluating preventive, detective, corrective, and compensating controls against identified threats
• Documenting residual risks, control limitations, assumptions, and recommended security improvements

Module 5: Threat Modeling Generative and Agentic AI

• Assessing attack surfaces created by Large Language Models and generative AI applications
• Evaluating prompt manipulation, sensitive information exposure, unsafe output, and excessive agency
• Identifying risks involving retrieval-augmented generation, embeddings, vector stores, and external knowledge sources
• Analyzing agent permissions, memory, planning, tool access, delegation, and autonomous actions
• Evaluating multi-agent communication, trust relationships, identity, authorization, and task handoffs
• Assessing risks from plugins, connectors, external APIs, third-party services, and dynamic content
• Defining security boundaries and controls for increasingly autonomous AI-enabled workflows

Module 6: Operationalizing AI Threat Modeling Programs

• Integrating threat modeling into AI design, development, acquisition, deployment, and change-management processes
• Establishing roles and responsibilities across security, engineering, risk, governance, and business teams
• Prioritizing threats using business impact, exploitability, exposure, likelihood, and existing controls
• Creating actionable threat registers, mitigation plans, security requirements, and risk acceptance records
• Reviewing threat models after architecture changes, new integrations, incidents, and emerging adversarial techniques
• Defining metrics for threat coverage, mitigation status, residual risk, and control effectiveness
• Applying a practical training approach that includes exercises, real-world case studies, and examples of processes and documentation used in AI threat modeling projects

Exam Domains

1. AI System Security Architecture
2. Adversarial Techniques and Abuse Cases
3. Data, Model, and Supply Chain Risk
4. Generative and Autonomous Application Security
5. Risk Prioritization and Control Validation
6. Governance, Documentation, and Continuous Assurance

Course Delivery

The Certified AI Threat Modeling Engineer (CAITME) Certification Program is delivered through instructor-led lectures, interactive discussions, guided workshops, practical exercises, real-world case studies, and project-based learning facilitated by professionals experienced in AI security, cybersecurity, threat modeling, and risk management. Participants examine representative AI architectures, attack scenarios, threat models, security requirements, risk records, and mitigation documentation.

The program uses a practical training approach that includes exercises, real-world case studies, and examples of processes and documentation used in AI threat modeling projects. Participants develop skills for applying structured threat analysis methods to enterprise AI systems, generative AI applications, agentic AI environments, and other AI-enabled solutions.

Assessment and Certification

Participants are assessed through quizzes, practical assignments, scenario-based threat analysis activities, and a capstone AI threat modeling project. Assessment activities evaluate the participant’s ability to identify assets, define trust boundaries, analyze adversarial behavior, develop attack scenarios, prioritize risks, recommend controls, and document threat-modeling results.

Participants must also successfully complete the CAITME certification examination. Upon meeting the program and examination requirements, participants will receive the Certified AI Threat Modeling Engineer (CAITME) certification.

Question Types

• Multiple Choice Questions (MCQs)
• Scenario-based Questions

Passing Criteria

To pass the Certified AI Threat Modeling Engineer (CAITME) Certification Training exam, candidates must achieve a score of 70% or higher.

Develop the expertise to identify AI attack surfaces, anticipate adversarial behavior, prioritize security risks, and engineer defensible AI systems. Enroll in the Certified AI Threat Modeling Engineer (CAITME) Certification Program by Tonex and strengthen your ability to protect modern AI technologies through structured, practical threat modeling.