Certified AI Cyber Defense Specialist (CAICD) Certification Program by Tonex
Certified AI Cyber Defense Specialist (CAICD) Certification Program by Tonex prepares security and AI teams to defend advanced intelligent systems against determined attackers. Participants learn how threat actors exploit large language models, retrieval augmented generation pipelines, and AI supply chains, then design controls that close those gaps. The program blends practical patterns for securing prompts, data flows, and integrations with a strong focus on governance and risk management aligned with leading frameworks.
By the end of the course, learners understand how to embed cybersecurity into every stage of AI model design, deployment, and operations. Emphasis is placed on preventing data leakage, model abuse, and systemic weaknesses that can undermine digital trust. This certification equips professionals to lead AI security reviews, guide architecture decisions, and communicate defensive priorities clearly to stakeholders across the business.
Learning Objectives
- Understand attacker goals, tactics, and techniques against LLMs and RAG enabled systems.
- Analyze and secure AI pipelines, data flows, and integrations across complex environments.
- Apply structured approaches to AI red teaming and reporting of exploitable weaknesses.
- Use SBOM and supply chain practices to reduce exposure to compromised AI components.
- Align AI model risk treatment with NIST AI RMF and related governance practices.
- Integrate cybersecurity controls into AI lifecycle governance, policy, and technical design.
- Communicate AI cybersecurity risks, tradeoffs, and mitigation strategies to business and technical leaders.
Audience
- Cybersecurity Professionals
- AI and ML engineers and architects
- Security architects and security engineers
- Red team operators and offensive security testers
- SOC analysts and threat hunters
- Governance risk and compliance specialists
- Technical product managers and AI program leads
Program Modules
Module 1: AI Attack Surface and OWASP LLM
- Threat landscape for AI systems
- Mapping OWASP LLM Top Ten
- Prompt injection and data exfiltration
- Jailbreak analysis and mitigation
- Hardening model endpoints and APIs
- Monitoring for abuse and misuse
Module 2: Securing Retrieval Augmented Generation Pipelines
- RAG architecture and trust boundaries
- Data source vetting and provenance
- Index poisoning and integrity risks
- Guarding retrieval and ranking logic
- Protecting embeddings and vector stores
- Observability for RAG behavior drift
Module 3: AI Red Team Tactics and Operations
- Scoping and rules of engagement
- Adversary emulation for AI systems
- Attack playbooks for LLM and RAG
- Tooling and automation for red teams
- Evidence collection and impact scoring
- Reporting findings and remediation guidance
Module 4: AI Supply Chain Security and SBOM
- AI components and dependency mapping
- SBOM creation for AI powered services
- Third party model and service evaluation
- Risks in datasets, libraries, and plugins
- Continuous assurance of upstream suppliers
- Contractual and vendor security expectations
Module 5: AI Risk Governance with NIST Framework
- NIST AI RMF core functions and profiles
- Identifying and prioritizing AI risks
- Risk registers for AI driven capabilities
- Metrics for effectiveness and residual risk
- Alignment with enterprise security programs
- Board and regulator facing risk narratives
Module 6: Designing Secure GenAI Reference Architectures
- Reference patterns for secure GenAI use
- Control layering across data and access
- Integration with identity and zero trust
- Protecting sensitive and regulated content
- Multi cloud and hybrid deployment patterns
- Roadmap for maturing AI cyber defense posture
Exam Domains
- Foundations of AI Cyber Defense and Threats
- Adversarial Testing and AI Abuse Techniques
- Secure Design and Hardening of AI Systems
- Data Protection, Privacy, and Model Integrity
- Governance Risk Compliance for AI Security
- Operationalization and Continuous Improvement of AI Defense
Course Delivery
The course is delivered through a combination of lectures, interactive discussions, expert demonstrations, and project based learning led by experienced AI security practitioners. Participants engage with real world patterns, case examples, and curated resources tailored to AI model security, LLM protection, and RAG pipeline defense. Learning materials include readings, reference templates, and structured exercises that help participants translate concepts into concrete controls they can apply in their own environments.
Assessment and Certification
Participants are evaluated through quizzes, structured assignments, and a capstone style assessment that tests their ability to analyze AI architectures and recommend effective defensive measures. Upon successful completion of all required assessments and achieving the required exam score, participants receive the Certified AI Cyber Defense Specialist (CAICD) Certification from Tonex, validating their capability to secure modern AI ecosystems.
Question Types
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria
To pass the Certified AI Cyber Defense Specialist (CAICD) Certification Program by Tonex exam, candidates must achieve a score of 70% or higher.
Strengthen your organization by putting robust AI cyber defense skills in the hands of your key technical and security leaders. Enroll in the Certified AI Cyber Defense Specialist (CAICD) Certification Program by Tonex to learn how to anticipate AI driven threats, design resilient architectures, and embed strong cybersecurity practices into every intelligent system you deploy.