Certified AI Secure Network Architect (CAISNA) Certification Program by Tonex
The Certified AI Secure Network Architect (CAISNA) program equips professionals with the expertise to secure networks supporting distributed AI pipelines. Participants learn advanced techniques for segmenting AI workloads, securing containerized AI models, protecting data pipelines, and managing large language model (LLM) traffic. The course emphasizes edge-to-cloud network security, AI security zones, and isolation practices for training and inference. Designed for professionals dealing with IoT and AI convergence, CAISNA addresses critical risks unique to AI-native architectures and distributed systems. By the end, learners will be ready to design, assess, and implement secure AI infrastructure strategies.
Audience:
- Network architects
- AI/ML security engineers
- IoT and edge computing professionals
- DevSecOps engineers
- Cybersecurity analysts
- IT infrastructure leads
Learning Objectives:
- Understand the security risks in AI-based distributed systems
- Design secure network architectures for ML pipelines
- Apply microsegmentation and zero trust to AI networks
- Protect AI containers and inference data flows
- Implement isolation for sensitive AI training environments
Program Modules:
Module 1: Network Security for Distributed AI Pipelines
- Fundamentals of distributed AI networking
- Risks in edge-to-cloud AI communication
- Role of SDN in AI traffic control
- Threat vectors in AI inference and training
- Network policies for AI data flow
- Architecting secure multi-hop AI pipelines
Module 2: Segmenting and Isolating AI Workloads
- Microsegmentation for ML/AI traffic
- Zero trust enforcement in AI zones
- Identity-based network segmentation
- Securing east-west AI communications
- Policy-based isolation techniques
- Avoiding lateral movement in AI clusters
Module 3: Securing AI Containers and Service Meshes
- Overview of Istio and Calico for AI
- Service mesh encryption policies
- Network policy configuration best practices
- Securing ML containers in Kubernetes
- Managing multi-tenant AI workloads securely
- AI-aware traffic filtering strategies
Module 4: Data Pipeline Protection for AI Inference
- Secure transport for AI output and models
- TLS and mTLS for AI data channels
- AI model integrity and tamper protection
- Detecting exfiltration via AI APIs
- Logging and auditing AI pipeline traffic
- Building trust boundaries for AI inferencing
Module 5: Security Zones and Air-Gapped ML Training
- Designing air-gapped training environments
- Secure update paths for isolated AI systems
- Risk analysis of connected vs. air-gapped AI
- Managing cross-domain data movement
- Implementing jump hosts for AI model transfers
- Role of DMZs in AI development networks
Module 6: LLM Firewalling and AI Microsegmentation
- Identifying LLM-specific network threats
- Limiting prompt injection through segmentation
- Deploying LLM-aware proxies and firewalls
- Preventing unauthorized LLM API calls
- Detecting anomalous LLM traffic patterns
- Network-level controls for LLM-based systems
Exam Domains:
- AI Network Threat Landscape
- Secure AI Infrastructure Architecture
- AI Pipeline Data Protection
- Edge and IoT AI Security
- Container and Service Mesh Security
- AI Access Control and Monitoring
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of AI-secure network architecture. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI Secure Network Architect (CAISNA).
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the Certified AI Secure Network Architect (CAISNA) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to secure the future of AI-powered networks? Enroll in the CAISNA Certification Program by Tonex today.