Certified AI Secure Coding & Implementation Specialist (CASCI) Certification Program by Tonex
Certified AI Secure Coding and Implementation Specialist CASCI validates hands on capability to build, integrate, and deploy AI systems with secure engineering discipline. It targets the real implementation risks that show up in GenAI apps, RAG pipelines, model serving layers, and ML platforms. Learners develop practical habits for writing safer ML and GenAI code, shaping prompts defensively, and designing AI services that resist abuse.
The program strengthens cybersecurity outcomes by reducing prompt injection exposure, data leakage paths, and supply chain weaknesses across dependencies and pipelines. It also emphasizes secure operationalization so teams can ship AI features without expanding attack surface. By the end, participants can align secure AI delivery with modern engineering workflows while meeting organizational cybersecurity expectations and governance needs.
Learning Objectives
- Apply secure ML and GenAI coding patterns in production codebases
- Design prompts and tool flows to reduce injection and misuse
- Implement secure RAG with controlled retrieval, grounding, and filtering
- Harden models and serving layers against tampering and abuse
- Manage dependencies, packages, and artifacts with supply chain controls
- Build secure AI APIs with strong auth, validation, and rate controls
- Strengthen cybersecurity by reducing AI driven attack surfaces and data exposure
Audience
- AI Developers
- ML Engineers
- Software Engineers
- DevSecOps Teams
- Cybersecurity Professionals
Program Modules
Module 1: Secure ML and GenAI Coding Basics
- Secure data handling in training and inference code
- Input validation patterns for ML feature pipelines
- Safe serialization and deserialization practices
- Error handling that avoids sensitive leakage
- Logging and telemetry with privacy guardrails
- Secure configuration patterns across environments
Module 2: Prompt Security and Injection Defense
- Threat modeling for prompt and tool interactions
- Prompt templates with boundary and role controls
- Output constraints and structured response validation
- Tool invocation controls and least privilege actions
- Jailbreak resistance techniques for applications
- Abuse monitoring signals and response playbooks
Module 3: Secure Retrieval Augmented Generation Design
- Document ingestion validation and sanitization steps
- Retrieval filtering for sensitive and toxic content
- Context window controls and citation aware policies
- Grounding checks and hallucination risk controls
- Tenant isolation and access control in retrieval
- Safe caching strategies for embeddings and results
Module 4: Model Hardening and Tamper Resistance
- Serving layer protections against model extraction
- Defensive rate limiting and anomaly detection hooks
- Integrity checks for model artifacts and weights
- Secure update and rollback practices for models
- Watermarking concepts and provenance workflows
- Abuse testing strategies for harmful behaviors
Module 5: Dependency, Secrets, and Key Protection
- Dependency pinning and provenance verification steps
- SBOM oriented practices for AI application stacks
- Secrets storage patterns for tokens and credentials
- Key rotation workflows for AI service integrations
- Secure environment variables and runtime injection
- Access reviews and least privilege enforcement
Module 6: Secure AI CI CD and API Delivery
- Secure build pipelines for model and app artifacts
- Policy gates for risk checks and approvals
- Secure API authentication and authorization patterns
- Request validation and schema enforcement controls
- Observability for abuse detection and incident triage
- OWASP LLM risks mapping to engineering controls
Exam Domains
- AI Application Threat Modeling and Governance
- Secure Software Architecture for AI Services
- AI Supply Chain Risk and Artifact Integrity
- AI Security Testing and Validation Strategy
- Incident Response for AI Enabled Systems
- Compliance, Privacy, and Secure Operations for AI
Course Delivery
The course is delivered through a combination of lectures, interactive discussions, hands on workshops, and project based learning, facilitated by experts in the field of Certified AI Secure Coding and Implementation Specialist CASCI. 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 Coding and Implementation Specialist CASCI.
Question Types
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria
To pass the Certified AI Secure Coding and Implementation Specialist (CASCI) Certification Training exam, candidates must achieve a score of 70% or higher.
Build AI that ships fast without shipping risk. Enroll in the CASCI program by Tonex and prove you can implement secure AI systems that stand up to real world threats.