Cyber AI Secure Coder (CAISC) Certification Program by Tonex
Cyber AI Secure Coder (CAISC) Certification Program by Tonex equips software engineers and security teams to build AI-enabled applications that remain secure under modern threat pressure. The program bridges secure coding fundamentals with AI-aware attack patterns such as prompt injection, model manipulation, insecure tool calling, data leakage, and dependency compromise.
Participants learn how to translate secure design principles into practical code decisions across APIs, services, and AI-assisted development workflows. Strong emphasis is placed on preventing defects early through secure requirements, threat modeling, secure defaults, and disciplined code review. Cybersecurity impact is woven throughout the program, helping teams reduce exploitable vulnerabilities, protect sensitive data, and improve resilience against real-world adversaries. Graduates leave with a repeatable approach to secure implementation, verification, and governance that fits product delivery without slowing teams down.
Learning Objectives
- Apply secure coding controls to AI-enabled features and services
- Detect and mitigate prompt and data injection risks
- Implement secure authentication, authorization, and session handling
- Strengthen secrets handling and secure configuration practices
- Improve code review quality using security-focused checklists
- Reduce software risk and improve cybersecurity outcomes in delivery pipelines
Audience
- Software engineers and technical leads
- AI application developers and integrators
- DevSecOps and platform engineers
- Application security engineers
- Security architects and engineering managers
- Cybersecurity Professionals
Program Modules
Module 1: Secure AI Coding Foundations
- Secure coding mindset
- Threat-aware requirements
- Secure defaults and inputs
- Error handling patterns
- Secure logging practices
- Security acceptance criteria
Module 2: AI Threat Modeling For Developers
- Asset and trust mapping
- Prompt injection analysis
- Tool and plugin abuse
- Data flow validation
- Abuse case definition
- Risk-based prioritization
Module 3: Secure Data Handling And Privacy
- Data minimization rules
- PII and PHI controls
- Encryption usage patterns
- Tokenization considerations
- Secure storage practices
- Retention and deletion logic
Module 4: API Security And Access Control
- AuthN and AuthZ patterns
- Least privilege design
- Rate limiting strategy
- Input validation routines
- Secure API contracts
- Session management hygiene
Module 5: Supply Chain And Dependency Security
- Dependency risk triage
- SBOM awareness practices
- Package integrity checks
- Build provenance basics
- Secrets scanning workflows
- Patch and upgrade planning
Module 6: Secure Release And Governance
- Secure review gates
- Security test planning
- Vulnerability remediation flow
- Incident-ready logging design
- Policy and exception handling
- Secure engineering metrics
Exam Domains
- AI Secure Coding Principles
- Adversarial Prompting and Misuse Defense
- Secure Application Data Governance
- Identity, Access, and API Protection
- Software Supply Chain Risk Control
- Secure Delivery Assurance and Governance
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 Cyber AI Secure Coder (CAISC). 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 Cyber AI Secure Coder (CAISC).
Question Types
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria
To pass the Cyber AI Secure Coder (CAISC) Certification Training exam, candidates must achieve a score of 70% or higher.
Build secure AI software with confidence and consistency. Enroll in CAISC by Tonex to standardize secure coding behaviors, reduce AI-driven risk, and strengthen cybersecurity outcomes across your engineering organization.