Certified AI Application Security Specialist (C-AIAS) Certification Program by Tonex
Certified AI Application Security Specialist C-AIAS by Tonex prepares security and engineering teams to defend large language models and AI enabled applications against modern attacks. The program focuses on real world threats such as prompt injection, data poisoning, model abuse, insecure plugins, and compromised RAG pipelines. Participants learn how to harden AI APIs, lock down integrations, and apply secure design patterns across end to end AI workflows.
The cybersecurity impact of this program is significant because vulnerable AI systems can leak sensitive data, expose business logic, and become high value targets for adversaries. By blending application security fundamentals with AI specific controls, the course helps organizations elevate their overall cybersecurity posture while still innovating with LLMs and intelligent services. Graduates leave with practical methods to assess, secure, and continuously monitor AI applications in demanding enterprise environments.
Learning Objectives
- Understand core concepts of AI application security across the full lifecycle
- Identify LLM and AI app attack surfaces in enterprise architectures
- Analyze prompt injection and data poisoning techniques used by real world adversaries
- Apply OWASP Top 10 for LLMs to design and code reviews
- Design secure AI APIs and RAG pipelines with strong access and data controls
- Strengthen organizational cybersecurity posture by integrating AI security into existing programs
- Develop actionable improvement roadmaps for securing AI projects and platforms
Audience
- Cybersecurity Professionals
- Application Security Engineers
- AI and Machine Learning Engineers
- Security Architects and DevSecOps Engineers
- Software Developers working with LLMs and AI APIs
- Cloud and Platform Engineers supporting AI workloads
- Technical Product Owners and Engineering Managers
Program Modules
Module 1: LLM And AI Security Foundations
- AI application security fundamentals
- Threat landscape for LLM based systems
- Differences from traditional web security
- Security risks in model lifecycle
- Shared responsibility across teams
- Mapping AI assets and data flows
Module 2: Mapping AI Application Attack Surfaces
- Components of AI enabled applications
- Entry points to LLM powered services
- Plugin extensions and tool integrations
- Third party model and API exposure
- Data stores and vector databases
- Trust boundaries and threat mapping
Module 3: Prompt Injection And Jailbreak Defense
- Types of prompt injection attacks
- Indirect injection via external content
- System prompt hardening strategies
- Guardrails and response filtering patterns
- Red teaming prompts and payload testing
- Monitoring for abuse and jailbreak attempts
Module 4: Data Poisoning Detection And Mitigation
- Poisoning risks in training data
- Risks in feedback and fine tuning loops
- Supply chain threats in data pipelines
- Detection heuristics and anomaly signals
- Data validation and provenance controls
- Recovery and cleanup after poisoning
Module 5: OWASP Top Ten For LLM Security
- Overview of OWASP LLM risk categories
- Insecure output handling and injection
- Excessive agency and unsafe tool use
- Sensitive information disclosure in responses
- Overreliance and model misuse risks
- Applying OWASP guidance in reviews
Module 6: Securing AI APIs And Microservices
- API authentication and authorization models
- Protecting AI gateways and proxies
- Rate limiting and abuse prevention patterns
- Input and output validation for AI endpoints
- Secrets and key management for model access
- Secure logging and privacy aware telemetry
Module 7: Protecting RAG Pipelines And Connectors
- Architecture of RAG based systems
- Indexing and retrieval security concerns
- Hardening connectors and data sources
- Preventing prompt injection via retrieved content
- Securing vector databases and indices
- End to end testing of RAG pipelines
Module 8: Secure AI Deployment And Monitoring
- Secure configuration for model hosting
- Isolation and segmentation for AI services
- Integration with SIEM and SOC workflows
- Runtime threat detection around AI traffic
- Policy enforcement for high risk actions
- Continuous improvement using security metrics
Module 9: Governance Compliance And Secure Operations
- AI security policies and standards
- Alignment with existing cybersecurity frameworks
- Risk assessments for AI driven projects
- Vendor and third party due diligence
- Documentation and evidence for audits
- Operating secure AI at enterprise scale
Exam Domains
- Core Concepts Of AI Application Security
- Threat Modeling And Risk Analysis For LLM Systems
- Defensive Engineering For AI Workflows And APIs
- Protection Of Data, RAG Pipelines, And Integrations
- Operational Monitoring And Incident Handling For AI Platforms
- Governance Compliance And Strategic AI Security Management
Course Delivery
The course is delivered through a combination of lectures, interactive discussions, guided demonstrations, and project based learning, facilitated by experts in AI application security. Participants explore patterns and anti patterns drawn from real incidents involving LLMs, AI APIs, and RAG architectures. They benefit from structured exercises, case studies, and group activities that reinforce the cybersecurity implications of design and implementation choices. Blended delivery options support onsite and virtual participation to accommodate global teams.
Assessment and Certification
Participants are assessed through quizzes, structured assignments, and a capstone style final evaluation focused on securing an AI centric architecture. The assessment process emphasizes practical application of concepts such as attack surface mapping, prompt injection defense, and protection of RAG pipelines. Upon successful completion of the course and final evaluation, participants receive the Certified AI Application Security Specialist C-AIAS Certification from Tonex, demonstrating advanced capability in securing AI applications within modern cybersecurity programs.
Question Types
- Multiple Choice Questions MCQs
- Scenario-based Questions
Passing Criteria
To pass the Certified AI Application Security Specialist C-AIAS Certification Training exam, candidates must achieve a score of 70% or higher.
Enhance your ability to secure LLMs and AI powered applications before adversaries exploit them. Enroll in the Certified AI Application Security Specialist C-AIAS Certification Program by Tonex and equip yourself and your organization with the skills needed to protect modern AI systems and strengthen overall cybersecurity resilience.