Cybersecurity for AI
Three certification levels from fundamentals to enterprise-scale AI security. Master threat modeling, prompt injection defense, and autonomous agent security.
Choose Your Certification Level
Three levels of AI Security certification, from fundamentals to enterprise-scale autonomous agent security.
AI Security Essentials
Understand how AI systems are attacked before you defend them
Prerequisites
- Basic programming (Python or Java)
- Understanding of software development
- Familiarity with APIs
- Basic cybersecurity concepts
Topics Covered
- AI vs Traditional Security: Why everything you know changes
- OWASP Top 10 for LLM Applications — hands-on lab
- AI Attack Surfaces: training data, APIs, model endpoints
- Threat modeling fundamentals for AI systems
- Input validation and output filtering for GenAI apps
- Zero-trust basics applied to AI workloads
- Monitoring & alerting for ML model anomalies
Exam Details
What You'll Achieve:
- •Map the full attack surface of any AI application
- •Apply OWASP LLM Top 10 mitigations immediately
- •Build your first AI threat model from scratch
Prompt Hacking & GenAI Defense
Learn to attack like a hacker, defend like a pro
Prerequisites
- Foundation level OR equivalent
- Python + LLM API experience
- API security basics
- Understanding of AI/ML concepts
Prerequisite: Foundation level or equivalent hands-on AI experience
Topics Covered
- Prompt Injection deep-dive: direct, indirect, cross-agent attacks
- Jailbreaking techniques: DAN, role-play, multi-language injection
- System prompt extraction — live demo and countermeasures
- OWASP LLM Top 10 exploitation in real apps — hands-on lab
- Designing multi-layer guardrails: pre, in, and post-processing
- Securing RAG pipelines and vector databases
- AWS Bedrock / Azure AI Content Safety guardrail implementation
- HarmBench red-teaming framework in practice
Exam Details
What You'll Achieve:
- •Execute and defend against prompt injection in production
- •Design guardrails that stop real attacks without breaking UX
- •Secure RAG pipelines and LLM API endpoints end-to-end
Agentic AI Security & Governance
Secure autonomous AI agents at enterprise scale
Prerequisites
- Intermediate level OR 2+ years AI/security engineering
- LLM + cloud infrastructure experience
- Enterprise security architecture knowledge
- Compliance frameworks understanding
Prerequisite: Intermediate level or 2+ years professional AI security experience
Topics Covered
- Agentic AI threat landscape: scheming, goal misalignment, tool abuse
- Zero-trust architecture for autonomous AI agents
- MITRE ATLAS framework — mapping AI attacks to enterprise systems
- STRIDE-AI threat modeling for multi-agent systems
- Adversarial ML: model extraction, membership inference, poisoning
- MLOps security: secure CI/CD, container hardening, secrets rotation
- EU AI Act, NIST RMF, ISO 42001 — compliance in practice
- Enterprise AI governance and responsible AI security frameworks
Exam Details
What You'll Achieve:
- •Architect zero-trust security for autonomous AI agents
- •Build enterprise threat models using MITRE ATLAS and STRIDE-AI
- •Implement governance and compliance frameworks that pass audits
Exam Preparation Guide
Everything you need to prepare and pass your AI Security certification. Follow our structured approach for success.
Study Resources
Official documentation and hands-on practice tools
Official Documentation
OWASP Top 10 for LLM Applications
Complete vulnerability guide with examples
NIST AI Risk Management Framework
Official government guidelines
MITRE ATLAS Framework
Adversarial threat landscape for AI
EU AI Act
Regulatory requirements
Practice Labs
Prompt Injection Playground
Interactive attack simulations
RAG Security Testing Lab
Vector database vulnerability testing
AI Threat Modeling Tool
Practice creating threat models
Guardrail Implementation Lab
Build security controls
Exam Format
Question types and scoring breakdown
Multiple Choice
Single and multiple correct answers testing theoretical knowledge
Scenario-Based
Real-world security situations and problem-solving
Practical Analysis
Configuration analysis and threat modeling
Level-Specific Preparation
Focus areas and study time for each certification level
Foundation Level
Focus areas and preparation timeline
Key Concepts
Study Timeline
Intermediate Level
Focus areas and preparation timeline
Key Concepts
Study Timeline
Advanced Level
Focus areas and preparation timeline
Key Concepts
Study Timeline
Test Day Preparation
Before, during, and after your exam
Before the Exam
- •Test webcam and microphone
- •Ensure stable internet
- •Clear workspace
- •Have government ID ready
- •Install ProctorWell software
During the Exam
- •Read questions carefully
- •Manage time (2 min/question)
- •Flag difficult questions
- •Stay focused on screen
- •Trust your preparation
After the Exam
- •Results appear immediately
- •Download certificate
- •Share on LinkedIn
- •Add to resume/CV
- •Plan next level
Exam Information
Everything you need to know about our certification exams.
Proctored Exams
AI-monitored via webcam and screen monitoring through ProctorWell
Instant Results
Get your results immediately upon completion. No waiting period.
Lifetime Validity
Your certification never expires and remains accessible forever.
Important Notes
- • All exams are proctored via webcam and screen monitoring
- • You must have a stable internet connection and quiet environment
- • Results are available immediately upon completion
- • You can retake any exam after 7 days if you don't pass
- • Each level builds upon the previous one - we recommend taking them in order
Ready to Get Certified?
Take the next step in your AI security career with our industry-recognized certification.