Certified AI Defense Professional (CAIDP)

By /

Certified AI Defense Professional (CAIDP)

The Certified AI Defense Professional (CAIDP) program is a comprehensive certification designed to address the growing need for robust safety and security in AI systems. As AI becomes integral to digital infrastructure, this certification ensures that professionals are equipped with the knowledge, tools, and frameworks required to build, deploy, and govern AI solutions securely.

This program covers end-to-end AI application security, from secure design and development of AI models and agents to real-world implementation of AI security taxonomies and reference architectures.

Learning Objectives

By the end of this program, participants will be able to:

  • Identify and mitigate security and safety threats in AI systems.
  • Apply AI security reference architectures for robust system design.
  • Secure AI agents and chatbots from injection, hallucination, and misuse.
  • Implement secure deployment pipelines for foundation models.
  • Understand and apply AI safety and security taxonomies.
  • Manage governance, risk, and compliance (GRC) across AI lifecycle.
  • Respond to emerging threats and implement defensive strategies.

Target Audience

This program is ideal for:

  • AI/ML Engineers and Developers
  • Security Architects
  • DevSecOps Professionals
  • Risk and Compliance Officers
  • Chief Information Security Officers (CISOs)
  • Product and Platform Security Teams
  • Cybersecurity Analysts
  • Technical Project Managers working with AI systems

Program Modules

Module 1: Introduction to AI Defense

  • Threat landscape in AI and ML
  • Overview of AI misuse, adversarial AI, and data poisoning
  • Key regulatory and compliance trends

Module 2: AI Application Security

  • Secure SDLC for AI-powered systems
  • Common attack vectors: prompt injection, model inversion, evasion
  • Security best practices for AI pipelines

Module 3: AI Agents and Chatbots

  • Architecture of conversational agents
  • Threat models: prompt hijacking, impersonation, jailbreaks
  • Defense mechanisms for generative agents

Module 4: Foundation Model Security

  • Understanding foundation models (e.g., GPT, BERT, LLaMA)
  • Data provenance and training integrity
  • Securing model inference and APIs

Module 5: AI Security Reference Architectures

  • Zero Trust for AI systems
  • Secure AI infrastructure patterns
  • LLMOps and secure deployment blueprints

Module 6: AI Safety and Security Taxonomy

  • OWASP Top 10 for LLMs
  • MITRE ATLAS framework
  • Risk classifications and mitigations
  • Applying NIST AI RMF

Module 7: Governance, Risk, and Compliance (GRC)

  • AI audit trails and logging
  • Security testing and AI red teaming
  • Policy controls for ethical AI use

 Certification Exam

  • Format: 75 Multiple Choice Questions
  • Duration: 90 minutes
  • Passing Score: 70%
  • Delivery Mode: Online

Exam Domains:

  • AI Security Fundamentals – 15%
  • AI Application Security – 20%
  • AI Chatbots and Agents – 15%
  • Foundation Model Defense – 15%
  • AI Security Architecture – 15%
  • AI Safety & Risk Taxonomies – 10%
  • GRC for AI – 10%

Credential Awarded

Upon successful completion, participants will be awarded the title:

  • Certified AI Defense Professional (CAIDP)
  • Credential Validity: 1 year
  • Continuing Education: 16 CPEs required for renewal

Add-ons

  • Lab Exercises: Real-world red team/blue team simulations
  • Capstone Project: Secure design of a generative AI application
  • Toolkit: Templates for threat modeling, GRC checklists, and mitigation maps

Lab Structure

Lab 1: Recon and AI System Mapping

  • Objective: Simulate early reconnaissance against an AI application to identify assets, APIs, model types, and exposed endpoints.
  • Red Team Task: Use tools like nmap, Burp Suite, and Postman to map model endpoints and metadata leaks.
  • Blue Team Task: Harden endpoints, enforce rate limiting, and obfuscate model/version metadata.

Lab 2: Prompt Injection and Jailbreaking a Chatbot

  • Objective: Attempt prompt injection to alter chatbot behavior and extract confidential data.
  • Red Team Task: Launch controlled attacks (e.g., DAN prompts, escape sequences).
  • Blue Team Task: Implement and test prompt sanitization, guardrails, and fine-tuned safe response models.

Lab 3: Model Extraction and Inference Snooping

  • Objective: Use adversarial queries to infer model parameters or extract proprietary training data.
  • Red Team Task: Perform model inversion and extraction with Tramer’s technique.
  • Blue Team Task: Add noise via differential privacy and monitor abnormal query patterns.

Lab 4: Adversarial Example Crafting

  • Objective: Generate subtle input perturbations to mislead an image or NLP model.
  • Red Team Task: Use Foolbox or TextAttack to bypass classification systems.
  • Blue Team Task: Detect and neutralize adversarial inputs using adversarial training or anomaly detection.

Lab 5: Data Poisoning in Training Pipeline

  • Objective: Modify training data to introduce backdoors into an AI model.
  • Red Team Task: Inject poisoned samples to cause malicious behavior.
  • Blue Team Task: Use data lineage tracking, cleaning tools, and hash validation to detect and reject tampered datasets.

Lab 6: Foundation Model Abuse Prevention

  • Objective: Explore abuse vectors on LLM APIs (e.g., misuse for phishing, hate speech generation).
  • Red Team Task: Engineer misuse prompts.
  • Blue Team Task: Build content filters, reinforce moderation layers, and deploy RLHF (Reinforcement Learning from Human Feedback).

Lab 7: Secure LLMOps Pipeline

  • Objective: Secure the continuous integration and deployment (CI/CD) pipeline of an AI application.
  • Red Team Task: Attempt supply chain injection and model tampering during deployment.
  • Blue Team Task: Enforce signing, model registry access control, pipeline hardening.

Tools

  • For Red Teams:

TextAttack, Foolbox, Burp Suite, Recon-ng, TruffleHog, PromptBench

  • For Blue Teams:

OpenAI Moderation API, LLM Firewall, MLflow, Snorkel, Diffprivlib, Anchors (LIME/SHAP)

Outcomes

  • Master practical attack and defense methods for real-world AI systems.
  • Understand AI-specific threat modeling and incident response.
  • Gain experience in securing conversational AI, vision/NLP models, and CI/CD environments.

Integration

These lab exercises are part of the CAIDP Certification and can be used in:

  • Workshops
  • Online virtual lab environments (e.g., JupyterHub, Google Colab)
  • Capstone evaluation
Scroll to Top