GIAC AI Platform Security (GAIPS)
The GIAC AI Platform Security (GAIPS) certification validates a practitioner’s ability to audit and secure Generative AI applications and large language model (LLM) development pipelines.
GAIPS certification holders demonstrate hands-on skills protecting data flows, model integrations, APIs, and deployment workflows against emerging threats across the AI lifecycle.
Areas Covered
- Generative AI common terminology and fundamental concepts
- Tuning, augmenting, and aligning foundational models for custom business requirements
- Designing and securing agentic systems and AI integrations
- AI application architecture and development frameworks
- Implementing MLOps and MLSecOps practices
- Responsible AI usage and risk management strategies
Who is GAIPS for?
- AI/ML Engineers
- Application Security Engineers
- Software Developers
- Cloud Security Engineers
- Security Managers, Practitioners, and Consultants
- Security Auditors and Risk Managers
CyberLive proves you've got what it takes
CyberLive is a hands-on exam format that replaces traditional multiple-choice testing with performance-based challenges in realistic lab environments to validate real-world capability.
Virtual Machines:
Full-scale lab systems that behave like physical computers: install, attack, defend, and run services.
Real Security Tools:
Exact tools used by professionals every day including all the quirks and challenges
Authentic Code:
Real code, real exploits, real impacts
Exam Format
- 1 proctored exam
- 56 questions
- 2 hours
- Minimum passing score of 67%
Note: GIAC periodically reviews and may update certification specifications to ensure fairness, validity, and reliability. Using a psychometric standard-setting study, GIAC has set the passing score for the GAIPS exam at 67% for all candidates who receive the exam version released on or after July 28, 2026.
To confirm the exam format and passing score that apply to your specific attempt, please refer to the Certification Information section of your GIAC account: https://exams.giac.org/pages/attempts.
Certification Delivery
GIAC certification attempts will be activated in your GIAC account after your application has been approved and according to the terms of your purchase. Details on delivery will be provided along with your registration confirmation upon payment. You will receive an email notification when your certification attempt has been activated in your account. You will have 120 days from the date of activation to complete your certification attempt.
NOTE: All GIAC Certification exams are web-based and required to be proctored. There are two proctoring options: remote proctoring through ProctorU, and onsite proctoring through PearsonVUE. Click here for more information.
Exam Certification Objectives & Outcome Statements
- Agentic Systems and AI IntegrationsThe candidate will demonstrate an ability to design and assess agentic AI systems, including inter-agent communication, context management, and protocol-level security.
- AI and LLM FoundationsThe candidate will demonstrate a technical understanding of generative AI concepts, large language models (LLMs), and transformer architectures, including their capabilities, limitations, and attack surface.
- AI Application Architecture and Development FrameworksThe candidate will demonstrate an ability to evaluate and apply GenAI application architectures and development frameworks, identifying supply chain and integration-layer vulnerabilities.
- AI Infrastructure and Deployment SecurityThe candidate will demonstrate the ability to assess security risks associated with hosting GenAI applications and models across cloud and other infrastructure environments, and to identify appropriate security controls.
- AI Risk Management and Strategic ApplicationThe candidate will demonstrate an understanding of how to apply structured threat modeling methodologies and long-term security planning to AI systems to support responsible AI use while maintaining business agility.
- Development Pipelines and MLOps SecurityThe candidate will demonstrate the knowledge to secure data workflows, training pipelines, and model lifecycle operations through MLOps and MLSecOps practices to implement end-to-end security from development to production.
- Knowledge Augmentation and RetrievalThe candidate will demonstrate an understanding of architectures that extend model knowledge via retrieval mechanisms and evaluate the security risks of vector databases and external knowledge sources.
- Model Customization and AlignmentThe candidate will demonstrate an understanding of techniques for adapting and aligning foundation models through fine-tuning and moderation controls, including associated security implications.
Practice Tests
- These tests are a simulation of the real exam allowing you to become familiar with the test engine and style of questions.
- Practice exams are a gauge to determine if your preparation methods are sufficient.
- The practice bank questions are limited so you may encounter the same question on practice tests when multiple practice tests are purchased.
- Practice exams never include actual exam questions.
Other Resources
- Training is available in a variety of modalities including live training and OnDemand
- Practical work experience can help ensure that you have mastered the skills necessary for certification
- College level courses or self-paced study through other programs or materials may meet the needs for mastery
- Understand the procedure to contest exam results