The course provided me with a solid understanding of IT service desk operations and troubleshooting techniques. It has improved my ability to handle user issues with confidence and professionalism.
AI+ Security Expert™
AT-2102
Protect and Secure: Leverage Intelligent AI Solutions
This certification validates intermediate-level knowledge of AI-driven cybersecurity concepts and assesses competency in applying security controls, risk management practices, and AI-enabled threat detection techniques. The exam evaluates understanding of advanced security principles within AI-augmented environments.
- High-Quality Video, E-book & Audiobook
- Modules Quizzes
- AI Mentor
- Access for Tablet & Phone
- Online Proctored Exam with One Free Retake
- Hands-on Practices
Why This Certification Matters
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Comprehensive AI-Cybersecurity Integration: Validates intermediate-level competency in AI-driven security defense mechanisms.
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Practical Python Programming Skills: Demonstrates competency in detecting and responding to modern cyber threats.
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Advanced Threat Detection Techniques: Exam includes scenario-based questions reflecting real-world cybersecurity incidents.
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Cutting-Edge AI Algorithms: Validates readiness for intermediate to senior-level cybersecurity responsibilities.
At a Glance: Course + Exam Overview
Program Name
AI+ Security Expert™
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Prerequisites
Interest in AI technologies, basic computer science knowledge, curiosity to learn, and awareness of AI ethics and data privacy.
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Delivery
Online labs, projects, case studies
Outcome
Industry-recognized credential + hands-on experience
Who Should Enroll?
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Cybersecurity Professionals: Professionals who want to stay updated on the latest AI-driven security tools, technologies, and techniques to strengthen cybersecurity practices.
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IT Professionals and System Administrators: Those who want to use AI capabilities to detect, analyze, and respond to security threats more effectively and efficiently.
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Cloud Architects and Engineers: Professionals who want to integrate AI-powered security solutions into cloud architectures and enhance the protection of cloud environments.
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Risk Management Specialists: Those who want to apply AI-driven approaches to identify, assess, and mitigate cybersecurity risks.
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Business Leaders and Decision Makers: Professionals who want to understand the role of AI in cybersecurity and make informed decisions about security investments and strategies.
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Software Developers: Developers who want to understand AI integration in security tools, applications, and secure software development practices.
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Security Consultants and Advisors: Professionals who want to gain advanced knowledge of AI technologies to provide strategic cybersecurity guidance and recommendations.
Skills You'll Gain
AI-driven threat detection and cybersecurity analysisMachine learning applications in security operationsPython-based security automation and tool developmentAI-powered malware and email threat detectionNetwork anomaly detection and risk analysisAI-based authentication and security solutionsPenetration testing using AI techniquesResponsible AI, security governance, and compliance practicesDesigning AI-powered cybersecurity solutions through practical projects
What You'll Learn
- This certification validates intermediate-level knowledge of AI-driven cybersecurity concepts and assesses competency in applying security controls, risk management practices, and AI-enabled threat detection techniques. The exam evaluates understanding of advanced security principles within AI-augmented environments.
- 1.1 AI Security Scope and Enterprise Context
- 1.2 AI Security Roles and Responsibilities
- 1.3 AI Security Use Cases and Opportunities
- 1.4 Use Cases
- 1.5 Case Studies
- 2.1 AI Application Components
- 2.2 Assets, Trust Boundaries and Data Flows
- 2.3 Modern Cybersecurity Architecture
- 2.4 Threat Modelling for AI Applications
- 2.5 Use Cases
- 2.6 Case Studies
- 3.1 Python for AI Security Tasks
- 3.2 Python Libraries for Security Engineering
- 3.3 Working with Security Data
- 3.4 Cybersecurity Data Analytics
- 3.5 Automation Patterns and Safe Scripting
- 3.6 Use Cases
- 3.7 Case Studies
- 4.1 GenAI Application Components
- 4.2 Secure Design Patterns
- 4.3 Secure AI SDLC
- 4.4 Use Cases
- 4.5 Case Studies
- 5.1 Prompt Injection Techniques
- 5.2 Sensitive Information Disclosure Risks
- 5.3 Unsafe Output Handling
- 5.4 Use Cases
- 5.5 Case Studies
- 6.1 RAG System Architecture
- 6.2 RAG-Specific Risks
- 6.3 RAG Controls and Monitoring
- 6.4 Use Cases
- 6.5 Case Studies
- 7.1 AI Data Security
- 7.2 Model and Artifact Security
- 7.3 ML Pipeline and MLSecOps Controls
- 7.4 AI-Based Detection and Model Monitoring
- 7.5 Adversarial ML Risks
- 7.6 Use Cases
- 7.7 Case Studies
- 8.1 AI Deployment Patterns
- 8.2 Identity and Secret Controls
- 8.3 Abuse Prevention and Cloud Controls
- 8.4 Use Cases
- 8.5 Case Studies
- 9.1 AI Security Telemetry
- 9.2 Detection Engineering for AI Threats
- 9.3 AI Incident Response
- 9.4 Use Cases
- 9.5 Case Studies
- 10.1 AI Governance Foundations
- 10.2 Privacy and Data Protection
- 10.3 Assurance Artifacts and Evidence
- 10.4 Use Cases
- 10.5 Case Studies
- 11.1 Red Teaming Methodologies for AI Systems
- 11.2 Advanced Threat Vectors
- 11.3 Red Team Reporting
- 11.4 Use Cases
- 11.5 Case Studies
- 12.1 Proactive Threat Intelligence Dashboard
- 12.2 AI-Driven Cybersecurity Solution Development
- 12.3 AI-Powered SOC Automation
- 12.4 LLM Security Monitoring and Defense System
- 1.1 What Are AI Agents?
- 1.2 Key Capabilities of AI Agents in Advanced Cybersecurity
- 1.3 Applications and Trends for AI Agents in Advanced Cybersecurity
- 1.4 How Does an AI Agent Work?
- 1.5 Core Characteristics of AI Agents
- 1.6 Types of AI Agents
Tools You'll Master
Prerequisites
- Basic understanding of cybersecurity concepts and practices.
- Familiarity with AI and machine learning fundamentals.
- Knowledge of cloud computing and application security.
- Experience with security assessment methodologies.
- Understanding of programming and automation concepts.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50 Correct)
Format
Multiple Choice Questions (MCQ)
Delivery Method
Online via proctored exam platform
Exam Blueprint:
- AI Security Context, Scope and Opportunities - 5%
- AI Application Architecture and Threat Modelling - 9%
- Applied Python Automation for AI Security Evidence - 9%
- GenAI Application Security Controls - 9%
- Prompt Injection, LLM Risk Testing, and Adversarial Attacks - 9%
- RAG and Knowledge System Security - 9%
- AI Data, Model, ML Pipeline and Detection Security - 9%
- Secure AI Deployment: Cloud, API and Identity - 9%
- AI Security Monitoring and Incident Response - 8%
- AI Governance, Privacy and Compliance - 8%
- Advanced Adversarial Testing, Red Teaming - 8%
- Capstone Project - 8%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Instructor-Led (Live Virtual/Classroom)
- 5 days (live or virtual) Self-Paced: 40 hours of content
- Real-time Q&A, peer collaboration, and hands-on labs
- Led by AI Certified Trainers and delivered through Authorized Training Partners
Self-Paced Online
- 40 hours of content
- Learn anywhere, anytime, with modular quizzes to track progress
Need More Flexibility?
To help you along your way, we can help you set up a customizable payment plan. Just book a call, we’ll provide you with the details!
Let's Arrange a Call9/10 of our learners achieve their learning objectives after successful completionProcess Advisors
The course gave me a clear understanding of network security concepts and practical exposure. It helped me build confidence in handling real-world security tasks.
My path to cloud architecture had its hurdles, but Fortray was there every step of the way. They provided valuable insights into cloud technologies and architectural design. With their guidance, I turned challenges into opportunities, and today, as a Cloud Architect, I help organizations leverage the cloud for their growth and success.
The path to becoming a Cybersecurity Engineer felt like decrypting an impossible algorithm—until Fortray provided the key. Their mentorship was like a robust IPS, detecting and eliminating my weaknesses while optimizing my strengths. Now, I stand as a protector in the ever-evolving IT landscape, proud to carry their legacy forward.
This course strengthened my full-stack development skills and practical understanding. I am more confident in building and managing complete web applications.
This course improved my understanding of project management methodologies and workflows. I gained practical skills to plan and execute projects effectively.
The course provided valuable hands-on experience in data analysis tools and techniques. It enhanced my ability to extract meaningful insights from data.
The training helped me build strong analytical skills and understand how to work with real-world data science problems effectively.
The course helped me understand automation testing frameworks and best practices. I gained practical experience in writing and executing automated test cases.
Frequently Asked Questions
The curriculum covers AI fundamentals, Python programming, machine learning, malware detection, network security, and AI-based threat analysis.
Learners explore tools such as CrowdStrike Falcon, Darktrace Enterprise, SentinelOne Singularity, IBM QRadar Advisor with Watson, and Fortinet FortiAI.
Learners receive an industry-recognized credential along with hands-on experience through projects and case studies.
Learners should have basic computer science knowledge, interest in AI technologies, and awareness of AI ethics and data privacy.
The certification is suitable for learners interested in AI technologies, cybersecurity, threat detection, and security automation.
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