AI+ Security Practitioner™

This certification validates foundational knowledge of AI-driven cybersecurity concepts and assesses understanding of security principles, threats, and controls. The exam evaluates competency in applying core cybersecurity knowledge within AI-enabled environments.

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Level
beginner

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Duration
50 MCQs, 90 minutes

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Format
Self-Paced Online

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Certification
AI CERTs®

Self-Paced Online

USD $ N/A

Instructor-Led Online

At a Glance: Course + Exam Overview

Our training approach is human‑centred and outcomes‑driven. We focus on what learners can apply confidently.

Program Name

AI+ Security Practitioner™

Prerequisites

Basic understanding of AI and cybersecurity concepts, including security principles and terminology.
Knowledge of security operations such as threat detection, risk management, vulnerability assessment, and incident response.
Familiarity with networking, systems, cloud environments, and security controls.
Understanding data protection, privacy, compliance, and secure data handling practices.
Basic programming and automation awareness for security workflows.
Awareness of responsible AI, security governance, and AI-powered security tools.

Exam Format

90 minutes

Certification Modules

Module 1: Computing, Linux, and Operating System Foundations

You will learn about computer systems, operating systems, Linux administration, file systems, commands, user management, permissions, authentication, and access control concepts.

Module 2: Networking Fundamentals and Traffic Analysis

You will learn networking concepts, IP addressing, protocols, TCP/IP communication, DNS, network security, traffic analysis, firewalls, IDS/IPS, and VPN technologies.

Module 3: Python for Security and Automation

You will learn Python programming fundamentals and how to use scripting for security automation, log analysis, data processing, and efficient security workflows.

Module 4: Cybersecurity Foundations and Threat Landscape

You will learn cybersecurity principles, risks, vulnerabilities, attack surfaces, security controls, common cyber threats, and industry security frameworks.

Module 5: Cryptography, Authentication, and Identity Security

You will learn encryption, hashing, digital signatures, TLS security, authentication methods, identity management, access controls, and identity protection practices.

Module 6: Introduction to Artificial Intelligence and Machine Learning

You will learn AI, ML, and Deep Learning fundamentals, learning approaches, ML lifecycle, datasets, model evaluation, and AI applications in cybersecurity.

Module 7: AI Applied to Security Detection and Threat Hunting

You will learn AI-based threat detection, behavioral analytics, anomaly detection, threat intelligence, threat hunting, MITRE ATT&CK mapping, and AI-assisted SOC operations.

Module 8: AI Security, LLM Security, and Responsible AI

You will learn LLMs, Generative AI, AI copilots, RAG, OWASP LLM security risks, AI vulnerabilities, governance, and responsible AI practices.

Module 9: Offensive Security for AI Systems

You will learn AI threat modeling, attack surfaces, adversarial attacks, STRIDE methodology, AI vulnerabilities, red teaming, and security testing approaches.

Module 10: Security Operations, Incident Response, and Malware Analysis

You will learn about SOC operations, SIEM concepts, incident response, malware analysis, threat investigation, and AI-assisted security operations.

Module 11: Governance, Compliance, and Ethical AI Security

You will learn security governance, risk management, AI governance, compliance, privacy principles, and responsible AI security practices.

Module 12: Capstone Project — AI-Driven Security Operations and Defense

You will apply cybersecurity skills through an end-to-end AI security project involving threat analysis, AI risk assessment, incident response, and professional security reporting.

Industry Opportunities

AI Security Analyst

Protect AI systems by identifying vulnerabilities, monitoring threats, and implementing security controls across AI applications.

Cybersecurity Analyst

Monitor security events, analyze cyber threats, and safeguard enterprise networks and AI-powered environments.

SOC Analyst

Detect, investigate, and respond to security incidents using Security Operations Center (SOC) tools and AI-driven monitoring solutions.

Threat Hunter

Proactively identify advanced cyber threats, uncover hidden attacks, and strengthen organizational security through continuous threat hunting.

Security Engineer

Design, implement, and maintain secure infrastructure, applications, and AI systems to protect against evolving cyber risks.

AI Risk Specialist

Assess AI-related security risks, ensure regulatory compliance, and develop governance frameworks for responsible AI adoption.

Incident Response Analyst

Investigate cybersecurity incidents, contain threats, and coordinate recovery efforts to minimize business impact and improve resilience.

Frequently Asked Questions

What does this course cover?

This course covers AI security, cybersecurity foundations, threat detection, AI-driven security operations, LLM security, and responsible AI practices.

Who can take this course?

This course is designed for learners with basic knowledge of AI, cybersecurity, networking, programming, security operations, and responsible AI.

What is the exam format for this certification?

The certification exam includes 50 questions, requires a 70% passing score, and has a 90-minute online proctored format.

What skills will I gain from this course?

You will gain skills in AI security, threat detection, cybersecurity operations, LLM security, automation, and responsible AI practices.

What tools will I explore during the course?

You will explore tools such as Scikit-learn, TensorFlow, PyTorch, Kali Linux, Wireshark, Nmap, Wazuh, Splunk, and OWASP ZAP.

Prerequisites

Basic understanding of AI and cybersecurity concepts, including security principles and terminology.
Knowledge of security operations such as threat detection, risk management, vulnerability assessment, and incident response.
Familiarity with networking, systems, cloud environments, and security controls.
Understanding data protection, privacy, compliance, and secure data handling practices.
Basic programming and automation awareness for security workflows.
Awareness of responsible AI, security governance, and AI-powered security tools.

Exam Blueprint

Computing, Linux and Operating System Foundations	5%
Networking Fundamentals and Traffic Analysis	9%
Python for Security and Automation	9%
Cybersecurity Foundations and Threat Landscape	9%
Cryptography, Authentication & Identity Security	9%
Introduction to Artificial Intelligence and Machine Learning	9%
AI Applied to Security Detection and Threat Hunting	9%
AI Security, LLM Security and Responsible AI	9%
Offensive Security for AI Systems	8%
Security Operations, Incident Response and Malware Analysis	8%
Governance, Compliance and Ethical AI Security	8%
Capstone Project — AI-Driven Security Operations and Defense	8%

Core AI Tools Covered

Scikit-learn

TensorFlow

PyTorch

Kali Linux

Wireshark

Nmap

Wazuh

Splunk

OWASP ZAP