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AKYLADE AI Security Foundation (A/AISF) Full Course & Exam

Name: AKYLADE AI Security Foundation (A/AISF) Full Course & Exam
Rating: 4.5 (190 reviews)

Pass the AKYLADE AI Security Foundation (A/AISF) (AIF-001) exam on your 1st attempt, includes a practice exam!

Created byJason Dion • 3 Million+ Enrollments Worldwide, Dion Training Solutions, Dion Training Solutions • ATO for ITIL & PRINCE2, Dion Training Solutions • ATO for ITIL & PRINCE2, Alyson Laderman, Jason Dion • 2.8 Million+ Enrollments Worldwide

Last updated 4/2026

English

What you'll learn

Cybersecurity professionals entering the AI domain
Risk management practitioners in organizations using AI
Compliance and legal officers responsible for AI governance
AI engineers and developers seeking security and trust alignment
IT auditors, analysts, and consultants specializing in emerging technologies
Professionals preparing for the AISF certification exam (AIF-001)

Course content

22 sections • 184 lectures • 11h 53m total length

Introduction15:13
Explore the AKYLADE AI security foundation certification, its five domains, and the NIST AI RMF core functions govern, map, measure, and manage, plus exam structure and resources.
Download your free study guide0:04
Exam Tips10:41
AI Security Pathway7:24
Checkpoint: Introduction

Machine Learning (ML) and Deep Learning (DL) (OBJ 1.1 and OBJ 1.2)3:18
Machine Learning Techniques (OBJ 1.1)5:35
Explore machine learning techniques that let systems learn from data and improve accuracy, using six types: supervised, unsupervised, reinforcement, semi-supervised, self-supervised, and transfer learning.
Machine Learning Algorithms (OBJ 1.1)3:54
Neural Networks (OBJ 1.2)3:40
Neural Network Architectures (OBJ 1.2)6:56
Training Neural Networks (OBJ 1.2)3:34
Data Preprocessing (OBJ 1.2)4:04
Data preprocessing cleans, normalizes, and transforms raw data to improve model accuracy and training efficiency while reducing bias and enabling effective feature extraction.
Data Preprocessing Methods (OBJ 1.2)3:53
Applications of Deep Learning (OBJ 1.2)5:18
Explore the applications of deep learning across image, video, and speech recognition, fraud detection, and algorithmic trading, powered by cnn, rnn, and lstm models in security, healthcare, finance, and e-commerce.
Case Study: Fraud Detection3:59
Checkpoint: Machine Learning (ML) and Deep Learning (DL)

AI Lifecycle (OBJ 1.3)3:42
Phase One - Plan and Design (OBJ 1.3)4:34
Phase Two - Collect and Process Data (OBJ 1.3)5:25
Phase Three - Build and Use Model (OBJ 1.3)4:37
Build and use the model in phase three of the ai lifecycle by selecting the right algorithm, training and evaluating performance, mitigating bias and ensuring transparency, and deploying with monitoring.
Phase Four - Verify and Validate (OBJ 1.3)4:26
Phase Five - Deploy and Use (OBJ 1.3)3:44
Deploy and use phase converts validated AI models into real-world systems with scalable infrastructure, cloud platforms, seamless integration, regulatory compliance, and ongoing bias mitigation through continuous monitoring.
Phase Six - Operate and Monitor (OBJ 1.3)4:05
Phase Seven - Use or Impacted By (OBJ 1.3)4:10
AI System Key Dimensions (OBJ 1.3)4:17
AI Model Development (OBJ 1.3)5:29
Case Study: Healthfirst (OBJ 1.3)3:58
Checkpoint: AI Lifecycle

AI Actors (OBJ 1.4)3:45
AI Design (OBJ 1.4)3:20
AI Development (OBJ 1.4)2:40
AI Deployment (OBJ 1.4)2:40
Operations and Monitoring (OBJ 1.4)2:32
Track AI system performance through continuous monitoring to detect unauthorized access, anomalies, and drift, and implement security updates to keep models accurate and secure.
Test, Evaluation, Verification, and Validation (TEVV) (OBJ 1.4)2:25
Human Factors (OBJ 1.4)2:07
Domain Experts (OBJ 1.4)3:12
Domain experts bring specialized industry knowledge to AI, ensuring regulatory and ethical compliance, with HIPAA privacy protection and guardrails against hallucinations across healthcare, law, finance, insurance, and defense.
AI Impact Assessment (OBJ 1.4)1:42
Procurement (OBJ 1.4)1:41
Governance and Oversight (OBJ 1.4)1:40
Additional AI Actors (OBJ 1.4)3:44
Case Study: Google Deepmind (OBJ 1.4)3:03
Analyze a case study of Google's DeepMind and the NHS that examines how AI deployment affects patient data privacy, consent, governance, and early diagnosis of acute kidney injury (AKI).
Checkpoint: AI Actors

AI Strategies and Maturity Levels (OBJ 1.5)3:24
Cross-functional Collaboration (OBJ 1.5)3:45
Roles and Responsibilities (OBJ 1.5)4:08
Developing a Strong Culture (OBJ 1.5)3:36
Aligning AI Strategies (OBJ 1.5)5:10
Developing and Implementing AI Strategies (OBJ 1.5)4:22
Develop a Roadmap (OBJ 1.5)3:47
Monitor and Evaluate AI Initiatives (OBJ 1.5)4:01
Monitor and evaluate AI initiatives with continuous metrics and KPIs, regular reviews, and user feedback to ensure they stay effective, secure, aligned with business goals, and scalable over time.
AI Maturity Levels (OBJ 1.5)5:21
Assessing Current State of AI (OBJ 1.5)3:38
Interviews and Surveys (OBJ 1.5)3:10
Identify Capability Gaps (OBJ 1.5)3:21
Case Study: Microsoft (OBJ 1.5)3:01
Checkpoint: AI Strategies and Maturity Levels

AI Risk Assessments (OBJ 2.3)2:44
Risk Identification (OBJ 2.3)4:50
Risk Analysis (OBJ 2.3)3:26
Risk Evaluation (OBJ 2.3)3:37
Risk Mitigation (OBJ 2.3)3:43
Develop and deploy targeted risk mitigation strategies for AI systems by identifying sources of risk, implementing controls, and continuously monitoring to reduce bias, adversarial threats, and privacy concerns.
Documentation and Reporting (OBJ 2.3)3:49
Monitor and Review (OBJ 2.3)3:48
Case Study: The Apple Card (OBJ 2.3)3:22
Checkpoint: AI Risk Assessments

Trustworthiness of AI Systems (OBJ 3.1 and OBJ 3.2)3:14
Valid and Reliable (OBJ 3.1)3:12
Safe (OBJ 3.1)2:40
Secure and Resilient (OBJ 3.1)3:01
Accountable and Transparent (OBJ 3.1)3:08
Explainable and Interpretable (OBJ 3.1)3:01
Privacy-enabled (OBJ 3.1)2:45
Fair with Harmful Bias Managed (OBJ 3.1)2:50
Balancing Characteristics (OBJ 3.2)4:47
Case Study: Uber (OBJ 3.1 and OBJ 3.2)5:14
Checkpoint: Trustworthiness of AI Systems

Requirements

A foundational understanding of cybersecurity and IT concepts
General familiarity with AI/ML terminology (recommended but not mandatory)
A computer with internet access for labs and study materials
No prior experience in AI development is required

Description

The AKYLADE AI Security Foundation (A/AISF) Certification Course is designed to introduce professionals to the foundational principles of AI security and governance. This course emphasizes critical areas such as AI risk management, trustworthy AI system characteristics, and how to apply the NIST AI Risk Management Framework (RMF). Learners will gain the knowledge necessary to understand and manage the security risks of AI systems across their entire lifecycle.

Domain Discussion

The A/AISF exam content is divided into five domains, each representing a key focus area in AI security. The breakdown of the course content by percentage is as follows:

Artificial Intelligence Concepts (23%)
This domain lays the foundation by explaining AI fundamentals including machine learning, deep learning, neural networks, data preprocessing, AI lifecycle stages, and maturity models. Learners will also explore AI actors, tools, platforms, and real-world use cases across industries.
AI Risk Management (17%)
Focuses on the identification, assessment, and mitigation of AI-related risks. Topics include risk measurement, risk treatment methods, governance structures, and integration of AI risk management within organizational policies and frameworks.
AI Risks and Trustworthiness (22%)
Explores the characteristics of trustworthy AI systems such as fairness, transparency, accountability, and privacy. Learners will examine how to identify and respond to ethical, social, and technical risks, and understand the roles and motivations of AI threat actors.
NIST AI RMF Core (23%)
Covers the four core functions of the NIST AI Risk Management Framework—Govern, Map, Measure, and Manage. Learners will develop the skills to apply these functions to real-world AI risk management scenarios and align them with organizational objectives.
NIST AI RMF Profiles (15%)
Teaches how to develop and tailor AI RMF Profiles to specific organizational needs. Learners will explore profile components, decision-making responsibilities, profile implementation steps, and tools to support AI risk management.

Course Features

This course includes a comprehensive study guide, knowledge check quizzes, and a full-length practice exam. The study guide walks through each domain with clear explanations and examples. Quizzes help reinforce understanding throughout the course, and the practice exam simulates the real certification experience, helping learners gauge their readiness and build exam confidence.

Take the first step toward mastering AI security!

Enroll in the AKYLADE AI Security Foundation (A/AISF) (AIF-001) certification course today and gain the practical knowledge and credentials to support secure, ethical, and compliant AI systems. Prepare with confidence—pass the exam and lead the future of secure AI.

What Other Students Are Saying About Our Courses:

Everything so far is clear cut what to expect from this course. (Alston S., 5 stars)
Great course!! I am really learning & enjoying the course. Thanks! (Suresh S., 5 stars)
The explanations and examples have really helped me to understand concepts I struggled with. (Muzzammil V., 5 stars)

Upon completion of this course, you will earn 13 CEUs towards the renewal of your CompTIA Tech+, A+, Network+, Security+, Linux+, Cloud+, PenTest+, CySA+, or CASP+ certifications.

Who this course is for:

Cybersecurity professionals entering the AI domain
Risk management practitioners in organizations using AI
Compliance and legal officers responsible for AI governance
AI engineers and developers seeking security and trust alignment
IT auditors, analysts, and consultants specializing in emerging technologies
Professionals preparing for the AISF certification exam (AIF-001)

AKYLADE AI Security Foundation (A/AISF) Full Course & Exam

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 33min

Artificial Intelligence Fundamentals8 lectures • 37min

Machine Learning (ML) and Deep Learning (DL) "10 lectures • 44min

AI Lifecycle11 lectures • 48min

AI Actors13 lectures • 35min

AI Strategies and Maturity Levels13 lectures • 51min

AI Risk Management9 lectures • 36min

AI Risk Assessments8 lectures • 29min

AI Governance7 lectures • 36min

Trustworthiness of AI Systems10 lectures • 34min

Requirements

Description

Who this course is for: