التقنية : الميتافيرس

الإلمام المهني بالذكاء الاصطناعي للأمن السيبراني ™

AI Literacy for Cyber Defense (AILC-CD)™ Workshop | Pathway Mandatory

(Mandatory Foundational Track for AICyberBridge™ | Prepared for CISAIP™ & iCISO™)

AI Literacy for Cyber Defense™ (AILC-CD™) builds foundational AI literacy for cybersecurity professionals and leaders, ensuring informed judgment, risk awareness, and accountability before entering advanced security and executive AI pathways.

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About the Workshop

AILC-CD™ is a mandatory foundational AI literacy workshop for the AICyberBridge™ pathway, designed to prepare participants for both Certified Information Security & AI Professional (CISAIP)™ and Intelligent Chief Information Security Officer (iCISO)™ programs.

The workshop introduces how artificial intelligence fundamentally changes cybersecurity threats, defenses, risk management, and leadership accountability. It aligns AI literacy with CISSP domains (1–8) at an introductory level, while also establishing executive-level awareness required for AI-driven security governance.
This course focuses on understanding, judgment, and readiness — not tools, controls, or implementation.

The course is designed for those interested in the design, implementation and responsible use of artificial intelligence systems and products within their organization.

– Cybersecurity professionals preparing for AI-enabled security roles
– Security managers and team leads entering AI-augmented environments
– CISAIP™ candidates requiring AI-security literacy
– iCISO™ candidates preparing for AI-driven executive responsibility
– Risk, compliance, and governance professionals in security functions
– Consultants and advisors working in cyber and AI risk

– Information Security Professionals
– Security Architects & Analysts (Non-Technical Literacy Layer)
– SOC Managers & Security Leads
– Risk & Compliance Officers
– Future CISOs / Deputy CISOs
– Security Consultants & Advisors

– How AI fundamentally reshapes cybersecurity risk and assumptions
– How AI behaves in security contexts — and where it fails
– How attackers and defenders use AI at a conceptual level
– How AI affects decision-making across security domains
– Why AI introduces new governance, audit, and accountability challenges
– How to prepare for advanced AI security and leadership tracks

– Enter CISAIP™ with AI-security literacy instead of tool confusion
– Enter iCISO™ with executive-level AI risk awareness
– Avoid over-trusting AI-driven security outputs
– Reduce strategic, regulatory, and reputational exposure
– Build a shared AI-security language across teams and leaders
– Strengthen judgment before depth, tools, or authority

– AI-Security Literacy: Understanding how AI changes cyber defense
– Risk Interpretation Skills: Evaluating AI-driven security risks
– Decision Oversight Awareness: Knowing when AI must not decide
– Governance Readiness: Recognizing accountability in AI security
– Leadership Awareness: Preparing for AI-informed security leadership

– AI Fundamentals for Cybersecurity – Understanding AI behavior in security contexts
– AI & Decision Impact – How AI influences detection, response, and prioritization
– AI as an Attack Surface – Recognizing AI systems as security targets
– AI-Enabled Threats – Awareness of adversarial AI use
– AI & Security Operations – Literacy-level SOC and operational awareness
– AI Governance & Leadership – Accountability, oversight, and executive risk

– AI-Aware Cyber Risk Competency: Understanding AI-driven risk dynamics
– Security Judgment Competency: Interpreting AI outputs responsibly
– Governance Awareness Competency: Recognizing oversight requirements
– Leadership Readiness Competency: Preparing for executive AI security roles
– Pathway Readiness Competency: Entering CISAIP™ and iCISO™ prepared

– CISSP Domains (1–8) — introductory, literacy-level alignment
– NIST AI Risk Management Framework — conceptual awareness
– ISO/IEC AI governance and security principles — awareness level
– Global Responsible AI and AI security best practices

– Optional & Recommended: Pass the Certificate : AI Literacy Core – AILC™
– General cybersecurity knowledge
– Familiarity with basic security concepts and environments
– No AI, coding, or machine-learning background required

– Course Students Study Notes – Online version Only
– AI-security literacy reference guides
– AI risk and failure-mode awareness checklists
– AI governance & accountability quick references
– Certificate of completion upon passing assessment

Service Guide

1. Implementation Manual

Step-by-step instructions on how to implement the ISO/IEC 42001 standard.
Detailed processes, templates, and best practices.

1. Printed Materials

– Physical copies of guides, templates, and notes.

2. Digital Resources

– Downloadable PDFs and editable documents.
– Online access to tools and software platforms.

3. Workshops and Webinars

– Interactive workshops for hands-on practice.
– Live and recorded webinars for remote learning.

4. Certification Exam Preparation

– Practice exams and sample questions.
– Study guides focused on certification requirements.

Practices

Practices for the ISO/IEC 42001 Course
English

1. Interactive Workshops:

Hands-on sessions where participants work on real-world scenarios.
Group activities to encourage collaboration and knowledge sharing.
Practical exercises to apply concepts learned.

2. Case Study Analysis:

In-depth examination of successful ISO/IEC 42001 implementations.
Discussion of challenges faced and solutions applied.
Lessons learned and best practices.

3. Risk Assessment Exercises:

Identifying and evaluating AI-related risks.
Developing risk mitigation strategies.
Using provided templates and tools for risk management.

4. Compliance Audits:

Conducting mock internal audits.
Reviewing compliance checklists and documentation.
Identifying areas for improvement and developing action plans.

5. Role-Playing Scenarios:

Simulating real-world situations to practice decision-making.
Role-playing different stakeholders to understand various perspectives.
Problem-solving tasks to reinforce learning.

6. Policy and Procedure Development:

Creating AI management policies using provided templates.
Developing standard operating procedures for AI tasks.
Customizing documents to fit organizational needs.

7. Ethical and Legal Considerations Workshops:

Discussing ethical implications and legal requirements of AI.
Analyzing case studies on ethical dilemmas.
Developing strategies for ethical AI management.

8. Software Tools Training:

Hands-on training with AI management software or platforms.
Demonstrating how to use tools effectively.
Practice sessions to build proficiency.

9. Q&A Sessions and One-on-One Consultations:

Scheduled sessions for participants to ask questions.
Opportunities for individual consultations with instructors.
Addressing specific concerns and providing tailored advice.

10. Certification Exam Preparation:

Practice exams and sample questions.
Study guides focused on certification requirements.
Tips and strategies for successful exam performance.

Arabic

1. ورش العمل التفاعلية:

جلسات عملية حيث يعمل المشاركون على سيناريوهات واقعية.
أنشطة جماعية لتعزيز التعاون وتبادل المعرفة.
تمارين عملية لتطبيق المفاهيم المكتسبة.

2. تحليل دراسات الحالة:

فحص متعمق لتنفيذات ناجحة لمعيار ISO/IEC 42001.
مناقشة التحديات التي واجهتها والحلول المطبقة.
الدروس المستفادة وأفضل الممارسات.

3. تمارين تقييم المخاطر:

تحديد وتقييم المخاطر المتعلقة بالذكاء الاصطناعي.
تطوير استراتيجيات التخفيف من المخاطر.
استخدام القوالب والأدوات المقدمة لإدارة المخاطر.

4. تدقيق الامتثال:

إجراء تدقيقات داخلية وهمية.
مراجعة قوائم التحقق ووثائق الامتثال.
تحديد مناطق التحسين وتطوير خطط العمل.

5. سيناريوهات لعب الأدوار:

محاكاة مواقف واقعية لممارسة اتخاذ القرارات.
لعب أدوار أصحاب المصلحة المختلفين لفهم وجهات النظر المختلفة.
مهام حل المشكلات لتعزيز التعلم.

6. تطوير السياسات والإجراءات:

إنشاء سياسات إدارة الذكاء الاصطناعي باستخدام القوالب المقدمة.
تطوير إجراءات التشغيل القياسية لمهام الذكاء الاصطناعي.
تخصيص الوثائق لتناسب احتياجات المنظمة.

7. ورش العمل حول الاعتبارات الأخلاقية والقانونية:

مناقشة التداعيات الأخلاقية والمتطلبات القانونية للذكاء الاصطناعي.
تحليل دراسات الحالة حول المعضلات الأخلاقية.
تطوير استراتيجيات لإدارة الذكاء الاصطناعي بشكل أخلاقي.

8. تدريب على أدوات البرمجيات:

تدريب عملي على إدارة البرمجيات أو المنصات المستخدمة في الذكاء الاصطناعي.
عرض كيفية استخدام الأدوات بشكل فعال.
جلسات تدريب لبناء الكفاءة.

9. جلسات الأسئلة والأجوبة والمشاورات الفردية:

جلسات مجدولة لطرح الأسئلة من قبل المشاركين.
فرص للتشاور الفردي مع المدربين.
معالجة القضايا المحددة وتقديم نصائح مخصصة.

10. التحضير لامتحان الشهادة:

اختبارات تجريبية وأسئلة نموذجية.
أدلة دراسية تركز على متطلبات الشهادة.
نصائح واستراتيجيات لتحقيق أداء ناجح في الامتحان.

By incorporating these practices into the course, participants will gain practical experience and confidence in implementing the ISO/IEC 42001 standard, preparing them effectively for real-world application and certification.

- Overview

– Course Introduction
– Certificate Introduction & Exam Details.

Module 1 – AI Literacy Foundations for Cyber Defense

Purpose
To establish a shared AI–security mental model and explain why artificial intelligence fundamentally changes cybersecurity risk, assumptions, and responsibility.

Topics & Sub-Topics

Why AI changes cybersecurity (not just improves it)
Traditional security automation vs AI-driven security
AI as a decision-influencing system in cyber defense
Human accountability in AI-enabled security decisions
Preparing mindset for CISSP-aligned depth and iCISO leadership

Module 2 – Understanding AI in Security Contexts (Non-Technical)

Purpose
To build a clear, non-technical understanding of how AI behaves in security environments and why its outputs must be interpreted, not blindly trusted.

Topics & Sub-Topics

How AI “sees” security data (patterns vs intent)
Machine learning vs rule-based security controls
Predictive AI vs generative AI in cybersecurity
AI confidence vs AI correctness
Explainability as a security requirement

Module 3 – AI & CISSP Domain 1: Security & Risk Management (Introductory)

Purpose
To introduce how AI reshapes security risk management, governance, and accountability, forming the foundation for CISAIP™ and iCISO™ risk ownership.

Topics & Sub-Topics

AI-driven cyber risk vs traditional cyber risk
Accountability when AI influences security decisions
Ethical implications of AI-assisted security
Cross-domain AI risk (security, privacy, compliance)
Executive vs operational risk ownership

Module 4 – AI & CISSP Domain 2: Asset Security (Introductory)

Purpose
To introduce AI systems, models, and data as critical security assets and prepare learners for asset-centric AI risk discussions.

Topics & Sub-Topics

AI models, data, and pipelines as security assets
Data as the primary attack surface for AI systems
Awareness of data poisoning and data misuse
Asset classification challenges in AI environments
Ownership and stewardship of AI-related assets

Module 5 – AI & CISSP Domain 3: Security Architecture & Engineering (Introductory)

Purpose
To build awareness of how AI disrupts traditional security architecture assumptions without entering engineering or design depth.

Topics & Sub-Topics

Where AI fits in modern security architectures
Non-deterministic behavior and architectural risk
Broken trust assumptions in AI-enabled systems
Explainability and traceability challenges
Why security architecture reviews must change with AI

Module 6 – AI & CISSP Domain 4: Communication & Network Security (Introductory)

Purpose
To explain how AI impacts network security, communication trust, and deception, with implications for both operations and leadership.

Topics & Sub-Topics

AI in network monitoring and anomaly detection (conceptual)
AI-assisted reconnaissance and targeting
Deepfakes and synthetic media risks
Communication trust erosion in AI-driven environments
Human judgment in interpreting AI-driven signals

Module 7 – AI & CISSP Domain 5: Identity & Access Management (Introductory)

Purpose
To introduce AI-driven identity risks and prepare learners for deeper IAM discussions in advanced tracks.

Topics & Sub-Topics

AI-assisted and behavioral authentication concepts
Identity abuse enabled by generative AI
Deepfake-driven impersonation risks
Weakening of traditional IAM trust models
Accountability in AI-assisted access decisions

Module 8 – AI & CISSP Domain 6: Security Assessment & Testing (Introductory)

Purpose
To reshape expectations around security testing, assurance, and audits in AI-enabled systems.

Topics & Sub-Topics

Why traditional testing struggles with AI systems
Assessing AI behavior vs system vulnerabilities
Black-box AI challenges in audits
Evidence, logging, and traceability issues
Preparing for AI-aware security assessments

Module 9 – AI & CISSP Domain 7: Security Operations (Introductory)

Purpose
To prepare learners for AI-augmented security operations without operational playbooks or tools.

Topics & Sub-Topics

Where AI fits in SOC workflows (literacy level)
Decision support vs decision delegation in SOCs
Automation bias and over-reliance risks
False positives vs false confidence
Escalation responsibility when AI fails

Module 10 – AI & CISSP Domain 8: Software Development Security (Introductory)

Purpose
To introduce AI-related SDLC risks and prepare learners for secure development discussions in CISAIP™.

Topics & Sub-Topics

AI-generated code risks (awareness level)
Training data leakage and IP exposure
AI’s impact on secure development assumptions
Responsibility boundaries across Dev, Sec, and AI teams
Preparing for AI-aware DevSecOps thinking

Module 11 – AI Security Leadership & iCISO Readiness (Introductory)

Purpose
To prepare learners for executive-level AI security leadership, governance, and board accountability addressed in iCISO™.

Topics & Sub-Topics

Why AI breaks traditional CISO mental models
AI as a board-level and executive risk
Delegation vs accountability in AI security
Communicating AI risk to non-technical boards
Regulatory, legal, and reputational exposure
Readiness checklist for iCISO™ depth

Module 12 – Final Assessment & Literacy Validation

Purpose
To validate AI-security literacy and judgment, not technical skills.

Topics & Sub-Topics

Scenario-based AI + cybersecurity decision cases
Identifying AI-driven security risks
Governance and escalation judgment questions
Conceptual MCQs mapped to CISSP domains and iCISO awareness
Certification readiness confirmation for AICyberBridge™

– Structured workshop slides and learning materials
– AI-security literacy reference guides
– AI risk and failure-mode awareness checklists
– AI governance & accountability quick references
– Certificate of completion upon passing assessment

Certificate/accreditation and examination

1. The importance of certification:

Journalists and content creators who work in various media outlets and want to keep up with technological developments.

2. The Exam:

Journalists and content creators who work in various media outlets and want to keep up with technological developments.

3. Certificate and accreditation:

Read the details here

Attendance Testimonials

1. مقدمة عن التقنيات الحديثة في الإعلام:

تعريف بالذكاء الاصطناعي والميتافيرس.
أهمية تطبيقاتهم في مجالات الصحافة والإعلام والثقافة.

Q&A

1. مقدمة عن التقنيات الحديثة في الإعلام:

تعريف بالذكاء الاصطناعي والميتافيرس.
أهمية تطبيقاتهم في مجالات الصحافة والإعلام والثقافة.

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More Details
GDPR T&C

First Name

Last Name

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Upload Profile Image

Choose File

Verse-related Technologies

Interested to be Part of

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LinkedIn Profile

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Feedback & Advices

Ideas & Recommendations

Suggest for us good Profiles like yours to join us:

Other Inputs & Details

I consent to having this website store my submitted information so they can respond to my inquiry

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LEVEL

Executive

Duration

3 Days

Modules

General Informations

– Delivery Languages : Arabic or English.
– Material Languages : Arabic or English.
– Delivery Format: Inperson , or Self-teaching video lectures.
– Access from any device and from anywhere.
– Internationally recognized certificate of attendance.
– CPE/CPD credits.

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Attend live, interactive sessions either online or Recorded inperson for a collaborative learning experience. Sessions led by industry experts, offering opportunities for networking, hands-on exercises, and real-time discussions to deepen their understanding of AI governance and oversight.

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Attendees who complete the program exam successfully will earn the prestigious certification. This globally recognized credential validates their expertise in AI governance, strategic oversight, and implementation. Achieving this certification demonstrates a professional's ability to lead AI initiatives ethically and effectively, and alignment with organizational objectives.

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Receive comprehensive study materials and Body of Knowledge (BoK)— a curated repository of essential concepts, frameworks, and tools in AI governance, strategy, and deployment. Resources are designed to reinforce learning and provide ongoing support throughout their professional journey.

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ISO 42001 Services

To implement an AIMS, you will need a solid understanding of ISO/IEC 42001's requirements. We have training to provide the required knowledge. Once the system is in place, we can offer a gap assessment. After the successful completion of an audit, we will issue your certificate, confirming the effective implementation of the standard's requirements. An audit against ISO/IEC 42001 from us will help your organization to stand out from the crowd by supporting you to develop and improve performance.

AI Literacy Preparedness Tooling™

The DASO Toolkit equips Professionals with practical resources to oversee AI initiatives. It includes templates for governance policies, risk assessment frameworks, compliance checklists, and tools for managing AI projects and teams. This toolkit ensures Professionals are prepared to apply their skills immediately within their organizations.

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AI Literacy Knowledge Resources

Knowledge Guides are a series of detailed, expert-authored documents that delve into specialized topics such as AI risk management, ethical AI practices, and regulatory compliance. These guides serve as valuable references, helping professionals stay informed and make informed decisions as they oversee AI initiatives.