Certified AI Cybersecurity Officer On-Demand

If you’re looking for the leading course in AI and cybersecurity governance, the C)AICSO™ – Certified AI Cybersecurity Officer is the definitive choice. This program empowers professionals to take personal and organizational responsibility for both the implementation and protection of AI systems across industries.

Unlike traditional cybersecurity programs that focus solely on defense, the C)AICSO™ prepares leaders to build resilience with AI—turning artificial intelligence from a potential threat into a trusted, strategic enabler. Participants learn to design and oversee secure, ethical, and auditable AI ecosystems while leading governance initiatives that align innovation with accountability.

The course introduces Mile2’s Progressive AI Risk Management Framework, which equips decision-makers with tools and methodologies to anticipate and mitigate emerging AI risks. Core areas include:

• Policy-First Security Design— Treating GenAI as a potential insider threat vector.
• Adversarial Use Case Mapping— Drawing from MITRE ATLASand the OWASP LLM Top 10 to identify exploitation patterns.
• Quarterly Risk Reviews— Structured leadership questions to evaluate AI controls and performance.
• Red Teaming & Simulation Exercises— Strategic exercises tailored for managers, not coders.

By the end of this course, participants will understand how to govern, defend, and audit AI responsibly, enabling innovation while maintaining compliance, resilience, and public trust

This course grants 40 CEUs upon completion.

What’s Included?

• 1-year Online Course Access
• Videos
• Workbook
• Exam Prep Guide
• Practice Quiz
• Certification Exam (2 attempts)

Audience

This course is designed for:

• IS Security Officers
• IS Managers
• Risk Managers
• Auditors
• Info Systems Owners
• IS Control Assessors
• System Managers
• AI Governance Officers
• Security Architects

Upon Completion

Upon completion, Certified AI Cybersecurity Officer students will be able to establish industry-accepted cybersecurity and Information Systems management standards with current best practices. In addition, the following competencies will be achieved:

• A comprehensive framework for assessing and mitigating AI security risks
• How to red team and incident plan for LLM and GenAI systems
• How to apply NIST and ISO frameworks to real AI workflows
• How to securely integrate GenAI into enterprise environments
• Governance blueprints for multi-stakeholder coordination and oversight

Exam Information

The Certified AI Cybersecurity Officer exam is taken online through Mile2’s Learning Management System and is accessible on you Mile2.com account.  The exam will take approximately 2 hours and consist of 100 multiple choice questions.

A minimum grade of 70% is required for certification.

All Mile2 certifications will be awarded a 3-year expiration date.

There are two requirements to maintain Mile2 certification:

• Pass the most current version of the exam for your respective existing certification
• Earn and submit 20 CEUs per year in your Mile2 account.

Course Outline

Module 01: What is AI, Really?

01.1 AI, ML, DL, and LLMs Explained

01.2 Reinforcement Learning and Generative AI

01.3 AI System Examples: ChatGPT, Sora, Claude, Gemini, DALL·E

01.4 The Capabilities and Limitations of Modern AI

Module 02: AI Business Applications Across Sectors

02.1 AI in Customer Service, Healthcare, HR, Fraud, Cyber

02.2 AI for Decision Augmentation vs Automation

02.3 Industry-Specific AI Use Cases (Critical Infrastructure, Finance, etc.)

02.4 Emerging Trends: Agenic AI & Autonomous Agents

Module 03: The Architecture of AI Systems

03.1 Data Pipelines: Ingestion, Cleaning, Feature Engineering

03.2 Models and Training vs Inference Workflows

03.3 APIs, Plugins, Cloud vs Edge Deployments

03.4 Cost, Performance & Scalability Trade-offs

Module 04: The Ethical, Legal & Regulatory Terrain

04.1 AI Bias, Fairness, and Explainability

04.2 EU AI Act, NIST AI RMF, ISO/IEC 42001, OECD

04.3 Compliance in High-Risk Sectors

04.4 Ethics of Autonomous Agents & Generative Models

PART II – AI-SPECIFIC THREATS AND RISKS

Module 05: Threat Landscape for AI Systems

05.1 Prompt Injection, Jailbreaks, Adversarial Inputs

05.2 Model Inversion, Data Poisoning

05.3 Hallucinations, Misinformation, and Impersonation

05.4 Case Examples from 2023–2025

Module 06: Infrastructure and Model Supply Chain Risks

06.1 Insecure Training Environments & Data Lakes

06.2 Model Theft, Tampering, & Inference Abuse

06.3 API Abuse and Plugin Vulnerabilities

06.4 OSINT, Third-Party Risks, and GenAI Abuse

Module 07: Securing GenAI Systems

07.1 OWASP Top 10 for LLMs

07.2 MITRE ATLAS Threats to AI

07.3 Red Teaming and Adversarial Testing

07.4 Hallucination Mitigation Techniques

Module 08: Advanced Threat Scenarios

08.1 GPU Hijacking, Cloud Escalation

08.2 Synthetic Identity and Deepfake Exploits

08.3 Autonomous Offensive AI (Agenic AI Threats)

08.4 Coordinated AI-led Attacks on CI (Critical Infrastructure)

PART III – DEFENSE & RISK MANAGEMENT

Module 09: Secure AI-by-Design Principles

09.1 Data Minimization and Privacy-Enhanced Learning

09.2 TEE, Federated Learning, Homomorphic Encryption

09.3 Threat Modeling for AI Workflows

Module 10: AI Risk Management Frameworks

10.1 NIST AI RMF Deep Dive

10.2 Implementing ISO/IEC 42001 in the Enterprise

10.3 Mapping AI Risks to Business Impact

Module 11: Identity, Access, and Control for AI Systems

11.1 Authentication for LLMs

11.2 RBAC/ABAC for AI APIs

11.3 Zero Trust Architectures for GenAI Systems

Module 12: Cloud-Native AI Security

12.1 AWS Bedrock, Azure OpenAI, Google Vertex AI

12.2 Cloud Misconfigurations and Exfiltration Paths

12.3 Logging, Threat Detection, and Response

PART IV – GOVERNANCE, INCIDENT RESPONSE & RESILIENCE

Module 13: AI Governance in Complex Organizations

13.1 Who Owns AI Risk? (CISO/CIO/CTO Debate)

13.2 AI Ethics Committees, Governance Boards

13.3 Documentation and Transparency Best Practices

Module 14: Auditing and Testing AI

14.1 AI Red Teaming Methodologies

14.2 Bias Detection and Fairness Audits

14.3 Third-Party Evaluation Frameworks

Module 15: AI-Centric Incident Response

15.1 Detection and Containment of AI Exploits

15.2 Toxic Output and Privacy Leaks

15.3 Playbooks for Prompt Injection and GenAI Abuse

Module 16: Futureproofing and AI Resilience

16.1 Adaptive Threats: Autonomous and Multi-Modal AI

16.2 R&D: Simulating Rogue Agents

16.3 Building Post-AI-Compromise Resilience

PART V – PRACTICALS, STRATEGY & ACTION

Module 17: Strategic Exercises and Scenarios

17.1 Attack Simulation: Policy-Only Scenario Labs

17.2 Controls Mapping for Different AI Models

17.3 Designing Security Playbooks

Module 18: What Managers Must Ask Quarterly

18.1 Governance Checklists

18.2 Architecture Review Questions

18.3 Prompt Abuse Controls

18.4 Transparency & Data Governance Updates

Module 19: AI Policy Building Blocks

19.1 Writing a Safe AI Policy from Scratch

19.2 Mandatory Training and Awareness

19.3 Defining “High-Risk” and “Low-Risk” Systems

19.4 Board-Level AI Policy Templates

Module 20: Your AI Security Program – End to End

20.1 Maturity Models for AI Security

20.2 Role of the CISO, ISO, and Emerging Roles (CAIOs)

20.3 Roadmap for the Next 18–24 Months

20.4 Closing Thoughts & Final Reflection

APPENDICES

• Glossary of AI + Cyber Terms
• AI Attack & Threat Matrix (Custom)
• Quarterly Review Template for Managers
• Policy Draft Template
• Dataset Checklist for Secure Training