C)AICSO™ – Certified AI Cybersecurity Officer

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 ATLAS and 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.

Course Modules

1. Module 01: What is AI, Really?
   1. AI, ML, DL, and LLMs Explained
   2. Reinforcement Learning and Generative AI
   3. AI System Examples: ChatGPT, Sora, Claude, Gemini, DALL·E
   4. The Capabilities and Limitations of Modern AI
2. Module 02: AI Business Applications Across Sectors
   1. AI in Customer Service, Healthcare, HR, Fraud, Cyber
   2. AI for Decision Augmentation vs Automation
   3. Industry-Specific AI Use Cases (Critical Infrastructure, Finance, etc.)
   4. Emerging Trends: Agenic AI & Autonomous Agents
3. Module 03: The Architecture of AI Systems
   1. Data Pipelines: Ingestion, Cleaning, Feature Engineering
   2. Models and Training vs Inference Workflows
   3. APIs, Plugins, Cloud vs Edge Deployments
   4. Cost, Performance & Scalability Trade-offs
4. Module 04: The Ethical, Legal & Regulatory Terrain
   1. AI Bias, Fairness, and Explainability
   2. EU AI Act, NIST AI RMF, ISO/IEC 42001, OECD
   3. Compliance in High-Risk Sectors
   4. Ethics of Autonomous Agents & Generative Models

PART II – AI-Specific Threats And Risks

5. Module 05: Threat Landscape for AI Systems
   1. Prompt Injection, Jailbreaks, Adversarial Inputs
   2. Model Inversion, Data Poisoning
   3. Hallucinations, Misinformation, and Impersonation
   4. Case Examples from 2023–2025
6. Module 06: Infrastructure and Model Supply Chain Risks
   1. Insecure Training Environments & Data Lakes
   2. Model Theft, Tampering, & Inference Abuse
   3. API Abuse and Plugin Vulnerabilities
   4. OSINT, Third-Party Risks, and GenAI Abuse
7. Module 07: Securing GenAI Systems
   1. OWASP Top 10 for LLMs
   2. MITRE ATLAS Threats to AI
   3. Red Teaming and Adversarial Testing
   4. Hallucination Mitigation Techniques
8. Module 08: Advanced Threat Scenarios
   1. GPU Hijacking, Cloud Escalation
   2. Synthetic Identity and Deepfake Exploits
   3. Autonomous Offensive AI (Agenic AI Threats)
   4. Coordinated AI-led Attacks on CI (Critical Infrastructure)

PART III – Defense & Risk Management

9. Module 09: Secure AI-by-Design Principles
   1. Data Minimization and Privacy-Enhanced Learning
   2. TEE, Federated Learning, Homomorphic Encryption
   3. Threat Modeling for AI Workflows
10. Module 10: AI Risk Management Frameworks
    1. NIST AI RMF Deep Dive
    2. Implementing ISO/IEC 42001 in the Enterprise
    3. Mapping AI Risks to Business Impact
11. Module 11: Identity, Access, and Control for AI Systems
    1. Authentication for LLMs
    2. RBAC/ABAC for AI APIs
    3. Zero Trust Architectures for GenAI Systems
12. Module 12: Cloud-Native AI Security
    1. AWS Bedrock, Azure OpenAI, Google Vertex AI
    2. Cloud Misconfigurations and Exfiltration Paths
    3. Logging, Threat Detection, and Response

PART IV – Governance, Incident Response & Resilience

13. Module 13: AI Governance in Complex Organizations
    1. Who Owns AI Risk? (CISO/CIO/CTO Debate)
    2. AI Ethics Committees, Governance Boards
    3. Documentation and Transparency Best Practices
14. Module 14: Auditing and Testing AI
    1. AI Red Teaming Methodologies
    2. Bias Detection and Fairness Audits
    3. Third-Party Evaluation Frameworks
15. Module 15: AI-Centric Incident Response
    1. Detection and Containment of AI Exploits
    2. Toxic Output and Privacy Leaks
    3. Playbooks for Prompt Injection and GenAI Abuse
16. Module 16: Futureproofing and AI Resilience
    1. Adaptive Threats: Autonomous and Multi-Modal AI
    2. R&D: Simulating Rogue Agents
    3. Building Post-AI-Compromise Resilience

PART V – Practicals, Strategy & Action

17. Module 17: Strategic Exercises and Scenarios
    1. Attack Simulation: Policy-Only Scenario Labs
    2. Controls Mapping for Different AI Models
    3. Designing Security Playbooks
18. Module 18: What Managers Must Ask Quarterly
    1. Governance Checklists
    2. Architecture Review Questions
    3. Prompt Abuse Controls
    4. Transparency & Data Governance Updates
19. Module 19: AI Policy Building Blocks
    1. Writing a Safe AI Policy from Scratch
    2. Mandatory Training and Awareness
    3. Defining “High-Risk” and “Low-Risk” Systems
    4. Board-Level AI Policy Templates
20. Module 20: Your AI Security Program – End to End
    1. Maturity Models for AI Security
    2. Role of the CISO, ISO, and Emerging Roles (CAIOs)
    3. Roadmap for the Next 18–24 Months
    4. Closing Thoughts & Final Reflection