Certified AI Cybersecurity Officer (C)AICSO)

Description

Artificial intelligence is transforming the way organizations operate, from customer service and HR to fraud detection and cybersecurity. But with these opportunities come new risks: prompt injection, data poisoning, deepfakes, hallucinations and misuse of generative AI models. The C)AICSO training provides you with a complete playbook to address these threats.

You will not only learn how to protect your organization from AI-related attacks, but also how AI can become a trusted, secure and strategic pillar. The training combines current frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001 and the EU AI Act with practical scenarios and exercises. You will work with the OWASP Top 10 for LLMs and MITRE ATLAS to map threats, and learn to design red teaming and simulation exercises specifically built for decision-makers and managers.

The C)AICSO deliberately focuses on the management perspective. You do not need to be a developer or data scientist to take this training. The emphasis is on policy, governance, risk management and leading AI security programs. Think of setting up AI ethics committees, conducting quarterly risk reviews and writing AI policies that align with your organization.

Learning Objectives

Upon completion of this training, you will be able to:

• Establish and apply a comprehensive framework for AI security risk assessment and mitigation
• Plan and execute red teaming and incident response for LLM and GenAI systems
• Apply NIST AI RMF and ISO/IEC 42001 frameworks to real-world AI workflows
• Securely integrate generative AI into enterprise environments
• Set up governance structures for multi-stakeholder coordination and oversight
• Write and implement AI security policies at board level
• Organize quarterly reviews and audits for AI systems

Prerequisites

The following prior knowledge is recommended:

• Mile2 Certified Security Principles (C)SP) or equivalent knowledge
• Mile2 Certified Information Systems Security Manager (C)ISSM) or equivalent knowledge
• A minimum of 12 months’ experience in Information Systems Management

Target Audience

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

Topics

• What is AI, Really? – AI, ML, Deep Learning and LLMs; reinforcement learning and generative AI; capabilities and limitations of modern AI
• AI Business Applications Across Sectors – AI in customer service, healthcare, HR, fraud detection and cybersecurity; decision augmentation vs automation; agentic AI and autonomous agents
• The Architecture of AI Systems – Data pipelines, model training vs inference, APIs, plugins, cloud vs edge deployments
• The Ethical, Legal & Regulatory Terrain – AI bias, fairness and explainability; EU AI Act, NIST AI RMF, ISO/IEC 42001, OECD; compliance in high-risk sectors
• Threat Landscape for AI Systems – Prompt injection, jailbreaks, adversarial inputs; model inversion, data poisoning; hallucinations, misinformation and impersonation
• Infrastructure and Model Supply Chain Risks – Insecure training environments, model theft, API abuse, third-party risks
• Securing GenAI Systems – OWASP Top 10 for LLMs, MITRE ATLAS, red teaming, hallucination mitigation
• Advanced Threat Scenarios – GPU hijacking, cloud escalation, synthetic identity, deepfake exploits, autonomous offensive AI
• Secure AI-by-Design Principles – Data minimization, federated learning, homomorphic encryption, threat modeling for AI
• AI Risk Management Frameworks – NIST AI RMF deep dive, ISO/IEC 42001 implementation, mapping AI risks to business impact
• Identity, Access, and Control for AI Systems – Authentication for LLMs, RBAC/ABAC for AI APIs, Zero Trust for GenAI
• Cloud-Native AI Security – AWS Bedrock, Azure OpenAI, Google Vertex AI; cloud misconfigurations and exfiltration
• AI Governance in Complex Organizations – Ownership of AI risk (CISO/CIO/CTO), AI ethics committees, documentation and transparency
• Auditing and Testing AI – AI red teaming methodologies, bias detection, fairness audits, third-party evaluation
• AI-Centric Incident Response – Detection and containment of AI exploits, playbooks for prompt injection and GenAI abuse
• Futureproofing and AI Resilience – Adaptive threats, simulating rogue agents, post-AI-compromise resilience
• Strategic Exercises and Scenarios – Attack simulations, controls mapping, designing security playbooks
• What Managers Must Ask Quarterly – Governance checklists, architecture review, prompt abuse controls
• AI Policy Building Blocks – Writing safe AI policy from scratch, mandatory training, defining high-risk vs low-risk systems
• Your AI Security Program End to End – Maturity models, role of the CISO and CAIO, roadmap for the next 18-24 months