Certified AI Security Management and Leadership (CAISML)

Certified AI Security Management and Leadership (CAISML) Certification Program by Tonex

Duration: 2 Days | Format: Live Virtual / In-Person / Hybrid
Level: Strategic – Executive, Management, and Governance Focused
Credential: Certification + Exam + Digital Badge

The CAISML certification equips business leaders, security executives, and governance professionals with the knowledge, frameworks, and tools to develop and lead AI security programs. It bridges technical and non-technical domains by focusing on risk management, compliance, governance, policy, incident response, and strategic planning across AI/ML systems, including LLMs, RAG systems, and AI-enabled platforms.

Learning Objectives

By completing CAISML, participants will be able to:

• Establish and govern enterprise-wide AI security strategies and risk frameworks.
• Lead cross-functional teams to manage AI-specific threats and vulnerabilities.
• Implement and align with NIST AI RMF, ISO 42001, and relevant cybersecurity frameworks (e.g., NIST CSF, ISO 27001).
• Oversee incident response processes involving LLMs, generative AI, and adversarial ML threats.
• Assess supply chain, privacy, model misuse, and trustworthiness risks.
• Design policies for AI security, model access, auditability, and explainability.
• Develop training and culture programs for AI security awareness across teams.

Target Audience:

• CISOs, CDOs, CIOs
• Risk & compliance leaders
• Security program managers
• Privacy officers and governance professionals
• Product and AI operations leads
• AI program directors in government or regulated industries
• Business unit leaders accountable for AI adoption

Program Agenda & Modules:

Day 1 – Governance, Risk, and Strategy

Module 1: Foundations of AI Security Leadership

• Security risks in AI/ML systems vs traditional IT
• Business implications of model compromise
• Overview of threat actors, attack surfaces, and AI use cases
• OWASP LLM Top 10, MITRE ATLAS, and AI red team outputs

Module 2: AI Risk Management Frameworks

• NIST AI RMF Core functions: Map, Measure, Manage, Govern
• ISO/IEC 42001: AI Management System (AIMS)
• Integrating AI with existing risk programs (NIST CSF, ISO 27001, CIS Controls)

Module 3: Policy, Ethics, and Governance

• AI acceptable use, explainability, transparency
• Model governance: approval, audit, accountability
• AI board policies, procurement due diligence, and third-party risks
• Responsible AI vs Secure AI vs Compliant AI

Day 2 – Security Operations, Response, and Oversight

Module 4: AI Security Program Architecture

• AI system inventory and risk classification
• AI-SOC integration: logging, monitoring, anomaly detection
• Security for LLMs, vector stores, RAG systems, and model APIs
• Role of zero trust, encryption, and segmentation in AI environments

Module 5: AI Incident Response and Crisis Leadership

• Developing an AI-specific IR plan
• AI failure scenarios: prompt injection, data leakage, model misuse
• Coordination between cyber, legal, PR, and compliance teams
• Tabletop exercises and crisis simulation walkthrough

Module 6: Metrics, Training & Culture

• AI security maturity models
• Metrics and KPIs for AI security posture
• Building an AI security culture
• Employee training, red team drills, and executive briefings

Capstone, Compliance & Certification Readiness:

Compliance and Regulatory Readiness

• Regulatory landscape: EU AI Act, White House EO, ISO/IEC 23894
• Data privacy and model compliance (GDPR, HIPAA, CCPA)
• Explainability, audit trails, and certification readiness
• Secure AI supply chain and model lineage

Capstone Case Study & Roadmap

• Guided scenario: Secure GenAI deployment at a large enterprise
• Identify risks, create a governance structure, and develop a risk response plan
• Final presentation: AI security roadmap for leadership teams

Certification Exam Domains: