Certified Chief AI Security Officer (C-CISO)

Certified Chief AI Security Officer (C-CISO™)

Offered by Tonex and NLL.ai

The Certified Chief AI Security Officer (C-CISO™) program is an executive-level certification that prepares professionals to lead AI security strategy, governance, policy development, and risk mitigation in organizations deploying advanced artificial intelligence systems.

As enterprises adopt LLMs, autonomous agents, and AI-as-a-Service at scale, the C-CISO plays a pivotal role in protecting these ecosystems against threats ranging from model theft and adversarial AI to compliance failures and Zero Trust enforcement breakdowns.

This program equips leaders with cross-functional knowledge in AI architectures, cybersecurity frameworks, governance models, and emerging regulatory landscapes to drive secure, resilient, and trustworthy AI operations.

Learning Objectives

Participants completing this program will be able to:

• Design and lead enterprise-wide AI security and governance strategies
• Evaluate and mitigate AI-specific risks using frameworks like MITRE ATLAS, OWASP LLM, and NIST AI RMF
• Oversee AI data privacy, ethics, compliance, and secure model operations
• Build cross-functional security teams aligned with AI innovation goals
• Ensure zero-trust and federated identity models for AI/ML systems
• Lead incident response and business continuity planning in AI environments

Target Audience

• Chief Information Security Officers (CISOs) expanding into AI
• AI/ML Security Leaders and Architects
• CTOs and Heads of Engineering/Innovation
• Cybersecurity Consultants working with AI clients
• Regulatory, Risk, and Compliance Officers in AI-intensive sectors

Prerequisites

• 5+ years of experience in cybersecurity, enterprise IT, or AI architecture
• Foundational knowledge of AI/ML systems and cloud environments
• Familiarity with cybersecurity frameworks (e.g., NIST CSF, ISO 27001, Zero Trust)

Program Highlights:

Program Modules

Part 1 – AI Security Strategy, Architecture & Threat Landscape

• Module 1: AI Security Architecture Fundamentals
  • Core components of AI/ML systems: models, pipelines, APIs, data layers
  • Differences between traditional cybersecurity and AI security
  • The AI attack surface: LLMs, agents, autonomous decision-making systems
• Module 2: Threat Modeling with MITRE ATLAS
  • Overview of MITRE ATLAS tactics and techniques
  • Mapping threats to AI lifecycle stages: training, inference, deployment
  • Use cases: AI fraud systems, recommendation engines, autonomous systems
• Module 3: OWASP LLM Top 10 (2024–2025)
  • Deep dive into the OWASP LLM risks: prompt injection, model theft, plugin abuse
  • Mapping controls to each risk (Zero Trust, plugin sandboxing, PII scanning)
  • Interactive exercise: Apply OWASP LLM to a real-world financial chatbot

Part 2 – AI Governance, Ethics, Risk & Compliance

• Module 4: AI Risk Management Frameworks
  • NIST AI RMF: Govern, Map, Measure, Manage
  • ISO/IEC 23894 and AI-specific ISO standards
  • The EU AI Act and global AI regulations overview
• Module 5: Enterprise AI Governance Design
  • Organizational structures for AI oversight
  • AI committee roles, internal policies, compliance registers
  • Case study: AI ethics failure and mitigation plan
• Module 6: Data Privacy and Ethical AI Practices
  • Differential privacy, federated learning, synthetic datasets
  • Governance for training data: consent, traceability, redaction
  • Legal requirements: GDPR, HIPAA, CCPA for AI systems
• Workshop: Design an AI Governance Framework
  • Hands-on session building a policy, audit trail, and risk log
  • Share and review draft frameworks with peers

Part 3 – Secure Deployment, Zero Trust AI, and MLOps

• Module 7: Zero Trust AI Identity & Access Management
  • SPIFFE/SPIRE for identity in AI infrastructure
  • Policy-based access control for models, datasets, APIs
  • Trust boundaries and workload segmentation
• Module 8: Secure MLOps and DevSecOps Integration
  • Security in ML pipelines (model packaging, versioning, rollback)
  • Protecting model CI/CD, supply chain security
  • Threat detection in MLflow, SageMaker, Vertex AI
• Module 9: AI Security Monitoring and Incident Response
  • AI SOC integration and anomaly detection
  • AI model forensics and drift monitoring
  • Response playbooks for AI-specific incidents (e.g., poisoning, exfiltration)
• Team Simulation: AI Security Tabletop Exercise
  • Simulate response to adversarial attack on deployed AI system
  • Apply MITRE ATLAS and NIST AI RMF to formulate recovery

Part 4 – Strategic Leadership, Business Continuity, and Capstone

• Module 10: AI Business Risk and Security Leadership
  • Communicating AI risks to executives and boards
  • AI investment vs. AI risk trade-offs
  • Building an AI security roadmap with measurable KPIs
• Module 11: Business Continuity and AI Security Strategy
  • AI failure scenarios and continuity planning
  • Resilience planning for critical AI workloads
  • Regulatory impact mapping and breach disclosure
• Capstone Simulation: Boardroom Briefing on AI Breach
  • Present a briefing to a simulated board of directors
  • Defend risk prioritization, mitigation strategy, and governance model
• Exam Prep Review Session
  • Recap of key frameworks: OWASP, NIST, ISO, ATLAS
  • Practice questions and scenario drills
  • Final Q&A for certification readiness

Exam Domains and Weights

Passing Criteria

• Exam Format: 100 multiple-choice and scenario-based questions
• Duration: 120 minutes
• Passing Score: 75%
• Delivery: Online proctored or approved training center
• Retake Policy: One free retake within 60 Parts