Certified AI & Quantum Security Compliance & Frameworks Professional (CAQSCF)

Certified AI & Quantum Security Compliance & Frameworks Professional CAQSCF validates the ability to align AI systems and post quantum security controls with globally recognized standards and regulatory expectations. The program builds practical skill in interpreting NIST AI RMF guidance, applying ISO and IEC risk and governance requirements, and using leading AI security references such as OWASP LLM Top 10 and MITRE ATLAS to strengthen assurance.

Participants learn how to translate technical controls into audit ready evidence, design compliance friendly secure development lifecycle practices, and document decisions that stand up to internal and external scrutiny. The cybersecurity impact is direct, improving control consistency, reducing exposure from model and data risks, and enabling measurable governance of cryptography transitions. CAQSCF also supports cybersecurity teams by creating a shared language between engineering, risk, and audit functions, helping organizations demonstrate trust, safety, and resilience as AI adoption accelerates.

Learning Objectives

• Interpret and apply NIST AI RMF functions to AI governance decisions
• Map ISO and IEC requirements into implementable security controls
• Assess LLM risks using OWASP LLM Top 10 and define mitigations
• Analyze adversary techniques with MITRE ATLAS for control validation
• Plan post quantum cryptography transition using NIST standardization direction
• Produce audit ready evidence packages and traceable compliance artifacts
• Explain cybersecurity impact of AI and PQC controls across stakeholders

Audience

• Compliance Officers
• Internal and External Auditors
• Security and Risk Leaders
• Governance Risk and Compliance Professionals
• Consultants and Advisory Teams
• Cybersecurity Professionals

Program Modules

Module 1: AI Governance Standards and Control Intent

• AI governance principles and accountability models
• Control objective interpretation and applicability scoping
• Policy hierarchy and exception management
• Data classification and model asset inventory alignment
• Roles, approvals, and segregation of duties
• Governance metrics and management reporting

Module 2: NIST AI RMF Operational Mapping Practice

• Map govern function to organizational governance artifacts
• Risk identification workflows for AI systems
• Measurement approaches for model performance and safety
• Risk treatment selection and residual risk acceptance
• Monitoring triggers and continuous oversight cadences
• Documentation patterns for traceability and defensibility

Module 3: ISO Risk Management for AI and Security

• Risk context definition and stakeholder requirements
• Threat modeling inputs for AI enabled systems
• Likelihood and impact scoring consistency methods
• Control selection aligned to risk treatment plans
• Risk register structure and audit alignment
• Communicating risk decisions to leadership

Module 4: LLM Application Security and Assurance

• Prompt injection and input handling governance
• Sensitive data exposure prevention and guardrails
• Access control, secrets, and plugin integration controls
• Output validation, hallucination risk, and misuse reduction
• Logging, monitoring, and abuse detection requirements
• Secure release governance for model updates

Module 5: MITRE ATLAS and Adversarial Validation

• Adversary mindset for AI enabled attack paths
• Technique to control mapping for assurance evidence
• Test planning for model manipulation and data poisoning
• Defensive coverage analysis and control gaps
• Incident response alignment for AI related events
• Lessons learned integration into governance updates

Module 6: PQC Compliance, Audits, and Evidence Readiness

• Cryptographic inventory and dependency discovery methods
• PQC migration planning and control ownership
• Vendor and third party assurance for crypto transitions
• Evidence collection, indexing, and retention strategy
• Audit narrative development and walkthrough readiness
• Cross framework mapping and control rationalization

Exam Domains

1. AI Regulatory Readiness and Governance Strategy
2. Assurance Evidence Engineering and Audit Defense
3. Third Party and Supply Chain Compliance for AI
4. Cryptographic Transition Governance and Controls
5. Model Risk Oversight and Control Effectiveness Metrics
6. Secure SDLC Compliance and Verification Management

Course Delivery
The course is delivered through a combination of lectures, interactive discussions, hands on workshops, and project based learning, facilitated by experts in the field of Certified AI & Quantum Security Compliance & Frameworks Professional CAQSCF. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.

Assessment and Certification
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI & Quantum Security Compliance & Frameworks Professional CAQSCF.

Question Types

• Multiple Choice Questions (MCQs)
• Scenario-based Questions

Passing Criteria
To pass the Certified AI & Quantum Security Compliance & Frameworks Professional CAQSCF Certification Training exam, candidates must achieve a score of 70% or higher.

Build defensible AI and post quantum compliance capability that auditors trust and leaders can act on. Enroll in CAQSCF by Tonex to standardize your approach, strengthen assurance, and accelerate secure adoption across the enterprise.