A tailored course, built for your situation
Mastering NIST CSF for AI Engineers in Telecommunications
Build governance-ready AI systems with structured security alignment across distributed teams
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
AI systems stalling in pre-deployment review due to misaligned security controls
The situation this course is for
Even well-built models face delays when they don’t speak the language of security and compliance teams. Without a shared framework, AI engineers spend cycles retrofitting instead of innovating.
Who this is for
AI Engineer at a regulated telecommunications provider, deploying scalable ML systems across diverse infrastructure and governance zones
Who this is not for
Engineers focused solely on research prototypes or isolated inference pipelines with no cross-team dependencies
What you walk away with
- System design templates pre-aligned with NIST CSF control families
- Clear mapping between model lifecycle phases and security review gates
- Reusable documentation for control evidence in multi-region deployments
- Faster sign-off from risk and compliance partners
- Increased visibility of AI work across infrastructure and security teams
The 12 modules (with all 144 chapters)
Module 1. Introduction to NIST CSF in AI System Design
Establish foundational alignment between machine learning workflows and the NIST Cybersecurity Framework, tailored for telecommunications environments. Understand how Identify, Protect, Detect, Respond, and Recover functions integrate with model development and deployment cycles. Focus on practical mapping rather than abstract compliance.
12 chapters in this module
- Defining NIST CSF in the context of AI engineering
- How telecommunications infrastructure shapes security expectations
- Key differences between ML and traditional software security
- The role of AI engineers in proactive risk reduction
- Mapping NIST CSF core functions to AI system phases
- Common misalignments between data science and security teams
- Signal patterns from audit findings in regulated AI deployments
- Leveraging NIST CSF to anticipate compliance requirements
- Integrating security posture into sprint planning
- Building stakeholder trust through early framework alignment
- Documenting control rationale for distributed reviewers
- Establishing feedback loops with security operations
Module 2. Threat Modeling for Machine Learning Pipelines
Apply structured threat modeling to ML data flows, training jobs, and inference endpoints. Use NIST CSF as a lens to identify high-impact risks before deployment. Focus on real-world attack vectors in cloud and hybrid environments.
12 chapters in this module
- Threat categories unique to machine learning systems
- Using STRIDE to evaluate AI pipeline risks
- Mapping threats to NIST CSF Protect and Detect functions
- Data poisoning and model inversion risks in telecom use cases
- Identifying privileged access points in training infrastructure
- Inference-time adversarial attacks and mitigations
- Supply chain risks in third-party model components
- Logging requirements for attack detection
- Red teaming assumptions for AI system validation
- Documenting threat model assumptions across teams
- Integrating findings into model cards and system design
- Updating threat models as infrastructure evolves
Module 3. Control Mapping for Model Development
Align each stage of the ML lifecycle with NIST CSF subcategories. Build evidence-ready outputs that satisfy security reviewers without slowing innovation. Focus on automation-friendly documentation patterns.
12 chapters in this module
- Mapping data ingestion to PR.DS controls
- Version control as a security control
- Access governance for model training environments
- Secure configuration of compute resources
- Authentication and authorization in pipeline orchestration
- Encryption needs for model artifacts and datasets
- Logging model training activity for audit
- Vulnerability management in containerized workflows
- Change control for production model updates
- Incident response planning for model drift alerts
- Documentation standards for cross-functional reviewers
- Using templates to scale control evidence
Module 4. Security Integration in CI/CD for ML
Embed security checks into automated ML pipelines. Ensure every model build produces compliance-ready artifacts. Focus on measurable control outcomes rather than policy abstractions.
12 chapters in this module
- Integrating static analysis into ML code review
- Automated policy checks for training scripts
- Secrets scanning in container builds
- Artifact signing for model provenance
- Dynamic analysis of inference endpoints
- Automated compliance gate logic in deployment
- Security test coverage metrics for ML systems
- Integrating findings into developer feedback loops
- Pipeline observability for security teams
- Handling false positives in automated checks
- Versioning security policies alongside code
- Audit readiness through pipeline consistency
Module 5. Cross-Functional Evidence Generation
Produce documentation that satisfies security, risk, and compliance reviewers without requiring rework. Focus on reusable formats accepted across business units.
12 chapters in this module
- Designing system diagrams for security review
- Model data flow documentation for compliance
- Control implementation statements for NIST CSF
- Evidence collection workflows for audit cycles
- Standardizing artifact formats across teams
- Integrating feedback from compliance reviewers
- Building audit trails for model updates
- Documenting third-party risk assessments
- Maintaining living system documentation
- Version control for compliance artifacts
- Sharing evidence with distributed stakeholders
- Reducing review cycles through clarity
Module 6. Governance Alignment Across Infrastructure Units
Navigate differing security expectations across network, cloud, and edge environments. Use NIST CSF as a common language to align stakeholders.
12 chapters in this module
- Identifying security variance across Verizon units
- Mapping regional requirements to core framework
- Negotiating control expectations with infrastructure teams
- Documenting exceptions with traceable rationale
- Standardizing terminology across domains
- Facilitating joint review sessions
- Building trust through consistent documentation
- Escalation paths for unresolved conflicts
- Incorporating lessons from past deployments
- Adapting to evolving infrastructure standards
- Maintaining alignment during team transitions
- Tracking policy drift across business units
Module 7. Incident Response for AI Systems
Prepare for security events involving ML systems. Define detection, containment, and recovery procedures aligned with organizational incident response plans.
12 chapters in this module
- Defining abnormal behavior in model predictions
- Logging requirements for forensic analysis
- Model rollback procedures during incidents
- Coordinating with SOC on AI-related alerts
- Communicating incidents to stakeholders
- Documenting root cause for model failures
- Preserving evidence from training environments
- Post-mortem templates for AI incidents
- Updating controls based on incident learnings
- Testing response plans with tabletop exercises
- Integrating ML systems into existing playbooks
- Reducing mean time to detect and respond
Module 8. Vendor and Third-Party Risk in AI
Assess and manage risks from external tools, APIs, and pre-trained models. Apply NIST CSF to vendor review and integration.
12 chapters in this module
- Classifying third-party components in ML systems
- Security requirements for API integrations
- Evaluating pre-trained model risk profiles
- Contractual considerations for model usage
- Audit rights for vendor environments
- Documentation needs for third-party attestations
- Monitoring vendor compliance posture
- Incident response coordination with vendors
- Exit strategies for deprecated integrations
- Maintaining inventory of external dependencies
- Assessing supply chain integrity
- Building fallback capabilities
Module 9. Continuous Control Monitoring
Implement ongoing verification of security controls. Move beyond point-in-time audits to sustained compliance.
12 chapters in this module
- Defining metrics for control effectiveness
- Automated checks for configuration drift
- Monitoring model access patterns
- Detecting unauthorized changes to pipelines
- Alerting on policy violations
- Integrating control data into dashboards
- Reporting compliance status to stakeholders
- Adjusting controls based on threat intelligence
- Maintaining evidence between audit cycles
- Reducing manual review burden
- Scaling monitoring across multiple models
- Documenting control improvements over time
Module 10. Change Management for AI Systems
Structure updates to models and infrastructure with appropriate oversight. Ensure changes maintain security and compliance alignment.
12 chapters in this module
- Defining change types in ML systems
- Establishing approval workflows
- Documentation requirements for changes
- Testing changes before deployment
- Rollback procedures for failed changes
- Communicating changes to stakeholders
- Tracking change history for audit
- Applying change controls to infrastructure
- Managing emergency changes
- Reviewing change effectiveness
- Integrating feedback into change processes
- Reducing change-related incidents
Module 11. Training and Knowledge Transfer
Equip peers and stakeholders with NIST CSF knowledge. Scale your influence by making security practices accessible.
12 chapters in this module
- Identifying knowledge gaps in AI teams
- Developing training materials for engineers
- Creating security playbooks for common scenarios
- Mentoring junior team members
- Conducting security onboarding sessions
- Sharing best practices across teams
- Documenting institutional knowledge
- Building communities of practice
- Measuring training effectiveness
- Updating materials based on feedback
- Scaling knowledge through templates
- Reducing dependency on individual experts
Module 12. Sustaining Compliance at Scale
Maintain alignment as systems grow. Build self-reinforcing processes that survive team changes and organizational shifts.
12 chapters in this module
- Designing for long-term maintainability
- Automating compliance evidence generation
- Standardizing patterns across projects
- Documenting architectural decisions
- Onboarding new team members effectively
- Preserving knowledge through attrition
- Adapting to framework updates
- Integrating lessons from audits
- Sharing successes across the organization
- Building reputation as a trusted resource
- Extending influence to adjacent domains
- Creating lasting impact beyond individual projects
How this maps to your situation
- Pre-deployment review bottlenecks
- Cross-team security alignment
- Audit preparation cycles
- Incident response readiness
Before vs. after
Before
Designing AI systems that later require rework to meet security and compliance standards
After
Deploying ML models with embedded security alignment, accepted faster across infrastructure and governance teams
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3-4 hours per module, designed to fit around engineering workloads.
If nothing changes
Continuing without structured NIST CSF integration may lead to repeated audit findings, delayed deployments, and missed opportunities to lead on high-visibility AI initiatives.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses specifically on AI engineering contexts and NIST CSF application, with templates and examples from telecommunications environments.
Frequently asked
Is this course technical or policy-focused?
It's technical, designed for engineers. You'll learn how to build systems that naturally satisfy policy requirements through architecture and documentation.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with internal audits?
Yes. You'll learn to generate evidence that meets reviewer expectations and reduces back-and-forth during audit cycles.
$199 one-time. Approximately 3-4 hours per module, designed to fit around engineering workloads..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours