A tailored course, built for your situation
Influence across more business units with NIST CSF
Build cross-functional reach as a Machine Learning Engineer through structured security alignment
$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.
Who this is for
Senior Machine Learning Engineer operating at scale in a regulated environment, already contributing to high-impact AI systems and now positioned to shape broader controls adoption.
Who this is not for
Entry-level practitioners still mastering core ML pipelines, or executives seeking board-level narratives without hands-on implementation focus.
What you walk away with
- Map NIST CSF functions directly to ML model lifecycle stages
- Produce audit-ready documentation that security teams reuse across regions
- Lead cross-functional input on control design without escalation
- Align model deployment checklists with enterprise security posture
- Become the internal reference for AI system compliance in regulated contexts
The 12 modules (with all 144 chapters)
Module 1. NIST CSF Core Functions Overview
Understand how Identify, Protect, Detect, Respond, and Recover apply to ML systems. See how top engineering teams embed these into model workflows.
12 chapters in this module
- Control mapping basics
- Function alignment to AI
- Security team language
- Compliance integration points
- Risk tiering for models
- Documentation expectations
- Audit preparation paths
- Cross-team handoff triggers
- Incident linkage patterns
- Model lifecycle stages
- Evidence collection timing
- Regulatory alignment scope
Module 2. Identify Function Deep Dive
Pin asset management, governance, and risk assessment to real model deployments. Connect inventory practices to actual codebases and datasets.
12 chapters in this module
- Asset register linkage
- Model ownership definition
- Data classification rules
- Governance workflow entry
- Risk assessment inputs
- Compliance scope setting
- Jurisdictional triggers
- Third-party model tracking
- Open-source model use
- Vendor documentation gaps
- Internal escalation paths
- Policy deviation flags
Module 3. Protect Function Applied to ML
Secure AI systems with access controls, data protection, and model integrity checks. Implement safeguards that auditors validate.
12 chapters in this module
- Access control design
- Authentication layers
- Model signing standards
- Data encryption in use
- Training data provenance
- Model version lockdown
- Code integrity checks
- Environment separation
- Secrets management
- API protection patterns
- Change approval gates
- Deployment freeze rules
Module 4. Detect Function for Model Systems
Implement monitoring, logging, and anomaly detection tuned for ML pipelines. Build alerts that security teams trust.
12 chapters in this module
- Log schema standards
- Model drift detection
- Input validation logging
- Output anomaly alerts
- Behavioral baselines
- Performance deviation
- Security event tagging
- Threat hunting inputs
- Incident correlation
- System health signals
- Model rollback triggers
- Audit trail completeness
Module 5. Respond Function Integration
Embed incident response plans into model operations. Ensure playbooks cover data poisoning, bias escalation, and model compromise.
12 chapters in this module
- Response plan drafting
- Model compromise paths
- Data contamination signs
- Bias incident triage
- Escalation routing
- Forensic data capture
- Communication templates
- Recovery timing
- Legal team triggers
- Regulator notification
- Post-mortem structure
- Improvement loop close
Module 6. Recover Function Execution
Restore systems after incidents with validated models and updated safeguards. Document recovery steps for audit use.
12 chapters in this module
- Recovery playbook use
- Model redeployment checklist
- Validation thresholds
- Rollback verification
- Lessons documented
- Controls enhancement
- Stakeholder updates
- Audit follow-up
- Timeline tracking
- Process change log
- Team training updates
- Vendor coordination
Module 7. Risk Assessment Workshop
Conduct risk assessments for new ML projects using NIST CSF guidance. Align technical choices with enterprise risk appetite.
12 chapters in this module
- Project intake form
- Model risk scoring
- Data sensitivity rating
- Compliance alignment check
- Third-party risk
- Model transparency level
- Human oversight need
- Incident likelihood
- Impact severity scale
- Risk treatment choice
- Acceptance criteria
- Escalation threshold
Module 8. Control Implementation Playbook
Build reusable templates for implementing NIST CSF controls in ML environments. Share them across teams.
12 chapters in this module
- Template structure
- Control ownership
- Evidence requirements
- Review frequency
- Automation feasibility
- Tool integration
- Version control
- Change management
- Approval workflow
- Audit trail setup
- Cross-team access
- Maintenance plan
Module 9. Cross-Functional Collaboration
Work effectively with compliance, legal, and security teams. Use shared artifacts to align priorities and reduce friction.
12 chapters in this module
- Meeting agenda prep
- Stakeholder mapping
- Communication frequency
- Artifact sharing
- Feedback integration
- Conflict resolution
- Priority alignment
- Escalation process
- Joint decision-making
- Shared documentation
- Ownership clarity
- Progress tracking
Module 10. Audit Readiness Preparation
Prepare for audits with complete, consistent documentation. Demonstrate compliance through clear evidence trails.
12 chapters in this module
- Audit scope review
- Document collection
- Evidence validation
- Gap identification
- Remediation planning
- Stakeholder input
- Response drafting
- Follow-up tracking
- Corrective action
- Process update
- Training needs
- Audit outcome review
Module 11. Continuous Improvement Cycle
Improve controls based on incidents, audits, and feedback. Keep security practices current across evolving threats.
12 chapters in this module
- Lesson capture
- Trend analysis
- Control effectiveness
- Update planning
- Stakeholder review
- Change implementation
- Testing validation
- Documentation update
- Training rollout
- Feedback loop
- Benchmark tracking
- Innovation adoption
Module 12. Scaling Influence Across Teams
Expand your impact beyond immediate projects. Become the internal reference for NIST CSF in ML systems.
12 chapters in this module
- Mentorship approach
- Best practice sharing
- Cross-team workshops
- Template adoption
- Peer review process
- Knowledge base use
- Community building
- Recognition strategy
- Leadership visibility
- Strategic input
- External engagement
- Career growth path
How this maps to your situation
- When launching a new model with compliance oversight
- During audit preparation cycles
- After a security incident involving AI systems
- When expanding ML use into regulated regions
Before vs. after
Before
Work happens in ML silo with periodic compliance check-ins.
After
Security and compliance teams proactively request input from you on model-related controls.
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 hours per week over 4 weeks to complete all modules and apply templates to current work.
How this compares to the alternatives
Generic security courses teach abstract principles. This course focuses on NIST CSF implementation specifically for ML engineers, with templates and examples from real-world deployments at scale.
Frequently asked
Is this course suitable for engineers without a security background?
Yes. It’s designed for ML engineers who want to deepen their security and compliance fluency using the NIST CSF framework, without requiring prior security certifications.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will the materials integrate with existing internal processes?
Yes. The templates are designed to plug into Meta-level compliance workflows and adapt to existing control review cycles.
$199 one-time. Approximately 3 hours per week over 4 weeks to complete all modules and apply templates to current work..
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