A tailored course, built for your situation
Mastering NIST AI RMF for Security Practitioners in AI-Driven Environments
Build trusted AI governance frameworks with confidence and precision
$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.
Generic AI governance courses don’t stick, they’re not tailored to the real pressure points of security practitioners in fast-moving AI environments.
The situation this course is for
Most AI governance training is theoretical or tool-specific. Practitioners like you need concrete, framework-backed methods to evaluate AI risk, produce defensible artefacts, and respond confidently to escalations, not just awareness, but authority.
Who this is for
Security practitioner in an AI-forward tech company, working at the intersection of compliance, risk, and emerging technology with increasing expectation to lead on governance.
Who this is not for
This is not for junior analysts, product marketers, or teams looking for tool-specific onboarding. It’s for experienced security professionals who need to lead with credibility.
What you walk away with
- Produce regulator-ready AI risk assessments using NIST AI RMF structure
- Own end-to-end vendor review cycles for AI systems with documented justification
- Respond confidently to M&A due diligence requests involving AI risk
- Lead internal AI governance workshops with framework-backed materials
- Build a personal library of reusable templates and decision logs
The 12 modules (with all 144 chapters)
Module 1. Foundations of NIST AI RMF in Security Context
Understand how NIST AI RMF integrates with existing security frameworks and fills gaps in AI-specific risk coverage.
12 chapters in this module
- What NIST AI RMF solves that ISO 27001 doesn’t
- Mapping Trustworthiness categories to security domains
- Differences from traditional risk matrices
- When to apply NIST AI RMF vs. OECD AI Principles
- Core terminology for cross-functional alignment
- How NIST AI RMF interacts with SOC 2
- Security-specific interpretation of the Playbook
- Key stakeholders in internal rollout
- Common misapplications in tech environments
- Documenting initial risk posture
- Version control of AI risk decisions
- Integrating with incident response plans
Module 2. Govern Function: Assigning Accountability
Establish clear ownership for AI risk decisions without stepping on engineering ownership.
12 chapters in this module
- Designating AI risk stewards by domain
- Escalation paths for unresolved disputes
- Creating risk appetite statements for AI
- Aligning with legal and compliance teams
- Documenting governance boundaries
- Onboarding peer reviewers
- Board-level expectations without board-level focus
- Roles in AI model review boards
- Conflict resolution frameworks
- Updating governance as models evolve
- Risk dashboard ownership
- Reporting cadence with leadership
Module 3. Map Function: Identifying AI System Boundaries
Accurately scope AI systems for assessment, even in complex, pipeline-driven environments.
12 chapters in this module
- Defining AI system start and end points
- In-scope vs out-of-scope components
- Data pipeline boundaries for AI models
- Third-party model ingestion
- Open-source model usage tracking
- Versioning AI system definitions
- Documenting data provenance
- Identifying inference endpoints
- Security classification of AI components
- Boundary validation with engineering
- Change triggers for re-mapping
- Template for AI system register
Module 4. Measure Function: Quantifying AI Risks
Apply repeatable methods to score AI risks in ways that resonate with technical and non-technical stakeholders.
12 chapters in this module
- Adapting FAIR to AI contexts
- Scoring model drift risk
- Bias detection thresholds
- Security exploit likelihood
- Downstream impact analysis
- Reputation risk scoring
- Combining qualitative and quantitative inputs
- Calibrating risk scales
- Peer review of risk scores
- Versioning measurement criteria
- Automated scoring triggers
- Documenting assumptions
Module 5. Manage Function: Mitigating AI Risks
Design and track mitigation plans that work across security, engineering, and compliance timelines.
12 chapters in this module
- Mitigation ownership assignment
- Engineering feasibility checks
- Security control alignment
- Third-party vendor requirements
- Documentation of compensating controls
- Escalation when controls fail
- Monitoring effectiveness
- Adapting mitigations over time
- Integrating with patch cycles
- Reporting progress to stakeholders
- Updating risk register
- Closure criteria
Module 6. Assurance for Regulator-Facing Reviews
Prepare defensible, concise responses for external auditors and regulators.
12 chapters in this module
- Common regulator questions
- Evidence pack structure
- Version-controlled artefacts
- Gap analysis for compliance
- Internal pre-review process
- Working with legal teams
- Documenting exceptions
- Response timelines
- Third-party auditor coordination
- Follow-up tracking
- Lessons from past reviews
- Template response library
Module 7. AI Risk in M&A Due Diligence
Lead AI risk assessment during acquisitions and partnerships with clear frameworks.
12 chapters in this module
- Initial screening checklist
- AI model inventory requests
- Third-party dependency review
- Security incident history
- Bias audit access
- Model documentation completeness
- Governance maturity score
- Integration risk assessment
- Vendor lock-in analysis
- Escalation to legal teams
- Final due diligence package
- Post-acquisition integration plan
Module 8. Cross-Team Workflow Integration
Embed AI risk practices in engineering, product, and security workflows.
12 chapters in this module
- CI/CD pipeline integration
- Pull request checklists
- Model registration requirements
- Security gate criteria
- Documentation handoffs
- Change approval workflows
- Incident escalation paths
- Model update notifications
- Stale model decommissioning
- Automated compliance checks
- Feedback loops with data science
- Post-mortem integration
Module 9. Vendor Review Lifecycle for AI Systems
Own the end-to-end evaluation and monitoring of third-party AI providers.
12 chapters in this module
- Request for information templates
- Security questionnaire design
- Evidence collection process
- Third-party audit report review
- AI-specific clauses in contracts
- Ongoing monitoring requirements
- Risk scoring of vendors
- Escalation triggers
- Vendor offboarding
- Multi-vendor comparison
- Negotiation support
- Lessons learned repository
Module 10. Incident Response for AI Systems
Adapt existing IR plans to include AI-specific failure modes and reporting paths.
12 chapters in this module
- AI failure mode taxonomy
- Detection of model drift
- Bias incident triage
- Model rollback procedures
- Notification requirements
- Forensic data preservation
- Legal and regulatory reporting
- Post-incident review
- Reputational impact assessment
- Public statement coordination
- Insurance claims
- Lessons integration
Module 11. Building Institutional Knowledge
Create documented practices that survive personnel changes and leadership shifts.
12 chapters in this module
- Knowledge capture from subject matter experts
- Documenting tribal knowledge
- Version-controlled playbooks
- Onboarding new team members
- Succession planning
- Internal training materials
- Lessons learned tracking
- Benchmarking against peers
- Feedback collection
- Updating based on incidents
- Stakeholder communication
- Archiving obsolete processes
Module 12. Maintaining Relevance Amid Changing Standards
Stay ahead of regulatory shifts and framework updates without constant reinvention.
12 chapters in this module
- Tracking NIST updates
- AI Act compliance watch
- EU AI Act mapping
- Industry coalition participation
- Internal change advisory board
- Framework update impact analysis
- Stakeholder notification process
- Phased rollout of changes
- Training updates
- Documentation refresh
- Audit trail maintenance
- Lessons from early adopters
How this maps to your situation
- When regulator asks for AI risk posture
- Before M&A target integration begins
- When new AI vendor contract is up for renewal
- After AI incident is detected
Before vs. after
Before
Waiting for others to define AI risk process, reacting to requests, creating one-off artefacts
After
Leading with a documented framework, producing regulator-ready outputs, and owning escalation paths
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 module, designed for self-paced learning with immediate applicability.
If nothing changes
Without structured AI risk practices, security teams risk being bypassed during critical events like M&A and regulatory reviews, reducing visibility and influence.
How this compares to the alternatives
Unlike generic AI ethics courses or tool-specific training, this course delivers a security-first, NIST AI RMF-based methodology tailored to practitioners in high-stakes environments.
Frequently asked
Is this course technical or strategic?
It’s for technical practitioners in strategic roles. You’ll learn to lead with framework-backed authority while staying grounded in security realities.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me in external audits?
Yes. Module 6 focuses on preparing assurance artefacts and responses for regulator-facing reviews.
$199 one-time. Approximately 3 hours per module, designed for self-paced learning with immediate applicability..
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