A tailored course, built for your situation
Deeper command of NIST 800-53 control mapping for AI systems
Turn complex compliance into clean, confident implementation
$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.
Spending too much time translating compliance requirements into technical controls?
The situation this course is for
AI engineers are often handed NIST 800-53 checklists without context, leading to misaligned implementations, rework, and friction between security and engineering teams. The gap isn’t effort, it’s precision in mapping controls to actual system design.
Who this is for
Senior AI/ML engineers working in regulated environments who need to implement NIST 800-53 controls confidently and correctly the first time
Who this is not for
Entry-level developers, auditors, or consultants without hands-on AI system implementation experience
What you walk away with
- Map NIST 800-53 controls directly to AI system design decisions
- Produce justification artefacts that pass internal and external review
- Anticipate auditor questions with pre-mapped evidence paths
- Reduce rework cycles between engineering and GRC teams
- Build repeatable templates for control implementation across projects
The 12 modules (with all 144 chapters)
Module 1. AI systems and compliance: the real intersection
Understand how NIST 800-53 applies to AI pipelines, data flows, and model deployment, not generic IT systems, but your actual architecture.
12 chapters in this module
- Where AI breaks traditional compliance models
- Control relevance by AI system layer
- High-impact controls for model training
- Mapping access to model inference endpoints
- Data lineage and control evidence
- Real-world AI compliance audit findings
- Common misinterpretations of AC-1
- Tailoring controls without weakening posture
- AI-specific evidence collection patterns
- When AI meets FedRAMP boundaries
- Control overlap with model risk management
- Architectural decisions that simplify compliance
Module 2. Control mapping fundamentals
Master the core method: translating control intent into specific, testable implementation steps.
12 chapters in this module
- Intent vs implementation: decoding control statements
- Identifying responsible roles in AI workflows
- Three types of control evidence for AI
- Mapping SC-13 to model input validation
- Automatable vs manual controls
- Documenting design decisions as evidence
- Using architecture diagrams as artefacts
- Avoiding overcompliance with smart scoping
- Control boundaries in microservices
- Versioning control mappings
- Cross-referencing with NIST AI RMF
- Building audit-ready mapping tables
Module 3. Access controls for AI systems
Implement IAM and data access in alignment with AC-1 through AC-6 and beyond.
12 chapters in this module
- AC-1 policy scope for AI teams
- Role-based access for model developers
- Attribute-based access for data scientists
- Time-bound access for external collaborators
- Model registry access controls
- Service account anti-patterns
- Mapping AC-2 to user provisioning
- AC-3 controls in cloud AI platforms
- Least privilege for inference endpoints
- Dynamic access revocation patterns
- AC-4 mechanisms for API gateways
- Session timeouts in notebook environments
Module 4. Audit logging and monitoring
Design logs that meet AU control requirements and support actual debugging and compliance review.
12 chapters in this module
- AU-1 organisational policy alignment
- AU-2 event coverage for AI workloads
- Logging model training triggers
- Capturing data drift detection events
- AU-3 audit content retention
- Immutable log storage patterns
- Centralised logging architecture
- AU-6 review frequency benchmarks
- Automated anomaly detection triggers
- Log integrity verification methods
- Correlating logs across pipeline stages
- Audit trail completeness checks
Module 5. System interconnections and boundaries
Map controls like CA-3, CA-7 and SI-1 to distributed AI system design.
12 chapters in this module
- CA-3 control assessment frequency
- Third-party model provider oversight
- SI-1 system characteristics for AI
- External data source risk profiles
- Model API interconnection controls
- Vendor risk documentation templates
- Penetration test scope for AI APIs
- Automated control monitoring tools
- Performance thresholds as control triggers
- Inter-system trust boundaries
- Model update validation requirements
- Secure handoff patterns between teams
Module 6. Configuration management for AI
Apply CM-2, CM-6 and CM-9 to model versions, pipeline code, and infrastructure as code.
12 chapters in this module
- CM-2 baseline establishment for AI systems
- Version control for training data
- Model registry as configuration item
- Change control for hyperparameters
- Automated drift detection in pipelines
- CM-6 software usage restrictions
- Infrastructure as code versioning
- Model rollback procedures
- CM-9 configuration complexity
- Pipeline retraining triggers
- Baseline deviation alerts
- Configuration audit trail structure
Module 7. Data protection in AI workflows
Implement encryption and privacy controls across training, inference, and storage.
12 chapters in this module
- SC-1 system architecture principles
- Encryption of model weights at rest
- Inference data in memory protections
- SC-8 transmission confidentiality
- Data anonymisation for training
- PII handling in prompt logs
- Tokenisation for sensitive inputs
- Model memorisation risk controls
- Data retention policies by type
- Secure deletion of training caches
- Cross-border data flow compliance
- Privacy-preserving model design
Module 8. Risk assessment integration
Align control mapping with ongoing risk assessment practices and executive reporting.
12 chapters in this module
- RA-1 risk assessment policy
- Threat modelling for AI systems
- Vulnerability scanning for models
- RA-5 vulnerability monitoring
- Model bias as a risk factor
- Third-party AI component risks
- Likelihood and impact scoring
- Risk register integration
- Executive risk summary structure
- Risk treatment plan templates
- Risk acceptance documentation
- Continuous risk monitoring
Module 9. Security planning and documentation
Produce and maintain documentation that satisfies compliance reviewers and internal stakeholders.
12 chapters in this module
- Developing AI system security plan
- PL-2 control implementation policy
- System categorisation under FIPS 199
- Control selection justification
- Tailoring rationale documentation
- Supplemental guidance for AI
- Document update frequency
- Version control for security plans
- Reviewer access setup
- Cross-referencing with architecture docs
- Automated documentation generation
- Single source of truth patterns
Module 10. Incident response for AI systems
Design IR procedures that address AI-specific failure modes and data exposures.
12 chapters in this module
- IR-1 policy development
- Model poisoning detection
- Adversarial attack response
- Data leakage from outputs
- IR-4 incident handling
- Model rollback as containment
- Forensic data preservation
- Notification thresholds
- Post-incident review structure
- Lessons learned documentation
- Tabletop exercises for AI risks
- Coordination with legal team
Module 11. Implementation playbook: AI system case study
Walk through a complete NIST 800-53 mapping for a production AI service.
12 chapters in this module
- Case study: fraud detection model
- System boundary definition
- Control selection rationale
- Access control mapping
- Audit log design
- Data protection measures
- Configuration management
- Risk assessment summary
- Security plan excerpt
- Incident response plan
- Evidence collection workflow
- Audit preparation checklist
Module 12. Sustaining compliance at scale
Build systems that maintain compliance as teams and models grow.
12 chapters in this module
- Template reuse across projects
- Automated control validation
- Onboarding new team members
- Control consistency checks
- Compliance debt tracking
- Toolchain integration patterns
- Cross-team review rhythms
- Updating mappings for new controls
- Lessons from high-performing teams
- Scaling documentation practices
- Feedback from auditors
- Continuous improvement roadmap
How this maps to your situation
- When starting a new AI system design
- Before internal compliance review
- During vendor or auditor assessment
- After a control finding or gap
Before vs. after
Before
Receiving compliance checklists without clear application to AI systems, leading to rework and misalignment.
After
Confidently mapping NIST 800-53 controls to AI architecture with precision and producing audit-ready artefacts.
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 to be completed incrementally alongside active projects.
If nothing changes
Continuing to rely on ad-hoc compliance mapping increases rework, delays, and exposure to audit findings that could impact project timelines and cross-team credibility.
How this compares to the alternatives
Unlike generic NIST 800-53 training, this course is tailored to AI engineers with real-world control mapping examples, repeatable templates, and implementation focus, no theory, no fluff, just applied precision.
Frequently asked
Is this course suitable for someone working on AI systems in regulated industries?
Yes, it’s specifically designed for engineers implementing NIST 800-53 in AI/ML contexts within finance, healthcare, and government-adjacent sectors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does it cover integration with other frameworks like SOC 2 or ISO 27001?
The focus is NIST 800-53, but control mappings include cross-references where relevant to common overlaps in practice.
$199 one-time. Approximately 3 hours per module, designed to be completed incrementally alongside active projects..
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