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CMP7875 Govern AI Systems with Confidence Using NIST and Compliance Frameworks

$199.00
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A tailored course, built for your situation

Govern AI Systems with Confidence Using NIST and Compliance Frameworks

A step-by-step guide to governing AI systems with confidence using NIST, compliance standards, and real-world implementation patterns.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

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.
Audit narratives that fall apart under cross-functional scrutiny

The situation this course is for

Technical teams build AI systems with security in mind, but when compliance or legal reviews hit, the control mappings lack shared language. Last-minute rework, conflicting interpretations of NIST, and ambiguous attestation packages delay deployment and erode trust.

Who this is for

Security executive (CISO, VP Infosec) with CISSP credential, responsible for aligning technical architecture with compliance outcomes. Values precision, traceability, and peer-resilient reasoning.

Who this is not for

Individual contributors without decision influence on control frameworks, vendors selling point tools, or teams looking for high-level AI ethics over operational governance.

What you walk away with

  • Produce AI governance documentation that withstands peer challenge with clear lineage to NIST and compliance standards
  • Reduce rework cycles by aligning technical controls and compliance language upfront
  • Explain AI system boundaries and risk treatments using specific examples and sourced rationale
  • Confidently represent security and compliance posture in cross-functional reviews
  • Turn CISSP-grade discipline into structured AI governance artifacts

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Governance for Security Leaders
Establish the core principles of AI governance as they apply to CISO responsibilities and compliance expectations.
12 chapters in this module
  1. Defining AI governance in the context of enterprise risk management
  2. Mapping CISO accountabilities to emerging AI regulatory expectations
  3. Key differences between traditional IT risk and AI system risk
  4. How NIST AI RMF aligns with existing cybersecurity frameworks
  5. The role of transparency and documentation in peer-reviewed decisions
  6. Common misalignments between engineering intent and compliance interpretation
  7. Integrating AI governance into existing GRC workflows
  8. Balancing innovation velocity with control maturity
  9. Understanding the lifecycle stages of AI systems from governance perspective
  10. Identifying critical touchpoints for security intervention
  11. Leveraging CISSP domains to strengthen AI governance posture
  12. Setting measurable objectives for AI governance maturity
Module 2. NIST AI Risk Management Framework Deep Dive
Break down the NIST AI RMF into actionable components tailored for security implementation.
12 chapters in this module
  1. Overview of NIST AI RMF structure and intended audience
  2. Mapping Govern function to CISO-led oversight processes
  3. Implementing Map phase with traceable data provenance
  4. Characterizing risk in AI systems using standardized taxonomies
  5. Incorporating stakeholder values into risk assessment criteria
  6. Developing playbooks for monitoring AI system behavior post-deployment
  7. Creating feedback loops between operations and governance
  8. Aligning AI RMF outcomes with board-level risk appetite statements
  9. Using AI RMF to standardize vendor evaluation questionnaires
  10. Tailoring AI RMF for sector-specific regulatory environments
  11. Documenting AI RMF application for auditor review
  12. Maintaining version control of AI governance artifacts
Module 3. Integrating NIST CSF with AI System Controls
Apply NIST Cybersecurity Framework functions to secure AI development and deployment pipelines.
12 chapters in this module
  1. Applying Identify function to AI asset inventory and classification
  2. Using Protect function to enforce model access controls
  3. Detect function adaptations for anomalous AI behavior
  4. Respond protocols specific to AI model compromise scenarios
  5. Recover strategies for corrupted training data or poisoned models
  6. Mapping CSF Subcategories to AI-specific control implementations
  7. Linking AI governance activities to CSF Implementation Tiers
  8. Using CSF Profiles to benchmark AI security maturity
  9. Crosswalking CSF with AI RMF for unified reporting
  10. Automating evidence collection for CSF-aligned AI controls
  11. Training security teams on AI-specific CSF applications
  12. Demonstrating CSF alignment during compliance audits
Module 4. Compliance Alignment Across GDPR, CCPA, and Sector Regulations
Ensure AI systems meet privacy and sector-specific regulatory requirements through structured governance.
12 chapters in this module
  1. Assessing AI system impact under GDPR Article 35 requirements
  2. Implementing data subject rights mechanisms in AI workflows
  3. Designing for CCPA opt-out rights in personalized AI models
  4. Handling biometric data processing in accordance with BIPA
  5. Meeting FTC expectations for truthful AI representations
  6. Aligning AI advertising systems with CAN-SPAM and state laws
  7. Sector-specific considerations for healthcare AI under HIPAA
  8. Financial services AI compliance with GLBA and Reg B
  9. Education sector AI systems and FERPA compliance requirements
  10. Documenting algorithmic fairness assessments for regulatory review
  11. Preparing for state privacy law variations in multi-jurisdiction deployments
  12. Maintaining compliance logs for AI-driven decision systems
Module 5. Control Mapping and Evidence Packaging
Build defensible control mappings that connect technical implementation to compliance obligations.
12 chapters in this module
  1. Structuring control statements for clarity and consistency
  2. Linking technical configurations to compliance requirement references
  3. Creating visual control mapping diagrams for cross-functional review
  4. Writing evidence descriptions that satisfy auditor expectations
  5. Standardizing evidence formats across multiple frameworks
  6. Using tables to show coverage across NIST, GDPR, and internal policies
  7. Versioning control mappings to reflect system changes
  8. Automating control mapping updates from infrastructure-as-code
  9. Conducting internal walkthroughs of evidence packages
  10. Anticipating challenge points in control interpretation
  11. Documenting exceptions with compensating control justifications
  12. Archiving historical versions for audit trail purposes
Module 6. Attestation and Audit Readiness
Prepare for successful audits with well-structured attestation packages and stakeholder coordination.
12 chapters in this module
  1. Understanding auditor expectations for AI system reviews
  2. Preparing SOC 2 Type II readiness assessments for AI workloads
  3. Compiling evidence portfolios for external audit cycles
  4. Conducting mock audits to identify documentation gaps
  5. Coordinating interviews between technical teams and auditors
  6. Writing clear responses to auditor inquiries and findings
  7. Addressing scope limitations in AI system attestations
  8. Demonstrating continuous monitoring capabilities
  9. Presenting risk treatment plans for unresolved findings
  10. Leveraging automation tools to reduce audit preparation time
  11. Maintaining auditor communication logs throughout engagement
  12. Closing out audit action items with verified remediation evidence
Module 7. Vendor Oversight and Third-Party AI Systems
Extend governance practices to third-party AI solutions and managed services.
12 chapters in this module
  1. Assessing vendor AI governance maturity during procurement
  2. Negotiating contractual terms for AI system transparency
  3. Reviewing third-party SOC reports for AI-relevant controls
  4. Conducting on-site assessments of vendor AI development practices
  5. Managing API security for externally hosted AI models
  6. Monitoring vendor model updates and retraining schedules
  7. Enforcing data handling requirements in third-party agreements
  8. Tracking compliance obligations that transfer to vendors
  9. Creating exit strategies for third-party AI dependencies
  10. Documenting due diligence efforts for regulatory review
  11. Benchmarking vendor performance against industry peers
  12. Managing concentration risk in AI service provider portfolios
Module 8. Incident Response Planning for AI Systems
Adapt incident response procedures to address AI-specific failure modes and attack vectors.
12 chapters in this module
  1. Identifying unique AI system failure indicators
  2. Classifying AI incidents by impact type and escalation path
  3. Updating IR playbooks to include model poisoning scenarios
  4. Containing compromised AI models without disrupting service
  5. Investigating data leakage through model inversion attacks
  6. Responding to adversarial input manipulation attempts
  7. Communicating AI-related incidents to stakeholders
  8. Conducting post-incident reviews focused on systemic fixes
  9. Testing IR plans with AI-specific tabletop exercises
  10. Coordinating with legal counsel on disclosure obligations
  11. Documenting lessons learned from AI incident simulations
  12. Improving detection capabilities based on incident data
Module 9. Model Development Lifecycle Governance
Embed governance checkpoints throughout the AI model development process.
12 chapters in this module
  1. Establishing governance roles in model development teams
  2. Requiring documentation at each stage of the model lifecycle
  3. Implementing code review standards for AI algorithms
  4. Verifying data quality and bias testing before training
  5. Approving model architectures based on risk profile
  6. Validating model performance against defined metrics
  7. Documenting hyperparameter choices and tuning rationale
  8. Conducting pre-deployment risk assessments
  9. Obtaining cross-functional sign-off before release
  10. Archiving model versions and associated metadata
  11. Tracking model lineage from development to production
  12. Enabling rollback procedures for problematic deployments
Module 10. Monitoring and Continuous Compliance
Implement ongoing monitoring to maintain compliance as AI systems evolve.
12 chapters in this module
  1. Defining key risk indicators for AI system operations
  2. Setting thresholds for automated anomaly detection
  3. Scheduling regular model validation checks
  4. Monitoring for concept drift and performance degradation
  5. Auditing user interactions with AI systems
  6. Logging model inference requests and outputs
  7. Generating compliance dashboards for leadership review
  8. Automating policy violation alerts
  9. Updating documentation to reflect system changes
  10. Reassessing risk profiles after significant modifications
  11. Conducting periodic reassessment of ethical implications
  12. Maintaining audit trails for regulatory inspections
Module 11. Stakeholder Communication and Executive Reporting
Develop clear communication strategies for discussing AI governance with executives and non-technical stakeholders.
12 chapters in this module
  1. Translating technical risks into business impact terms
  2. Creating executive summaries of AI governance posture
  3. Presenting risk treatment options to decision-makers
  4. Facilitating discussions on acceptable risk levels
  5. Explaining trade-offs between innovation and control
  6. Reporting on compliance status across multiple frameworks
  7. Visualizing AI risk exposure using heat maps
  8. Preparing for questions from investors or board members
  9. Building trust through transparent communication
  10. Educating stakeholders on AI governance fundamentals
  11. Managing expectations around AI system limitations
  12. Documenting key messages for consistent external messaging
Module 12. Scaling AI Governance Across the Organization
Expand governance practices to support organization-wide AI adoption.
12 chapters in this module
  1. Developing centralized AI governance policies
  2. Creating reusable templates for project teams
  3. Training developers on governance requirements
  4. Establishing center of excellence for AI best practices
  5. Implementing governance as code in CI/CD pipelines
  6. Integrating AI governance into enterprise architecture
  7. Measuring effectiveness of governance programs
  8. Sharing lessons learned across business units
  9. Adapting frameworks for different risk tolerance levels
  10. Managing resource allocation for governance activities
  11. Evolving policies based on operational experience
  12. Positioning AI governance as an enabler of responsible innovation

How this maps to your situation

  • When preparing for first AI-focused audit
  • After acquiring third-party AI capability
  • During company-wide AI adoption initiative
  • Before launching customer-facing AI product

Before vs. after

Before
Spending weeks assembling audit-ready documentation with inconsistent terminology and fragmented evidence trails.
After
Walking into reviews with organized, source-backed packages that demonstrate clear alignment between technical controls and compliance requirements.

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 18, 24 hours total, designed for completion in short sessions over several weeks.

If nothing changes
Without structured governance, AI initiatives face delays, inconsistent risk treatment, and potential regulatory scrutiny due to lack of defensible decision records.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementable patterns used by leading security teams to produce auditable, peer-defensible AI governance artifacts grounded in NIST and compliance frameworks.

Frequently asked

Is this course technical or strategic?
It's implementation-focused, bridging technical execution and compliance requirements with concrete examples, templates, and decision frameworks used by security leaders.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does it cover specific tools or platforms?
No, it focuses on framework application and artifact creation rather than tool-specific configurations.
$199 one-time. Approximately 18, 24 hours total, designed for completion in short sessions over several weeks..

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
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