A tailored course, built for your situation
First 90 Days: Building Board-Ready Security & AI Governance for High-Growth Insurtech
Implementation-grade NAIC MAR alignment from day one, with board-level justification, evidence flows, and control automation built in
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.
The situation this course is for
Security and compliance leaders in high-growth insurtechs spend disproportionate time reconciling controls, chasing evidence, and retrofitting narratives for NAIC MAR reviews, especially when AI systems are in scope. This creates cycle drag, team burnout, and inconsistent outcomes across audits.
Who this is for
Chief Information Security Officer and IT Director at a high-growth US insurtech, responsible for security, compliance, and technology scalability. Works across regulatory, investor, and engineering stakeholders to build trustworthy, auditable systems. Needs to prove control integrity fast, with minimal rework.
Who this is not for
This is not for junior compliance analysts, auditors, or consultants without direct accountability for security program outcomes. It’s not for firms operating legacy stacks without AI adoption plans.
What you walk away with
- Ship NAIC MAR-aligned controls in the first 90 days of security or AI initiatives
- Automate evidence collection for recurring review cycles
- Build defensible control narratives with source-backed reasoning and real-world examples
- Reduce audit prep from weeks to under 48 hours
- Turn compliance from a cost center into a strategic trust accelerator
The 12 modules (with all 144 chapters)
- What NAIC MAR is and why it applies to AI-integrated systems
- Key differences between NAIC MAR and SOX 404 in practice
- Mapping NAIC MAR domains to insurtech-specific risk areas
- How regulators interpret 'adequate oversight' in agile environments
- The role of the CISO in NAIC MAR evidence ownership
- Common misalignments between policy language and technical controls
- Case study: NAIC MAR findings in a Series B insurtech audit
- Regulatory timelines and review cycles to track
- How investor due diligence uses NAIC MAR readiness
- Integrating NAIC MAR into incident response planning
- Defining scope: what systems and data fall under MAR
- Building a NAIC MAR register for recurring use
- Setting control objectives before architecture decisions lock in
- Using risk tiering to prioritize NAIC MAR coverage
- How to scope AI models under NAIC MAR without overreach
- Defining control owners across engineering and security teams
- Writing testable control statements that pass reviewer scrutiny
- Avoiding common scoping errors in cloud-native environments
- Mapping data flows to control boundaries
- When to engage legal and compliance in control design
- Designing for change: versioning control documentation
- Integrating NAIC MAR into sprint planning and backlog grooming
- Using architecture diagrams to justify control placement
- Validating scope completeness with cross-functional walkthroughs
- Types of evidence required under NAIC MAR by domain
- Automating user access reviews with identity platforms
- Logging and retention requirements for AI decision trails
- Using API calls to pull real-time configuration snapshots
- Integrating evidence collection into CI/CD pipelines
- Designing dashboards for ongoing control monitoring
- Storing evidence in audit-ready formats with tamper protection
- Automating attestation workflows for control owners
- Scheduling evidence refreshes aligned to review cycles
- Handling exceptions and manual overrides transparently
- Validating automation accuracy with sample testing
- Documenting evidence sources for external reviewer access
- Translating technical controls into business risk language
- Building executive summaries that answer 'so what?'
- Using real-world examples to justify control decisions
- Referencing NIST CSF and other frameworks to strengthen reasoning
- Structuring narrative flows for regulator presentations
- Anticipating reviewer questions and pre-bunking gaps
- Incorporating board feedback into control improvements
- Balancing transparency with confidentiality in disclosures
- Creating versioned narrative packages for reuse
- Using visuals to show control maturity progression
- Linking control outcomes to business resilience metrics
- Defending design choices under cross-examination
- Where AI systems fall under NAIC MAR scope
- Mapping model lifecycle stages to control requirements
- Designing controls for model drift detection and response
- Auditing prompt engineering and input validation processes
- Ensuring human oversight is documented and testable
- Handling third-party AI vendors in your control framework
- Logging and explaining AI-driven underwriting decisions
- Integrating model risk management with NAIC MAR reviews
- Using synthetic data for safe testing and evidence generation
- Defining roles for ML engineers in control ownership
- Validating fairness and bias checks as part of evidence
- Building runbooks for AI incident response under MAR
- Defining RACI matrices for NAIC MAR activities
- Setting up recurring sync points for control updates
- Using shared tools to reduce email and Slack chasing
- Standardizing handoff templates between teams
- Onboarding new team members into control workflows
- Handling turnover without losing institutional knowledge
- Running table-top exercises for audit readiness
- Resolving ownership disputes over edge-case systems
- Integrating vendor management into control evidence
- Managing dependencies with third-party SaaS providers
- Using status dashboards to reduce meeting overhead
- Documenting decisions in searchable knowledge bases
- Understanding the NAIC MAR review process timeline
- Preparing evidence packages in advance of request cycles
- Conducting internal mock audits with external reviewer standards
- Responding to findings with root cause and remediation plans
- Using past findings to improve future readiness
- Coordinating with external auditors without over-sharing
- Handling requests for undocumented processes gracefully
- Managing time pressure during crunch periods
- Using feedback loops to improve control design
- Documenting compensating controls when needed
- Escalating blockers to executive sponsors effectively
- Closing out findings with verified evidence
- Scheduling regular control reviews and updates
- Tracking changes in architecture that impact control scope
- Versioning control documentation with changelogs
- Using git-like practices for policy and procedure updates
- Automating alerts for control drift
- Integrating control updates into change management workflows
- Retiring controls when systems are decommissioned
- Maintaining historical versions for audit trail purposes
- Updating evidence collection when tooling changes
- Revalidating controls after major incidents
- Communicating control changes to stakeholders
- Measuring control stability over time
- Using SIEM for NAIC MAR logging and monitoring
- Leveraging identity providers for access attestation
- Configuring cloud platforms for control-friendly architectures
- Integrating GRC platforms with technical tooling
- Using SOAR for automated response and evidence capture
- Aligning DevSecOps tools with control objectives
- Choosing SaaS vendors with compliance in mind
- Evaluating tools based on evidence export capabilities
- Building custom integrations when off-the-shelf falls short
- Ensuring API reliability for automated evidence flows
- Documenting tool configurations as part of evidence
- Managing secrets and credentials in automated systems
- Using risk scoring to prioritize control implementation
- Balancing NAIC MAR coverage with innovation velocity
- Allocating team time based on impact and likelihood
- Identifying low-effort, high-impact control improvements
- Using maturity models to guide investment decisions
- Communicating trade-offs to executive leadership
- Avoiding over-engineering in early-stage environments
- Scaling control efforts with company growth
- Benchmarking against peer insurtechs
- Justifying tooling and headcount with risk reduction metrics
- Rebalancing priorities after audit findings
- Using heat maps to show risk exposure over time
- Designing role-based training for control responsibilities
- Creating short, actionable guidance for non-experts
- Using video walkthroughs for complex evidence tasks
- Running hands-on workshops for control execution
- Providing templates and examples for common tasks
- Setting up QA processes for control performance
- Tracking completion and understanding with quizzes
- Onboarding contractors and temporary staff into control workflows
- Using feedback to improve training materials
- Measuring team confidence in control execution
- Recognizing and rewarding strong control ownership
- Updating training content with real audit feedback
- Adjusting control rigor with funding stage
- Preparing for increased regulatory scrutiny post-Series C
- Building a compliance function as you scale
- Documenting processes before they become tribal knowledge
- Using automation to avoid linear headcount growth
- Aligning with enterprise risk management frameworks
- Preparing for IPO or acquisition readiness
- Integrating with parent company standards if acquired
- Maintaining agility while adding structure
- Hiring for hybrid security-compliance roles
- Using metrics to show compliance ROI
- Transitioning from founder-led to team-led governance
How this maps to your situation
- First 90 days of new role or initiative
- Preparing for first external audit
- Introducing AI systems into regulated environment
- Scaling security program with company growth
Before vs. after
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 8, 10 hours of focused work, designed to be completed in short sessions over 3, 4 weeks.
How this compares to the alternatives
Unlike generic compliance courses or PDF frameworks, this course delivers implementation-grade guidance tailored to high-growth insurtechs with AI adoption, including real-world examples, source-backed reasoning, and automation blueprints.
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.