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CMP4072 AI Code Ownership for IT and Compliance Leaders

$199.00
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The Executive Diagnostic and Governance Toolkit

AI Code Ownership for IT and Compliance Leaders

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing aI agents will soon own parts of your production codebase. Cognition's autonomous software engineer, Devin, is being integrated into production workflows, meaning code ownership will split between humans and AI agents within 12 months. This means code review, testing, and deployment roles must adapt to include oversight of AI-generated logic. Teams that treat AI as just a coding assistant will fall behind. The immediate question: Ask your development lead which tasks are being handed to AI tools and define how your team will validate the output.

$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.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
AI agents are now writing and deploying production code, but your code review, testing, and compliance processes haven’t caught up.

The situation this is built for

Your team is responsible for systems where AI now generates, modifies, and deploys code autonomously. Yet your validation workflows, deployment gates, and compliance audits still assume human authorship. This mismatch creates blind spots in quality, security, and regulatory accountability. Without a clear framework to govern AI-authored logic, your team risks technical debt, compliance failures, and operational incidents caused by unreviewed code. The tools are here. The workflows are not.

Who this is for

IT leaders, operations managers, compliance officers, and service management leads responsible for code quality, deployment integrity, and regulatory adherence in software delivery pipelines.

Who this is not for

Individual developers looking to use AI coding tools, startup founders building AI engineering tools, or executives seeking high-level AI trends without operational detail.

What you walk away with

  • Define which code modules are safe for AI ownership
  • Implement validation checkpoints for AI-generated logic
  • Align compliance requirements with autonomous code changes
  • Document oversight responsibilities between humans and AI agents
  • Create an audit trail for AI-authored production changes

How this maps to your situation

  • Current state: AI tools operate without formal oversight
  • Transition state: Teams pilot AI governance frameworks
  • Target state: Full integration of AI into code ownership models
  • Future state: Autonomous updates governed by policy

Before vs. after

Before
Unclear accountability for AI-written code, inconsistent review practices, compliance gaps, and reactive incident management.
After
Defined oversight model, standardized validation processes, compliant audit trails, and proactive governance of AI-authored logic.

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 6-8 hours per module, designed to be completed alongside regular work over 8-12 weeks.

If nothing changes
Continuing without a governance model for AI code ownership leads to undetected vulnerabilities, compliance violations, production outages from unreviewed logic, and loss of control over technical direction.

How this compares to the alternatives

Unlike generic AI awareness courses, this program delivers actionable frameworks, specific to code ownership, with templates and playbooks focused on compliance, validation, and governance of AI-authored production logic.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Understanding AI Code Ownership Shifts
Introduce the concept of shared code ownership between humans and AI agents and its implications for governance.
12 chapters in this module
  1. Recognizing when AI generates production code independently
  2. Mapping current tasks handled by AI coding tools
  3. Identifying code modules already influenced by AI agents
  4. Assessing team awareness of AI-authored logic
  5. Differentiating between AI assistance and AI ownership
  6. Reviewing recent production deployments for AI contributions
  7. Documenting known AI-generated code changes
  8. Evaluating version control annotations for AI authorship
  9. Establishing baseline metrics for AI code volume
  10. Interviewing developers on AI tool usage patterns
  11. Classifying code changes by level of AI autonomy
  12. Creating a timeline of AI integration into workflows
Module 2. Auditing Current Code Review Practices
Evaluate existing code review protocols for their ability to handle AI-generated logic.
12 chapters in this module
  1. Reviewing pull request templates for AI-specific checks
  2. Assessing reviewer capacity to validate AI-written logic
  3. Identifying gaps in understanding AI-generated algorithms
  4. Measuring time spent reviewing AI versus human code
  5. Determining if AI code receives equivalent scrutiny
  6. Analyzing past merge decisions involving AI contributions
  7. Evaluating comment quality on AI-authored pull requests
  8. Checking for standardized review criteria for AI code
  9. Mapping code ownership tags in version control systems
  10. Auditing reviewer assignment logic for AI submissions
  11. Documenting exceptions made for AI-generated changes
  12. Benchmarking review thoroughness across team members
Module 3. Defining AI Code Validation Standards
Develop criteria to assess correctness, safety, and compliance of AI-generated code.
12 chapters in this module
  1. Specifying functional correctness requirements for AI code
  2. Establishing security thresholds for AI-authored logic
  3. Creating test coverage expectations for AI-generated modules
  4. Defining performance benchmarks for AI-introduced changes
  5. Setting style and documentation standards for AI output
  6. Requiring explainability for non-trivial AI-written logic
  7. Implementing linting rules tailored to AI patterns
  8. Validating dependency choices made by AI agents
  9. Enforcing license compliance in AI-suggested packages
  10. Requiring traceability from prompt to implementation
  11. Defining rollback procedures for faulty AI-generated code
  12. Creating audit artifacts for regulatory validation
Module 4. Integrating AI into Deployment Workflows
Adapt CI/CD pipelines to include AI-specific validation gates and approval steps.
12 chapters in this module
  1. Mapping current deployment stages for AI intervention points
  2. Inserting automated checks for AI-generated code blocks
  3. Configuring pipeline alerts for high-autonomy AI changes
  4. Requiring human signoff for critical path AI modifications
  5. Versioning AI model used to generate code changes
  6. Tagging deployment artifacts with AI contribution metadata
  7. Creating staging environments for AI-only change testing
  8. Setting rate limits on AI-initiated deployment requests
  9. Logging AI decision rationale in deployment records
  10. Enabling rollback triggers specific to AI-authored releases
  11. Integrating AI code metrics into deployment dashboards
  12. Documenting AI role in post-deployment incident reports
Module 5. Governance of Autonomous Code Changes
Build oversight frameworks to maintain control over AI-driven development.
12 chapters in this module
  1. Defining permissible scope for AI code ownership
  2. Establishing escalation paths for questionable AI logic
  3. Creating AI change advisory board membership rules
  4. Scheduling regular reviews of AI-authored modules
  5. Implementing change freeze policies for sensitive systems
  6. Assigning human stewards for AI-maintained components
  7. Requiring pre-approval for AI refactoring initiatives
  8. Monitoring AI code drift from original specifications
  9. Tracking technical debt introduced by AI suggestions
  10. Enforcing architecture alignment for AI-generated designs
  11. Reviewing AI’s interpretation of business requirements
  12. Auditing AI’s adherence to regulatory constraints
Module 6. Compliance in a Multi-Author Environment
Ensure regulatory standards are met when code has both human and AI authors.
12 chapters in this module
  1. Mapping GDPR implications for AI-written data handlers
  2. Ensuring SOX compliance for AI-modified financial logic
  3. Verifying HIPAA alignment in AI-generated health modules
  4. Documenting AI’s role in compliance audit trails
  5. Certifying AI code meets industry-specific regulations
  6. Training compliance staff on AI authorship nuances
  7. Updating internal policies to include AI developers
  8. Conducting compliance walkthroughs with AI-generated code
  9. Validating data provenance in AI-refactored components
  10. Requiring AI to log decision rationale for audits
  11. Aligning AI code practices with external certification bodies
  12. Preparing for regulatory inquiries about AI authorship
Module 7. Testing Strategies for AI-Generated Logic
Design test approaches that account for the unpredictability of AI-authored code.
12 chapters in this module
  1. Extending unit tests to cover AI-generated edge cases
  2. Building fuzz testing pipelines for AI-introduced functions
  3. Creating regression suites for AI-refactored modules
  4. Implementing property-based testing for AI logic
  5. Monitoring behavioral consistency across AI versions
  6. Validating AI code against known vulnerability patterns
  7. Testing AI-generated code under load conditions
  8. Assessing fault tolerance of AI-authored components
  9. Checking for unintended side effects in AI changes
  10. Running security penetration tests on AI output
  11. Evaluating AI code maintainability over time
  12. Measuring test coverage delta after AI modifications
Module 8. Incident Response with AI Developers
Prepare incident management processes for failures involving AI-authored code.
12 chapters in this module
  1. Classifying incidents caused by AI-generated logic
  2. Updating runbooks to include AI-specific failure modes
  3. Assigning triage responsibility for AI-related outages
  4. Creating AI rollback playbooks for production incidents
  5. Documenting AI decision history during incident analysis
  6. Investigating prompt inputs that led to faulty code
  7. Requiring AI to generate postmortem hypotheses
  8. Tracking recurrence of AI-introduced bugs
  9. Measuring mean time to resolve AI-caused incidents
  10. Establishing communication protocols for AI failures
  11. Conducting blameless reviews including AI agents
  12. Updating training data based on incident findings
Module 9. Team Roles in the Age of AI Engineering
Redefine responsibilities for developers, reviewers, and leads in hybrid teams.
12 chapters in this module
  1. Clarifying ownership boundaries between humans and AI
  2. Updating job descriptions to include AI oversight
  3. Defining code reviewer expectations for AI submissions
  4. Establishing AI steward roles within development teams
  5. Training leads on validating autonomous code changes
  6. Creating career paths for AI collaboration expertise
  7. Measuring team performance with AI contributors
  8. Revising sprint planning for AI-involved workflows
  9. Allocating time for AI-generated code review
  10. Setting expectations for AI refactoring participation
  11. Balancing AI autonomy with team control
  12. Documenting team decisions about AI task delegation
Module 10. Metrics for Human-AI Code Collaboration
Develop KPIs that reflect both productivity gains and risk exposure from AI use.
12 chapters in this module
  1. Tracking percentage of code authored by AI agents
  2. Measuring review time for AI-generated pull requests
  3. Calculating defect rates in AI versus human code
  4. Monitoring test pass rates for AI-introduced changes
  5. Assessing technical debt accumulation from AI suggestions
  6. Evaluating code complexity changes from AI refactoring
  7. Benchmarking deployment frequency with AI involvement
  8. Measuring incident severity linked to AI-authored logic
  9. Tracking compliance finding density in AI modules
  10. Analyzing rework required after AI code integration
  11. Calculating cost savings from AI development time
  12. Balancing velocity metrics with quality safeguards
Module 11. Documentation Standards for AI Contributions
Ensure AI-generated code is maintainable through rigorous documentation practices.
12 chapters in this module
  1. Requiring AI to generate inline code comments
  2. Validating documentation completeness for AI modules
  3. Creating runbooks for AI-maintained system components
  4. Archiving prompts used to generate critical code
  5. Storing AI model version with generated artifacts
  6. Updating architecture diagrams to reflect AI changes
  7. Maintaining changelogs that identify AI authorship
  8. Documenting assumptions made by AI during development
  9. Preserving rationale for AI design decisions
  10. Creating handover materials for AI-owned systems
  11. Enforcing API documentation for AI-generated endpoints
  12. Auditing documentation quality in AI-refactored code
Module 12. Implementing AI Code Oversight Organization-Wide
Scale governance practices across teams and systems.
12 chapters in this module
  1. Developing enterprise-wide AI code ownership policy
  2. Rolling out standardized validation checklists
  3. Training cross-functional teams on AI oversight
  4. Integrating AI governance into vendor management
  5. Scaling AI steward model across departments
  6. Creating central repository for AI code patterns
  7. Establishing AI code review board structure
  8. Conducting organization-wide AI audit readiness
  9. Aligning AI practices with enterprise architecture
  10. Reporting AI code metrics to executive leadership
  11. Iterating policy based on operational feedback
  12. Planning for next-generation AI developer capabilities

Frequently asked

Who should take this course?
IT leaders, operations managers, compliance officers, and service management leads responsible for code quality, deployment integrity, and regulatory adherence in software delivery pipelines.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover specific AI tools?
No. This course focuses on governance, oversight, and policy for AI-authored code, not on using or comparing specific AI coding tools.
Will I receive practical resources?
Yes. Each module includes downloadable templates and worked examples, plus a hand-built implementation playbook delivered at enrollment.
Can this be used for team training?
Yes. The course and materials are designed for individual study but can be adapted for team workshops and policy development.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 6-8 hours per module, designed to be completed alongside regular work over 8-12 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·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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