Your board wants quarterly risk appetite dashboards. Your third-party risk team needs vendor assessment automation for 200+ suppliers. Engineering needs compliance integration that doesn't break their deployment velocity.

You fire up Codex, spend 3 hours prompting for a "GRC dashboard," and get back 500 lines of AWS Lambda functions. Problem is, you work with SharePoint and ServiceNow, not S3 and DynamoDB.

Sound familiar?

Meanwhile, a colleague uses systematic context engineering to generate custom solutions in 20 minutes: risk appetite dashboards that integrate with existing PowerBI reports, vendor assessment workflows that work with ServiceNow ticketing, and governance frameworks that bridge Microsoft environments with business requirements.

The difference isn't technical skill. It's systematic methodology for context engineering that ensures AI generates solutions for your actual environment, not generic cloud tutorials.

Let’s learn how to Vibe Code, the GRC way!

IN PARTNERSHIP WITH

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Why It Matters 🔍

The why: the core problem this solves and why you should care

AI and vibe coding have the potential to bridge the technical gap that GRC professionals have faced for decades - if it's done the right way and understood for what it's good at versus trying to force AI into every GRC challenge.

Where AI Excels in GRC

Sifting Through Vast Amounts of Unstructured Data: Analyzing vendor contracts, policy documents, regulatory guidance across multiple frameworks and jurisdictions

Understanding Context Exceptionally Well: Connecting governance requirements with business processes, translating technical findings into board-appropriate language

Spinning Up Quick Solutions: Generating custom scripts, dashboards, and workflows that integrate with your specific tool stack

Natural Language to Code Transformation: Converting business requirements into technical implementations that work with your actual systems

Critical Limitations and Boundaries

AI Does Not Replace Developers: When your GRC program requires complex systems with ETL processes, centralised data pipelines, integration across multiple frameworks, and coordination with enterprise tools - you need proper GRC engineering, not just AI prompting skills.

Enterprise Complexity Requires Real Engineering: Building systems that communicate with different platforms (some with APIs, some legacy, some private cloud, some completely custom) where no reliable documentation exists requires architectural thinking that AI cannot provide.

Context Matters More Than Code: The practitioners who understand when to use vibe coding for rapid prototyping versus when to invest in proper engineering for production systems will create the most value.

Strategic Framework 🧩

The what: The conceptual approach broken down into 3 main principles

Context > Prompting: The Foundation

Traditional AI prompting fails because it lacks systematic context about your organisational reality. You get generic solutions that require extensive modification to fit your actual governance structures, risk management approaches, and stakeholder ecosystems.

Context engineering is becoming the new frontier in AI application - moving beyond simple prompting to systematic context management that drives consistently relevant outputs. LangChain has a super insightful blog post about context engineering that demonstrates how persistent, well-structured context dramatically improves AI solution quality.

GRC Application: Your baseline context captures not just technical stack, but governance structures, stakeholder relationships, risk methodologies, and business processes. Every generated solution understands your organisational reality from the start so you don’t end up prompting for hours.

AI Use Cases Beyond Compliance Automation

Cloud compliance automation gets attention because it's low-hanging fruit with a very deterministic API-driven model. It has significant limitations in enterprise settings where access control to cloud resources is tightly managed, where a CSPM tool has been onboarded to limit direct access to public cloud endpoints or in environments which aren’t 100% public cloud-native (which is more common than we think).

Also, because of that, if you work at a smaller company, maybe as the first GRC hire, GRC automation tools are a no-brainer - potentially 1/10th or 1/20th the cost of building custom solutions. The vendor platforms provide every piece of evidence you need directly because they leverage the same straightforward APIs you would use when building a custom solution.

You can still work on your skills but if it derails the speed at which your company needs to be compliant, you might be in trouble.

Of course there’s a ton of super cool GRC Engineering use-cases outside of cloud compliance automation. Risk quantification, TPRM reviews, markdown policy management, automatic gap assessments, common control framework mappings, etc.

The limits are your imagination and your bank account!

Avoiding Technical Debt and Implementation Pitfalls

If you're already working with engineering teams, creating technical debt through poor AI-generated code can damage your credibility and stakeholder relationships.

Professional Context Awareness: Understanding how your team actually uses Terraform in production, the specific trade-offs they've made, how complex your gitlab-ci.yml files are for specific pipelines - this context prevents generating solutions that contradict established practices.

Quality Standards: Generated code must meet your organisation's actual development standards, not tutorial quality. If your team uses specific naming conventions, security patterns, or architectural approaches, your AI solutions must respect these constraints.

Integration Complexity: Your governance automation must work with existing business processes, not create parallel systems that duplicate effort or create coordination overhead which you’re the only one to maintain.

Stakeholder Trust: Engineers judge GRC professionals by the quality of technical solutions they propose. Poor AI-generated code that ignores established practices undermines your credibility faster than no technical solution at all (especially if they have to leverage/own it later)

Production Reality: Building systems that span multiple frameworks, integrate with enterprise tools, and coordinate across business units requires systematic architecture thinking that AI supports but cannot replace.

Not all automation is created equal.

Most GRC automation tools crumble at enterprise scale. Cypago is the only Cyber GRC automation tool that was purpose-built for the complexity and scale of large orgs - hybrid environments, custom workflows, and overlapping frameworks. Fast to deploy, flexible where it matters.

Ready to give it a spin?

Execution Blueprint 🛠️

The how: 3 practical steps to put this strategy into action at your organisation

The Universal GRC Vibe Coding Framework

In my opinion, I see three main application patterns where Vibe Coding serves GRC very well. They represent different urgency levels and business contexts where vibe coding creates the most value for GRC practitioners. Each pattern addresses a specific type of challenge you face in enterprise environments.

Quick Problem Solving handles immediate stakeholder requests and GRC gaps that need rapid demonstration of value.

Solution Prototyping focuses on building stakeholder buy-in and testing business value before major investment.

Custom Learning generates professional development that matches your actual job environment and current skills rather than generic tutorials.

All three patterns start with the same comprehensive baseline context, ensuring every generated solution fits your organisational reality. The key is choosing the right pattern for your specific situation and understanding when each approach creates the most strategic value.

Your Baseline Context Template:

# GRC Vibe Coding Baseline Context

## Personal & Technical Profile
Role: [YOUR_POSITION - e.g., "Head of Risk", "Governance Manager", "TPRM Lead"]
Technical Skill Level: [TECH_PROFICIENCY - e.g., "Advanced Excel/PowerBI", "Intermediate Python", "SQL comfortable", "Non-technical"]
Coding Experience: [PROGRAMMING_BACKGROUND - e.g., "Never coded", "Basic scripting", "Former developer", "Self-taught automation"]
Strongest Technical Areas: [TECH_STRENGTHS - e.g., "Data analysis", "Process automation", "Report building", "Stakeholder communication"]
Technical Learning Gaps: [AREAS_TO_DEVELOP - e.g., "API integration", "Infrastructure concepts", "Database queries", "CI/CD workflows"]
Preferred Learning Style: [LEARNING_APPROACH - e.g., "Hands-on practice", "Documentation-heavy", "Visual examples", "Step-by-step guidance"]_

## Professional & Organisational Profile
Team Structure: [YOUR_CONTEXT - e.g., "5-person GRC team", "Matrix organisation"]
Industry: [YOUR_SECTOR - e.g., "Financial Services", "Healthcare", "Manufacturing"]
Company Scale: [SIZE_CONTEXT - e.g., "2,000-person fintech", "Global enterprise"]
Years in GRC: [EXPERIENCE_LEVEL - e.g., "2-5 years", "10+ years veteran", "Career changer"]_

## Governance & Stakeholder Environment
Board Structure: [GOVERNANCE - e.g., "Quarterly risk committee", "Audit committee"]
Stakeholder Ecosystem: [STAKEHOLDERS - e.g., "CISO, CFO, Legal", "Business unit heads"]
Decision Authority: [AUTHORITY - e.g., "Centralised risk", "Distributed governance"]
Communication Patterns: [COMMS - e.g., "Monthly executive briefings", "Quarterly board reports"]

## Risk & Vendor Management
Risk Methodology: [RISK_APPROACH - e.g., "Quantitative analysis", "Qualitative scoring"]
Vendor Ecosystem: [TPRM_CONTEXT - e.g., "200+ vendors", "Critical supplier focus"]
Assessment Tools: [VENDOR_TOOLS - e.g., "ServiceNow", "Custom spreadsheets", "Archer"]
Regulatory Environment: [REGULATION - e.g., "Highly regulated", "Emerging compliance"]_

## Business Systems & Technology
Platform Environment: [TECH_STACK - e.g., "Microsoft ecosystem", "Google Workspace", "Hybrid"]
Document Systems: [DOCS - e.g., "SharePoint", "Confluence", "Box"]
Workflow Tools: [WORKFLOW - e.g., "Teams", "Slack", "ServiceNow", "Manual processes"]
Reporting Systems: [REPORTING - e.g., "PowerBI", "Tableau", "Excel dashboards"]_

## Current Organisational Challenges
[SPECIFIC_PAIN_POINTS - e.g., "Manual vendor reviews", "Inconsistent risk scoring", "Board reporting gaps"]

Implementation: Save as persistent context in your preferred AI environment (could be in memory for ChatGPT, Claude.md file, etc.). Reference this baseline for every GRC vibe coding session to ensure consistent, contextualised solutions.

Application Pattern 1: Quick Problem Solving

When to Use: Immediate governance gaps, stakeholder requests on TPRM, or compliance issues requiring rapid solution development that demonstrates value whilst buying time for long-term solutions.

Sample Quick Application Prompt:

Reference my GRC Vibe Coding Baseline Context, then:

I need to address [SPECIFIC_GRC_CHALLENGE] for [STAKEHOLDER_GROUP] within [TIMEFRAME].

Context: [BRIEF_SITUATION_DESCRIPTION]
Required Output: [DELIVERABLE_TYPE - e.g., "Board dashboard", "Risk assessment", "Policy document"]
Success Criteria: [WHAT_GOOD_LOOKS_LIKE]

Generate immediate solution that:
- Matches my technical skill level and learning style
- Works with my actual business systems and workflow tools
- Fits my governance structure and stakeholder communication patterns
- Addresses my regulatory environment and compliance requirements
- Can be implemented given my team structure and current capabilities

Focus on demonstrating value quickly while respecting my organisational constraints.

Application Pattern 2: Solution Prototyping

When to Use: Business process integration, policy exception automation or gap assessments requiring working demonstration before formal investment in enterprise development.

Sample Quick Application Prompt:

Reference my GRC Vibe Coding Baseline Context, then:

Design prototype for [GRC_PROCESS_OR_WORKFLOW] that needs stakeholder buy-in.

Current State: [EXISTING_PROCESS_DESCRIPTION]
Target Stakeholders: [WHO_NEEDS_TO_BE_CONVINCED]
Business Case: [WHY_THIS_MATTERS_TO_ORGANISATION]
Integration Requirements: [SYSTEMS_THAT_NEED_TO_CONNECT]

Generate working prototype that:
- Demonstrates clear business value to my stakeholder ecosystem
- Works within my decision authority and governance structure
- Integrates with my existing business systems and technology stack
- Matches my team's technical capabilities and can be maintained
- Addresses my specific industry and regulatory requirements
- Fits my communication patterns and reporting cycles

Provide architectural approach that builds confidence for formal investment.

Next Week: Architectural templates, stakeholder validation frameworks, and business process integration patterns.

Application Pattern 3: Custom Learning

When to Use: Professional development that matches your actual tech stack, GRC challenges, control set and organisational context and builds learning opportunities that directly relate to improvements in your day job!

Sample Quick Application Prompt:

Reference my GRC Vibe Coding Baseline Context, then:

Create comprehensive learning experience for [SPECIFIC_GRC_CAPABILITY].

Learning Objective: [SKILL_OR_KNOWLEDGE_TO_BUILD]
Application Context: [HOW_ILL_USE_THIS_IN_MY_ROLE]
Current Starting Point: [MY_EXISTING_KNOWLEDGE_LEVEL]
Time Investment: [REALISTIC_LEARNING_COMMITMENT]

Generate learning lab that:
- Matches my technical skill level and preferred learning style
- Uses my actual business systems and technology environment
- Addresses challenges specific to my industry and company scale
- Incorporates my governance structure and stakeholder dynamics
- Works with my current vendor ecosystem and assessment tools
- Builds capability that applies to my specific organisational challenges
- Progresses appropriately given my GRC experience level

Design for practical skill building that enhances my actual job performance.

Next week we will dive deeper into the first application on quick problem solving,

Content Queue 📖

The learn: This week's resource to dive deeper on the topic

As we’re diving deeper into Vibe Coding for GRC use-cases, understanding how to speak to a coding agent is like learning a new language, completely different way of interacting.

This article, though specific for Claude Code, gives amazing tips that are applicable across platforms to get more out of your assisted coding sessions and ensure the output is on-par with the standards your company has.

Great reading!

That’s all for this week’s issue, folks!

If you enjoyed it, you might also enjoy:

• My spicier takes on LinkedIn [/in/ayoubfandi]

• Listening to the GRC Engineering Podcast 

  • Spotify

  • Youtube

  • Apple Podcasts

See you next week!