I’m having the same conversation with CTOs every week. The CEO is pushing hard for AI initiatives after seeing competitors gain market advantage, but the CISO is raising legitimate concerns about data governance and audit requirements.
“How do we move forward without introducing massive risk?” they ask. “We’ve got a pretty good grasp of building cloud-native systems, but AI feels like uncharted territory.”
Your Current Landing Zone Remains Essential
If you’ve got an existing landing zone running with AWS Control Tower, a multi-account setup, and established governance, you’re sitting on solid foundations. The fundamentals haven’t changed: you still need account separation, network controls, identity management, and all the governance components that keep your CISO happy.

Landing zones are now a well established concept and are table stakes for any organisation making serious use of the cloud. They remain absolutely essential for AI initiatives. The difference is that AI workloads introduce new challenges that require extending this proven pattern rather than replacing it.
The Governance Challenge
Here’s what we’re seeing: organisations launch AI pilots with great enthusiasm, achieve impressive proof-of-concept results, then hit a wall when trying to move to production. The biggest blocker isn’t often technical capability or budget. It’s confidence.
Leadership needs assurance that the system won’t hallucinate in customer-facing scenarios, won’t inadvertently expose sensitive data, and won’t generate inappropriate content that damages the brand. Without visibility into what the AI is actually doing and proper governance controls, that confidence simply doesn’t exist.
AI projects are gathering dust because the pilot team couldn’t demonstrate to stakeholders that the system was behaving reliably. The technology works, but the governance framework doesn’t yet exist to support a production deployment.
Why AI Changes the Governance Game
Traditional workloads are largely predictable. You know what your application will do, what data it will access, and how it will behave. AI workloads are fundamentally different. Every prompt interaction is unique, every model response is generated rather than retrieved, and the potential for unexpected outputs is inherent to the technology.
Traditional security monitoring focuses on infrastructure and application behaviour, but can’t detect when an AI model generates biased outputs, hallucinates false information, or exhibits unexpected patterns in decision-making. Standard monitoring tells you about infrastructure performance but nothing about content quality, fairness across demographic groups, or the accuracy of generated responses. Traditional security reviews work for static applications but struggle with systems that generate novel responses to user inputs.
This is where an AI landing zone becomes crucial. It’s essentially an enhanced governance layer that extends your existing architecture with AI-specific controls and visibility.
The Extension Approach
An AI landing zone provides the additional governance layer your existing architecture needs. In practice, this means several key characteristics:
Governance and Safety Controls: Real-time content filtering and PII redaction through Bedrock Guardrails, automatic prompt and response logging for audit trails, granular model access controls that specify who can use which models for what purposes, and cost controls with budget alerts per project or team.
Observability and Metrics: Token consumption tracking with cost attribution, model performance metrics including latency and error rates, guardrail violation reporting, usage patterns across teams and projects, all integrated with your existing monitoring stack.
Developer Experience: Rapid sandbox provisioning to enable quick feedback loops for model testing and validation. Pre-built Infrastructure as Code templates for common patterns like RAG pipelines and agent workflows, integrated development environments with AI coding assistance, and streamlined promotion paths from development to production.
Infrastructure Integration: Seamless integration with your existing landing zone deployment and integration with current identity providers, SSO systems, etc.
The key insight is that these capabilities extend your existing foundation. Your account structure gains new capabilities without structural changes. Your security team gets AI-specific controls without learning entirely new systems. Your operational processes remain broadly familiar whilst handling new requirements.
Building Confidence Through Visibility
After implementing a number of AI projects across multiple clients, we’ve found several components are essential for moving beyond pilot stage:
Real-time content guardrails that automatically enforce policies to every prompt and response.
Comprehensive observability that shows exactly what the AI is doing, how it’s performing, and what it’s costing.
Robust testing frameworks that can validate AI system behaviour before production deployment. Traditional unit tests can’t verify that your model won’t exhibit bias against certain demographic groups or generate plausible-sounding but incorrect information.
Audit trails that demonstrate compliance and enable continuous improvement of model behaviour over time.
Properly Testing AI Systems
The testing aspect is where many organisations stumble. Your test suites need to measure the same bias indicators, hallucination rates, and content quality metrics that you’ll be monitoring in production. Without this continuity between testing and production metrics, you’re essentially deploying blind.
When executives can see these metrics, when compliance teams can verify proper controls are working, when finance can track costs accurately, that’s when AI projects gain the confidence needed for production deployment.
Moving Forward Thoughtfully
The organisations succeeding with AI understand that good governance accelerates innovation rather than hindering it. They build on proven foundations and maintain the visibility needed for confident decision-making.
We’re an AWS Generative AI Competency Partner with exclusive access to AWS co-investment opportunities for qualifying projects. Our AI Foundations solution extends existing landing zones with the governance and visibility needed to move AI projects from pilot to production. Let’s discuss your AI plans and explore available co-investment opportunities.
This blog is written exclusively by The Scale Factory team. We do not accept external contributions.