Enterprise AI Governance for SHVA
Scaling AI-Assisted Development with Enterprise Controls
Industry
Payment Infrastructure / Financial Technology
Services
Technology
GitHub Copilot, Reusable Skills, Agents, Enterprise Repositories
TL;DR
- 1.Built a governance framework for expanding AI-assisted development across additional engineering teams while maintaining uniform security and quality rules.
- 2.Defined policies for acceptable AI use, information and code security, code review, production promotion, team-lead accountability, and value measurement.
- 3.Designed the next phase as an active software control layer spanning specification, development, testing, delivery, and change management, with approximately five core controls to be finalized with the client.
- 4.Set a program target of controlled AI-assisted work for 15 developers, with a proposed 12-week path from control definition to conclusions.
The Challenge
SHVA sought to expand the use of AI tools across development teams, improve engineering productivity, and make the capability available to additional groups.
In a sensitive payment-infrastructure environment, broader AI use by developers required consistent rules for what information and code may be shared, how AI-assisted code is reviewed, how changes reach production, who is accountable, and how business value is measured.
- 1
Inconsistent Team Practices
Without an enterprise framework, each team could develop its own rules for AI-assisted coding, creating uneven security, review, and documentation standards.
- 2
Secure Path to Production
AI-generated or AI-assisted code needed clear gates for security, code control, testing, approval, and controlled promotion into production.
- 3
Measuring Real Value
The organization needed ROI and KPI measures that connect AI adoption to actual productivity change rather than tool usage alone.
The Solution
Observe the Real Development Workflow
Dofinity.AI conducted observation and deep-dive sessions with the CTO, infrastructure specialists, team leads, and developers to map how work was actually performed and where AI introduced practical risks.
Define a Shared Development Policy
A principles document was created covering AI development policy, allowed and prohibited practices for information security, code promotion to production, code review, and ROI / KPI measurement. The rollout also included guided work with GitHub Copilot and alignment of engineering practices.
Operationalize Continuous Governance
The next phase was defined as an active software control layer through the full development lifecycle: specification, development, testing, delivery, and change management. The program includes roughly five core controls to be defined with SHVA, together with reusable enterprise assets such as Skills, Agents, and Repositories. Control is designed around gates throughout the lifecycle rather than a one-time check at the end.
Success
The framework gives SHVA a way to expand AI-assisted development without asking every team to invent its own standards. Management gains a clearer view of adoption and control status, team leads receive consistent criteria, and developers gain an approved working environment with reusable components.
The program is designed to support broader AI adoption while preserving security, code quality, documentation, and measurable business value. The stated target is controlled AI-assisted work for 15 developers through a proposed 12-week implementation path.
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