AI governance for engineering teams.
Practical frameworks for AI compliance, SOC 2 readiness, data handling policies, and audit-ready AI operations. Built for engineering leaders who need governance that works without slowing teams down.
AI governance isn't a compliance checkbox. It's the operational infrastructure that lets engineering teams use AI tools confidently, pass audits without scrambling, and maintain customer trust as your AI usage scales.
Most governance frameworks fail because they're designed by compliance teams in isolation, disconnected from how engineers actually work. The result is either a document that nobody follows or restrictions so onerous that teams route around them.
VG Tech Consulting takes an engineering-led approach to AI governance. We design frameworks that integrate into existing delivery workflows. Access controls, audit trails, data handling policies, and escalation paths that work because they're built with the engineering team, not imposed on them.
AI adoption checklist before rollout
Twelve questions every CTO should answer before deploying AI tools to engineering teams. Covers tooling, risk, and evidence.
ReadAI coding agents and compliance: how to choose
A practical framework for evaluating AI coding assistants against your data handling, audit, and enterprise requirements.
ReadIndividual vs corporate AI plans: what changes
The shift from personal ChatGPT to a company-wide AI subscription is not just pricing. It's governance, IP, and team trust.
ReadEngineering AI Adoption
A software company wanted to adopt AI across engineering in a practical way, but needed the right workflows, training, governance, and rollout model to make it useful and compliant.
Read case studyEnterprise AI architectureGlobal professional services firm · 5,000+ employees · multi-jurisdictionEnterprise AI Platform
A large enterprise needed a secure, governed way for employees to use LLMs internally without exposing sensitive information or relying on uncontrolled public tools.
Read case studyWhat is an AI governance framework?
An AI governance framework is a structured set of policies, controls, and processes that define how an organisation uses AI tools responsibly. It covers data handling (what data can be sent to AI models), access controls (who can use which tools), audit trails (logging what AI does), and compliance documentation (evidence for auditors and enterprise customers).
Why does SOC 2 matter for AI tooling?
If you sell to enterprises, your customers will ask about your AI practices during procurement and security reviews. SOC 2 compliance provides the evidence that your AI operations meet industry security standards, covering data residency, access controls, monitoring, and incident response for AI-generated outputs.
How do you evaluate AI coding agents for compliance?
We evaluate AI coding agents across six dimensions: data residency and flow, access scope and permissions, output provenance and traceability, audit logging, policy enforcement capabilities, and vendor risk management. This maps directly to what enterprise customers and auditors ask about.
What's the difference between personal and corporate AI plans from a governance perspective?
Personal AI accounts give organisations zero visibility into data exposure, no contractual protections, and no audit trail. Corporate plans provide data processing agreements, admin controls, centralised logging, and contractual commitments about data retention and training opt-outs. The minimum requirements for responsible AI use in professional settings.