No approved matrix for coding tools or models
Procurement and security cannot distinguish sanctioned platforms from shadow usage, leaving licensing, data residency, and IP questions unresolved.
Governance & Security
Developer AI Governance
Operationalize governance for AI coding agents — approved tools and models, repository classification, source-code handling rules, MCP policy, human-review requirements, and incident handling that security and engineering can both own.
Approved Tool MatrixRepository ClassificationMCP & Command PolicyHuman Review StandardsAudit-Ready Controls
Aligned to Copilot, Codex, Claude Code, Cursor, and enterprise MCP governance
Why This Matters Now
The challenges that bring enterprise teams to this conversation
Repository sensitivity is not mapped to agent permissions
Agents gain write and command access on production-critical or regulated code without classification-based controls or exception handling.
MCP and automation lack audit and human-review rules
Tool calls and agent-authored changes move into production without logging, approval gates, or clear incident response ownership.
Strategic context: Developer AI governance is the difference between measured productivity and unmanaged risk — policy, tooling controls, and team accountability must ship together with every agent rollout.
Capability Coverage
What the program covers
Approved Tool & Model Matrix
Define which AI coding platforms and models are sanctioned by role, environment, and data classification — with clear exception paths.
Repository Classification
Classify repositories by sensitivity and map agent capabilities, content exclusions, and write permissions to each tier.
Source-Code Handling Policy
Codify rules for prompts, instruction files, secrets, customer data, and third-party model training postures across tools.
MCP & Command-Execution Policy
Govern MCP servers and agent command execution with authentication, authorization, audit, and approval requirements.
Human-Review & Audit Standards
Set review requirements for agent-authored pull requests, security-sensitive paths, and audit evidence for regulated workloads.
Incident & Exception Handling
Define escalation, exception grants, and incident response when developer AI tools misuse data, tools, or privileged access.
Delivery Approach
How we deliver this
01
Assess
Governance Gap Review
Inventory current coding-tool usage, policy gaps, repository risk, MCP exposure, and audit readiness against enterprise standards.
02
Design
Policy & Control Framework
Design the tool/model matrix, classification scheme, MCP and command policies, review standards, and accountability model.
03
Enable
Controls & Team Training
Configure platform controls, publish playbooks, and train engineering, security, and platform owners on the operating rules.
04
Adopt
Live Governance Cadence
Activate exception workflows, audit reporting, incident handling, and a recurring review cadence for continuous compliance.
Capability Programs
Programs for this area
Proof & Perspectives
Implementation evidence and strategic context
Representative Healthcare Product Team
Product and engineering teams needed to evaluate AI and generative AI use cases while building implementation capability in a governed enterprise setting.
Azure OpenAIResponsible AIEnterprise Prototyping
Read engagement detailsStrategy6 min read
AI Readiness: The Work That Happens Before You Touch a Model
Organisations that struggle to get value from enterprise AI investments almost never fail because of model capability. They fail because the data is not ready, the teams are not aligned, and the governance layer does not exist. This post outlines the readiness work that precedes meaningful AI adoption.
AI StrategyEnterprise AIData ReadinessGovernance
Read insightReady to Begin
Start your Developer AI Governance program
Work with our team to design an enablement program matched to your team's readiness, platform priorities, and delivery timeline.
Approved Tool Matrix
Repository Classification
MCP & Command Policy
Human Review Standards