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title Governed AI SDLC - Executive Summary
description Two-page executive summary of the Governed AI SDLC Enterprise Adoption Plan for leadership stakeholders
author Platform AI Team
ms.date 2026-04-23
ms.topic overview

Governed AI SDLC - Executive Summary

Full plan: Governed AI SDLC - Enterprise Adoption Plan

The opportunity

AI-assisted development is no longer experimental. GitHub's own engineering team uses Copilot to generate PRs across their core platform — from typo sweeps (161 fixes in one PR) to new REST endpoints and database migrations. The value is "not starting from zero": letting AI handle the tedious 80% so engineers focus on the critical 20%.

We will embed a governed fleet of AI agents into every stage of our SDLC for ~1,000 developers, accelerating delivery while enforcing security, compliance, and Responsible AI.

North-star outcomes (12-18 months)

Outcome Target
Weekly active AI-agent usage across eligible developers ≥ 80%
Lead-time-for-change improvement on pilot services Measurable improvement (baselined in Phase 0; industry benchmarks suggest 20-40%)
AI-generated code traceable and policy-checked pre-merge 100%
AI-attributable incident MTTR (safety metric, distinct from DORA MTTR) < 4 hours
P1 incidents from ungoverned AI output Target zero

What we are building

A central AI SDLC Platform Team that productizes an Agent Factory — a governed catalog of 15 AI agents covering ideation through operations. Developers consume these agents via golden paths on our Internal Developer Platform. All usage is policy-gated, observable, and measured against DORA/SPACE + AI-specific KPIs.

Three-plane architecture (proven pattern from Kubernetes, Azure, and Microsoft Foundry):

Plane What it does Owner
Control Rules, registries, governance decisions, kill switches Governance Board + Platform
Agent Runtime execution — reasoning loops, tool calls, orchestration Platform (Agent Engineering)
Data/Tool What agents touch — repos, APIs, knowledge indexes, telemetry Product squads + Platform

Phased rollout

Phase Duration Scope Key milestone
0 - Foundations 4-6 weeks Platform Team, governance, baselines AUP + RAI Standard published
1 - Pilot 8-12 weeks 2-3 squads (≤ 50 devs), 3 core agents Eval harness + red-team pass
2 - Expand 12-16 weeks ≤ 250 devs, multiple BUs, +7 agents (10 cumulative) Self-service catalog live
3 - Scale 12-20 weeks All ~1,000 devs, full 15-agent catalog T3/T4 HITL gates operational
4 - Optimize Ongoing Multi-agent workflows, continuous eval External benchmark maturity

Each phase has measurable graduation gates and rollback triggers (detailed in the full plan, section 10).

Governance at a glance

  • Risk tiering (T1-T4): Every agent and use case is classified. T1 (code suggestions) needs baseline policies. T4 (regulated data, safety-critical) requires Board approval, isolated tenancy, and full provenance.
  • Policy-as-code in CI: Schema validation, MCP tool allowlists, secret/PII scanning, license checks — all enforced automatically.
  • Audit & observability: Unified audit log (GitHub + MCP + model provider) streamed to SIEM. Cost caps with hard limits per org/cost center.
  • Responsible AI: Model cards for each agent, bias/fairness checks, transparency labels on every AI contribution, developer override path.

Risk posture

Risk Mitigation
IP leakage via prompts DLP on prompts, enterprise-tenant models
Over-reliance / skill atrophy Pair programming norms, code-review expectations
Cost sprawl Per-BU budgets, token quotas, FinOps Agent, hard caps
Shadow AI tools Approved catalog with easy on-ramp, egress controls

Investment required

  • AI SDLC Platform Team: ~12-18 FTE (Agent Engineering, Prompt/Eval, MLOps, Security, DevEx, Product)
  • AI Champions Network: ~40 champions (1 per ~25 devs, part-time)
  • Licensing: GitHub Copilot Enterprise
  • ROI formula: (time saved × loaded cost) − (platform + license + compute)

Immediate asks (first 30-60 days)

  1. Charter the Platform Team and Governance Board; name accountable exec sponsor
  2. Enable Copilot Enterprise tenant policies, audit log export, and Metrics API
  3. Publish v1 of AI Acceptable Use Policy, Responsible AI Standard, and risk tiering
  4. Select 2 pilot squads and define success criteria
  5. Launch Champions cohort #1 and baseline DORA/SPACE survey

This summary is derived from the full Governed AI SDLC - Enterprise Adoption Plan, which includes detailed architecture, agent catalog, governance controls, metrics framework, research sources, and independent validation findings.