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| 1 | +--- |
| 2 | +title: Governed AI SDLC - Executive Summary |
| 3 | +description: Two-page executive summary of the Governed AI SDLC Enterprise Adoption Plan for leadership stakeholders |
| 4 | +author: Platform AI Team |
| 5 | +ms.date: 2026-04-23 |
| 6 | +ms.topic: overview |
| 7 | +--- |
| 8 | + |
| 9 | +## Governed AI SDLC - Executive Summary |
| 10 | + |
| 11 | +> **Full plan:** [Governed AI SDLC - Enterprise Adoption Plan](20-governed-ai-sdlc-plan.md) |
| 12 | +
|
| 13 | +### The opportunity |
| 14 | + |
| 15 | +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%. |
| 16 | + |
| 17 | +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. |
| 18 | + |
| 19 | +### North-star outcomes (12-18 months) |
| 20 | + |
| 21 | +| Outcome | Target | |
| 22 | +|---|---| |
| 23 | +| Weekly active AI-agent usage across eligible developers | ≥ 80% | |
| 24 | +| Lead-time-for-change improvement on pilot services | Measurable improvement (baselined in Phase 0; industry benchmarks suggest 20-40%) | |
| 25 | +| AI-generated code traceable and policy-checked pre-merge | 100% | |
| 26 | +| AI-attributable incident MTTR (safety metric, distinct from DORA MTTR) | < 4 hours | |
| 27 | +| P1 incidents from ungoverned AI output | Target zero | |
| 28 | + |
| 29 | +### What we are building |
| 30 | + |
| 31 | +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. |
| 32 | + |
| 33 | +**Three-plane architecture** (proven pattern from Kubernetes, Azure, and Microsoft Foundry): |
| 34 | + |
| 35 | +| Plane | What it does | Owner | |
| 36 | +|---|---|---| |
| 37 | +| **Control** | Rules, registries, governance decisions, kill switches | Governance Board + Platform | |
| 38 | +| **Agent** | Runtime execution — reasoning loops, tool calls, orchestration | Platform (Agent Engineering) | |
| 39 | +| **Data/Tool** | What agents touch — repos, APIs, knowledge indexes, telemetry | Product squads + Platform | |
| 40 | + |
| 41 | +### Phased rollout |
| 42 | + |
| 43 | +| Phase | Duration | Scope | Key milestone | |
| 44 | +|---|---|---|---| |
| 45 | +| **0 - Foundations** | 4-6 weeks | Platform Team, governance, baselines | AUP + RAI Standard published | |
| 46 | +| **1 - Pilot** | 8-12 weeks | 2-3 squads (≤ 50 devs), 3 core agents | Eval harness + red-team pass | |
| 47 | +| **2 - Expand** | 12-16 weeks | ≤ 250 devs, multiple BUs, +7 agents (10 cumulative) | Self-service catalog live | |
| 48 | +| **3 - Scale** | 12-20 weeks | All ~1,000 devs, full 15-agent catalog | T3/T4 HITL gates operational | |
| 49 | +| **4 - Optimize** | Ongoing | Multi-agent workflows, continuous eval | External benchmark maturity | |
| 50 | + |
| 51 | +Each phase has **measurable graduation gates** and **rollback triggers** (detailed in the full plan, section 10). |
| 52 | + |
| 53 | +### Governance at a glance |
| 54 | + |
| 55 | +* **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. |
| 56 | +* **Policy-as-code in CI:** Schema validation, MCP tool allowlists, secret/PII scanning, license checks — all enforced automatically. |
| 57 | +* **Audit & observability:** Unified audit log (GitHub + MCP + model provider) streamed to SIEM. Cost caps with hard limits per org/cost center. |
| 58 | +* **Responsible AI:** Model cards for each agent, bias/fairness checks, transparency labels on every AI contribution, developer override path. |
| 59 | + |
| 60 | +### Risk posture |
| 61 | + |
| 62 | +| Risk | Mitigation | |
| 63 | +|---|---| |
| 64 | +| IP leakage via prompts | DLP on prompts, enterprise-tenant models | |
| 65 | +| Over-reliance / skill atrophy | Pair programming norms, code-review expectations | |
| 66 | +| Cost sprawl | Per-BU budgets, token quotas, FinOps Agent, hard caps | |
| 67 | +| Shadow AI tools | Approved catalog with easy on-ramp, egress controls | |
| 68 | + |
| 69 | +### Investment required |
| 70 | + |
| 71 | +* **AI SDLC Platform Team:** ~12-18 FTE (Agent Engineering, Prompt/Eval, MLOps, Security, DevEx, Product) |
| 72 | +* **AI Champions Network:** ~40 champions (1 per ~25 devs, part-time) |
| 73 | +* **Licensing:** GitHub Copilot Enterprise |
| 74 | +* **ROI formula:** (time saved × loaded cost) − (platform + license + compute) |
| 75 | + |
| 76 | +### Immediate asks (first 30-60 days) |
| 77 | + |
| 78 | +1. Charter the Platform Team and Governance Board; name accountable exec sponsor |
| 79 | +2. Enable Copilot Enterprise tenant policies, audit log export, and Metrics API |
| 80 | +3. Publish v1 of AI Acceptable Use Policy, Responsible AI Standard, and risk tiering |
| 81 | +4. Select 2 pilot squads and define success criteria |
| 82 | +5. Launch Champions cohort #1 and baseline DORA/SPACE survey |
| 83 | + |
| 84 | +--- |
| 85 | + |
| 86 | +*This summary is derived from the full [Governed AI SDLC - Enterprise Adoption Plan](20-governed-ai-sdlc-plan.md), which includes detailed architecture, agent catalog, governance controls, metrics framework, research sources, and independent validation findings.* |
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