VELOCITY GOVERNANCE

Your teams are shipping faster with AI.
Is your governance keeping up?

Velocity Governance detects AI-accelerated teams and scores them against the 10 SDLC standards that matter most when models are in the loop. New risks surface. Existing gaps become critical.

See the Platform View the Framework →

What happens when practices don't keep pace with AI velocity

These are not new risks — they are the predictable consequences of existing governance gaps under AI-accelerated development.

⚡

Non-determinism in production

Same input, different outputs. Weak CI gating and test discipline — manageable before — can no longer catch what reaches production. AI amplifies whatever testing gaps were already there.

💉

Prompt injection

External content manipulating model behaviour through input. SAST tools built for deterministic code miss this entirely. Teams with weak security analysis practices have no automated gate against it.

🌊

Model drift without deployment

Behaviour changes without a PR merged or a release cut. Teams with weak rollback capability and untested feature flags have no response plan — the gap was always there, AI made it consequential.

🔍

Non-auditable decision paths

Model selection, provider changes, prompt strategy — these are architectural decisions. Teams that weren't writing ADRs before won't start under AI pressure. The audit trail gap compounds with every adoption decision made.

Automatically detects AI-active repos

No manual tagging. Concordance scans four detection surfaces across your connected repositories.

SDK Dependencies

openai, anthropic, langchain, huggingface and 56 others across npm, PyPI, Go, Maven, Cargo

AI Tooling Config

.cursor/, .coderabbit.yml, aider.conf, copilot-instructions.md

CI API Keys

OPENAI_API_KEY, ANTHROPIC_API_KEY, and 13 other LLM provider env vars in workflow YAML

Prompt Artefacts

/prompts/, system_prompt.txt, *.prompt.md, AGENTS.md committed to the repo

10 standards. Each one amplified by AI.

The 10 Concordance standards where AI integration raises the stakes. Each one maps to a specific failure mode that emerges when models are in the loop.

IDStandardPhaseWithout governance…

2.2Architecture Decision RecordsDesignNo audit trail for AI adoption decisions

2.6Dependency ManagementDesignSilent LLM SDK drift and supply chain exposure

3.1Branch ProtectionDevelopmentAI-generated code bypassing review

3.2PR Review QualityDevelopmentRubber-stamp reviews at AI velocity

3.6Code OwnershipDevelopmentNo accountability when model behaviour changes

3.9Secrets ManagementDevelopmentLLM API keys exposed in CI

4.1CI PipelineTestingNo automated gate on AI-generated code

4.6Security AnalysisTestingPrompt injection patterns undetected

5.7Rollback CapabilityReleaseNo response plan when model behaviour drifts

5.8Feature FlaggingReleaseNo kill-switch without a full deploy

View full framework with risk detail →

From scan to governance action in minutes

1

Detect

Concordance scans your connected repos for SDK dependencies, config files, CI API keys, and prompt artefacts. AI-accelerated teams are identified automatically — no manual tagging.

2

Score

Each AI-accelerated team is scored across 10 governance standards using the same scan data that powers the rest of Concordance. No additional setup required.

3

Surface

Gaps are ranked by consequence — Critical (below 2.0) and High (below 2.5). Each team receives specific action items, not generic advice.

4

Track

Re-scan next sprint. Governance improvement is measurable. Ship faster and govern better — the two are not in conflict.

See which of your teams are exposed

Connect your SCM and Concordance will detect AI-active repos, score governance posture, and surface action items — automatically.