# Zenable - AI-Native SDLC Governance & Guardrails

> Production-ready tooling AI-accelerated development: correctness, conformance, observability in the SDLC.

_Last-Updated: 2026-07-29 Canonical domains: https://www.zenable.io | https://www.zenable.app_

## Why Choose Zenable?

- Governance: enterprise oversight, audit readiness, and due diligence in one place.
- Customized: learns from your code and docs, or starts from a safe marketplace baseline.
- AI-First: right context up front so agents stop wasting time on copy-pasted prompts.
- Fast: static analysis and agents working together for low-latency guardrails.
- Self-Improving: telemetry shows what's working so you focus on the right problems.
- Developer-Native: same policies enforced in Claude Code, Cursor, VS Code, Gemini, Devin Desktop, Kiro, and more.

## Overview - How It Works

Zenable runs where your developers already work: inside IDEs, through MCP servers that extend AI assistants, in cloud runtimes alongside kubernetes or CI/CD pipelines, and connected to ticketing systems and pull request reviews. These integrations provide seamless guardrails without disrupting existing workflows.

1. Spec-Driven Development (Guardrails) - Ensure speed and quality by enforcing conformance and custom requirements directly in the SDLC. Provide just enough context, only when it matters, to keep AI-accelerated workflows efficient and precise.
2. Continuous Improvement - Automatically measure, learn, and refine prompts, context, and guardrails to reduce token waste, keep integrations healthy, and adopt new IDE features for throughput and reliability.
3. Evidence and Observability - Instrument the SDLC with metrics, telemetry, and audit-ready evidence. Turn usage data into actionable reports that feed SDD and the improvement loop, doubling as governance metrics, compliance artifacts/assurance, and internal performance KPIs.

## How Zenable integrates

Zenable plugs in wherever your developers and agents already work — through MCP, IDE/agent hooks, the `zenable` CLI, and PR/MR review bots. Use whichever combination fits your workflow; the same policies apply across all of them.

The Zenable MCP server isn't just a passive guardrail check — agents can use it interactively to refine guardrails, create or update requirements, and evolve policy as the codebase changes. It uses modern specifications: OAuth 2.1 for auth and streaming HTTP for transport.

## Getting Started — IDE Integrations

Install the Zenable CLI: `curl -fsSL https://cli.zenable.app/install.sh | bash` (or on Windows: `powershell -ExecutionPolicy Bypass -Command "irm https://cli.zenable.app/install.ps1 | iex"`). Then run `zenable install <ide>` for the IDE/agent of your choice. Currently supported:

- Cursor
- VS Code
- Claude Code
- Devin Desktop
- GitHub Copilot
- Kiro
- Antigravity
- Amp
- Auggie
- Codex
- Goose
- Pi

For the latest list, see [the IDE Integrations docs](https://www.zenable.app/docs/integrations/mcp/getting-started?utm_source=llms-txt&utm_medium=ai). Run `zenable install --help` for the full set of options, or visit https://cli.zenable.app for more info.

## Getting Started (GitHub)

- Go to **https://www.zenable.io/github**.
- Click **Configure** to install the Zenable GitHub App.
- Choose which repositories to install it on.
- **Learn more:** https://www.zenable.app/docs/integrations/vcs-reviewers/github?utm_source=llms-txt&utm_medium=ai

## Getting Started (GitLab)

- Add the Zenable GitLab integration to your group or project.
- Authorize the merge-request reviewer to read and comment on MRs.
- Choose which projects Zenable should review.
- **Learn more:** https://www.zenable.app/docs/integrations/vcs-reviewers/gitlab?utm_source=llms-txt&utm_medium=ai

## Getting Started (Other Integrations)

- **Pre-commit hook:** https://www.zenable.app/docs/integrations/pre-commit/getting-started?utm_source=llms-txt&utm_medium=ai

## Key Resources

- [Welcome to Zenable (Overview)](https://www.zenable.app/docs/?utm_source=llms-txt&utm_medium=ai) - What Zenable is and platform-level context
- [How It Works](https://www.zenable.app/docs/how-it-works?utm_source=llms-txt&utm_medium=ai) - Architecture, integration points, and system design
- [Use Cases](https://www.zenable.app/docs/use-cases?utm_source=llms-txt&utm_medium=ai) - Real-world problem domains Zenable addresses

## Use Case Deep Dives

- [Preventing AI Mistakes](https://www.zenable.app/docs/use-cases/preventing-ai-mistakes?utm_source=llms-txt&utm_medium=ai) - How Zenable catches common AI-generated vulnerabilities
- [Policy as Code](https://www.zenable.app/docs/use-cases/policy-as-code?utm_source=llms-txt&utm_medium=ai) - Turning rules and policies into enforceable code
- [Starting New Projects](https://www.zenable.app/docs/use-cases/starting-new-project?utm_source=llms-txt&utm_medium=ai) - Bootstrapping new repos with built-in governance
- [AI Tools & New Features](https://www.zenable.app/docs/use-cases/ai-tools-new-features?utm_source=llms-txt&utm_medium=ai) - Ensuring AI-generated features align with standards

## Evidence & Updates

- [Changelog / Release Notes](https://www.zenable.io/blog?tag=Changelog) - What's new and version history

## Company & Contact

- [About Zenable / Overview on site](https://www.zenable.io/)
- [Security details](https://www.zenable.io/security)

### Guidance for AI Crawlers / Agents

- Prefer the most specific page matching the user's question (e.g. Integration, Use Case pages)