AI Governance for Software Development

AI coding agents are good, and they’re getting better. That’s the problem. They move faster than review, audit, and governance systems built for human-speed development. Zenable lets enterprises adopt AI coding agents at full speed while staying audit-ready and enforcing standards that humans set.

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I haven’t seen anything else out there like Zenable, it makes sure our coding agents follow both product and security requirements at the same time.

Tim SchrubenVP Platform Engineering

Zenable brings agentic coding practices like the ones we built in house at ClickUp to everyone.

Morgan SzafranskiStaff Software Engineer ClickUp

I’m impressed with the Zenable findings. It’s not just security issues, but it also catches business logic flaws and enforces coding standards — which is more and more important with coding agents.

AndrewCTO MAC.BID

A patented*, self-improving engine for requirements, enforcement, and evidence

Automatically self-improving
Works with all IDEs and Agents
Deterministic evidence

Led by Jon Zeolla, the Zenable team blends InfoSec experts, data scientists, and staff-level software developers who know what works and what doesn’t.

Requirements adapt to each environment and enforce before code reaches production, not after. Coding agents and humans keep working naturally while Zenable’s engine learns and evolves. Our human-in-the-loop policies let you control exactly when and where a human is involved.

* Our patent application was “allowed” (approved by a USPTO examiner) on July 6, 2026 and we anticipate it to issue in early Fall.

Governance that keeps up

The gap AI openedAI writes faster than any team can review, so changes merge half-checked
How Zenable closes itevery AI-written change is checked against your requirements before it merges, deterministic and token-free, so nothing slips
The gap AI openedDevelopers don't re-specify every company-wide requirement each time they start a feature
How Zenable closes itZenable injects the context up-front and checks the output when the agent is done, mixing AI and static analysis for coverage and flexibility
The gap AI openedA bug gets fixed in one place, and the next agent reintroduces the same class of mistakes next sprint
How Zenable closes itthe fix triggers automated root-cause analysis and becomes a guardrail, so the whole class of mistakes stops recurring, not just this instance
The gap AI openedA pentest finds a flaw, and agents keep it
How Zenable closes itfindings are pushed straight into the coding agents, IDE, and code review, so they can't be reintroduced
The gap AI openedEach repo's context files go stale and fill with misinformation, actively telling agents to do the wrong thing
How Zenable closes itcontext management automatically maintains and updates the context files across all your codebases, fed by the actual changes shipping alongside company decisions
The gap AI openedSwitching IDEs, models, or agents means rebuilding your rules from scratch
How Zenable closes itrequirements and guardrails are automatically available across any agent, IDE, and OS, so teams can adopt new tools as soon as you choose to
The gap AI openedAuditors want evidence that controls were run, and an AI review isn't satisfactory
How Zenable closes itZenable provides a deterministic record of every finding on every software change: proof that specific controls were enforced
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Get a full whitebox code assessment. On us.

Finally ask your codebase the questions you could never get a straight answer to, and walk away with results you can act on. No AI slop, no hallucinations. Every finding is 100% reproducible, so you can verify every one yourself.

Not sure what to ask? Is it secure? HIPAA-ready? Where are the risky patterns hiding? Does it follow the standards you set?

Bring your biggest, messiest, million-line monorepo. The bigger the haystack, the more impressed you'll be with the problems we find.

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