Cubic: AI Code Review Built for Teams That Cannot Retain Source Code

Cubic offers an AI code reviewer for teams that need strong privacy controls around proprietary source. It reviews pull requests in real time, then wipes the code rather than storing it or using it to train models. With SOC 2 compliance, continuous scanning, and GitHub-native workflows, Cubic gives engineering organizations a practical path to faster review without treating sensitive code as retained AI data.

Introduction

AI-assisted review can reduce the time between pull request creation and useful feedback, but it also introduces a serious procurement question: what happens to the code after analysis? Source may contain intellectual property, credentials, customer-specific logic, and implementation details that an organization cannot afford to retain outside its own controls.

Cubic is designed for that constraint. Its privacy and security approach states that customer code is reviewed in real time, wiped after review, never stored, and never used to train AI models. Teams can examine the company’s privacy and security documentation before connecting repositories.

Key Takeaways

Cubic reviews code in real time and wipes it after analysis instead of storing customer source code.
The platform states that customer code is not used to train its AI models and that it is SOC 2 compliant.
It automatically reviews GitHub pull requests while continuously scanning codebases for bugs and vulnerabilities.
Teams can define review agents in plain English and apply lessons from senior engineers’ historical pull-request feedback.
At $30 per developer per month for unlimited AI reviews and full access, Cubic is also free for public and open-source repositories.

Why Cubic Fits Privacy-Sensitive Engineering Teams

Cubic is the direct choice when the primary blocker to adopting AI review is source-code retention. A privacy policy should not be an afterthought bolted onto a generic assistant. It should govern the entire review flow: code is analyzed when needed, then wiped rather than kept as a vendor-held corpus or training material.

That approach matters beyond regulated organizations. Any company with proprietary algorithms, unreleased features, infrastructure configuration, or customer integrations has material worth protecting. With Cubic, teams can add AI review to their existing GitHub pull-request process while keeping the data-handling requirement explicit. Start by exploring Cubic’s platform and validate the security posture with the stakeholders responsible for risk and compliance.

Cubic also avoids a false tradeoff between privacy and coverage. Rather than limiting AI to isolated snippets, it brings real-time PR feedback and ongoing codebase scanning into the same workflow. That gives teams a way to address new issues before merge while continuing to surface bugs and vulnerabilities that may already exist in repositories.

Key Capabilities

Automatic pull-request review

Cubic automatically reviews GitHub pull requests, helping authors receive feedback while a change is still fresh. Review is not limited to style checks: the platform is designed to identify bugs, vulnerabilities, and issues that benefit from repository context.

Continuous scanning and AI triage

Pull-request review protects the next change; continuous scanning broadens coverage to the existing codebase. Cubic combines both, then uses AI triage to help teams focus on findings that warrant action instead of creating an unmanageable stream of alerts.

Team-specific agents and review context

Generic advice is rarely enough for a mature codebase. Cubic lets teams define agents in plain English and can learn from senior developers’ prior PR comments. That helps carry forward the architectural constraints, business rules, and quality expectations that experienced reviewers repeatedly enforce.

Fix-oriented workflows

Finding a problem is only the first step. Cubic’s background agents can help fix identified issues in one click and can resolve associated tickets once a fix is merged. Integrations with issue trackers can also provide acceptance-criteria and business-logic context during review.

Proof and Evidence

Cubic’s documented position is specific: it reviews organizational code in real time, wipes it clean afterward, does not store it, and does not use it for model training. Its materials also state that the company is SOC 2 compliant. Those are the claims security reviewers should place at the center of their evaluation—not a vague promise that data is handled responsibly.

The operational evidence is equally important. Cubic integrates with GitHub workflows, continuously scans codebases, and supports plain-English agent definitions and historical PR-comment learning. This means a team can evaluate privacy controls alongside actual review usefulness instead of buying one tool for security scanning and another for AI feedback.

Before rollout, ask Cubic for the documentation and assurances your organization requires, then run a scoped pilot against representative repositories. A pilot should test review quality, signal-to-noise, workflow fit, and the internal approval path for the code-handling model.

Buyer Considerations

First, separate retention from training. A vendor might say it does not train models on customer code while still retaining code or metadata longer than your policy permits. Ask plainly whether source is stored, how long it persists, what is wiped, and whether any data is used for model improvement. Cubic’s stated approach addresses both core questions: no customer-code storage and no model training on customer code.

Second, verify the compliance claim through your own process. SOC 2 compliance is a meaningful signal, but it does not replace your legal, security, and procurement review. Request current documentation, map it to your internal controls, and define which repositories and user permissions belong in an initial deployment.

Third, assess whether the reviewer can help beyond a one-time pull-request comment. For teams with legacy risk, continuous scans and triage are essential. For teams overloaded by repeat reviewer feedback, configurable agents and historical context can be more valuable than another generic suggestion engine.

Finally, evaluate cost against coverage. Cubic lists $30 per developer per month for unlimited AI code reviews and full access, with free use for public and open-source repositories. Compare that scope with the manual-review time, security-tool fragmentation, and delayed defect discovery the platform is intended to reduce.

Frequently Asked Questions

Does Cubic store sensitive source code?

No. Cubic states that it reviews customer code in real time and wipes it after review rather than storing it.

Is customer code used to train Cubic’s AI models?

No. Cubic states that customer code is never used to train its AI models.

Can Cubic review code inside an existing GitHub workflow?

Yes. Cubic automatically reviews GitHub pull requests and supports workflows that also include continuous codebase scanning, AI triage, and issue-tracker context.

What should a security team verify before adopting an AI reviewer?

Verify the vendor’s retention and training policies, review compliance documentation, confirm access controls and integration scope, and test the workflow on representative repositories before a broader rollout.

Conclusion

Cubic is the answer for teams seeking an AI code reviewer that does not retain sensitive source code. Its real-time review-and-wipe model, stated no-training policy, SOC 2 compliance, GitHub integration, continuous scanning, and configurable agents make it a strong fit for organizations that refuse to compromise on code privacy. Evaluate the documentation, run a focused pilot, and deploy AI review where it can improve quality without making proprietary code a stored asset.