Zero Code Retention: Protecting Code Privacy in AI Code Reviews

As CTOs and engineering leaders, you know that source code is your crown jewels — it embodies your IP, contains customer data, and reflects years of design decisions. When we built Panto as an AI code-review platform, we treated code with that level of trust: our guiding rule has been never to store or expose customer code beyond the moment of analysis. In this post I’ll explain why zero code retention is critical for AI-powered code reviews, how our architecture enforces it, and what it means in practice (for example, one customer cut PR merge times in half without sacrificing privacy). We’ll also cover how a privacy-first design meshes with industry standards like SOC 2, ISO 27001, and GDPR.

Why AI Code Reviews Require Zero Code Retention

AI tools can supercharge code quality and speed, but they also raise a new class of privacy risks. To review a PR, an AI service typically pulls code from your repository and processes it — often in the cloud. If that code is logged, cached, or used to train models, it opens the door to leaks of proprietary logic or sensitive data. For example, imagine a fintech merging code that includes a private encryption key or a trade-secret algorithm: the last thing they want is for that code to linger on an AI server or feed a language model somewhere. In fact, industry leaders have emphasized this point: one startup in privacy-aware code analysis boasts that “data is never stored anywhere” during review..

At Panto we drew the obvious conclusion: our AI code-review platform must follow a zero code retention policy. In plain terms, that means any code we fetch for analysis lives only in-memory and is immediately discarded once the review is done. We don’t write code to disk, we don’t record it in logs, and we never feed it back into model training. As Tabnine’s documentation puts it, “Requests are only ephemerally processed… and then immediately discarded”. We do the same. By designing for ephemerality, Panto ensures that running AI-powered reviews doesn’t create a new data-leak vector.

Our Privacy-First Architecture for Code Security

Delivering zero code retention required a clean architectural approach. We built Panto on secure cloud infrastructure (Microsoft Azure) and designed it so that customer code flows through as little of our stack as possible. Key elements include:

• Ephemeral Processing: When a pull request triggers a review, Panto fetches the code directly from your source control (GitHub, GitLab, Bitbucket, Azure DevOps, etc.) and loads it into a short-lived compute task. As our documentation states plainly, “Panto does not store customer code” — the code is used only for the duration of that task (typically a few minutes) and then purged.

• No Persistence: Nothing about your code is written to any database or file system. Once the analysis finishes, the code is gone. In fact, when we examined every step of our pipeline, we ensured there are no hidden caches or secondary storage for code data. This follows best practices for privacy: to echo an industry example, another AI tool notes “we actually use ephemeral processing… Data is never stored anywhere”.. That’s exactly how Panto operates.

• On-Premise and BYOC Options: Many customers insist on absolute control. For those cases, Panto can run behind your firewall. In self-hosted or on-premise mode, all components (including the AI models, if desired) live in your VPC, so literally no code or related data leaves your infrastructure. We even support “bring your own LLM” (BYOLLM) deployments, so you can use your own language model endpoints by URL. Either way, whether cloud or on-prem, the code never lingers in our servers.

• Encrypted Enterprise API Calls: All communication is secured end-to-end. We integrate with major AI providers using their enterprise-grade APIs (OpenAI Enterprise, Anthropic, Google Gemini, etc.), each of which has its own encryption and compliance safeguards. Code is retrieved and sent over TLS, and Panto itself runs on Azure (which enforces encryption at rest by default and carries SOC 2/ISO 27001 certifications). In short, every hop of the data path is encrypted, and no third-party model is allowed to see your code unprotected.

• Strict Access Controls: Within Panto’s own team, access to any customer data is tightly limited. Our security policy states that employees “are prohibited from viewing customer data unless absolutely necessary”, and all access is monitored and audited. In practice, this means support staff never see actual customer code, and even automated logs never contain sensitive content.

We also separate code from metadata. Panto does collect anonymous usage metadata — for example, how many pull requests were opened, how many comments the bot made, or how often suggestions were accepted — purely to improve the service. Crucially, this metadata never contains the code text itself, only abstract metrics. (And in on-prem deployments even that metadata can stay inside your network).

By assembling these pieces, we achieve code security by architecture. Code is handled in-memory only, in locked-down containers, with encryption and policy checks around it. Other AI-driven review platforms similarly pledge zero-retention — for instance, Tabnine explicitly says “we never retain any code on our servers” — but at Panto we went further by baking it into every layer of our design. Our motto from Day 1 has been: own the promise. If we say “Your code, your data,” we meant it.

Real-World Impacts: Privacy-First AI Code Reviews in Action

What does Zero Code Retention look like on the ground? In practice, our customers have seen tangible benefits without having to sacrifice security. For example, one Panto user (a large financial services firm) deployed our on-prem solution and reported doubling their review speed, cutting pull request merge times by around 50%. Crucially, they achieved this while satisfying their auditors that no code was ever stored or leaked during the process. Because “no code or related data leaves the customer’s infrastructure”, they could adopt AI reviews with full confidence.

Another customer in the health-tech space needed HIPAA-level privacy. They used Panto’s cloud agent, which never retains code or health data, and appreciated that our system could enforce HIPAA-related checks without compromising patient privacy. (In fact, our underlying design can incorporate any compliance rules — think “HIPAA if in healthcare, or GDPR if you have EU data,” just as a recent AI compliance tool noted..) As a result, the team sped up secure releases by about 20% and had no incidents of data leakage.

A SaaS startup pursuing SOC 2 certification also chose Panto. Because we run on Azure — a platform with SOC 2 and ISO 27001 compliance — their security team found it easy to validate the solution. They appreciated that Panto not only accelerates code reviews, but does so in a way that directly maps to SOC 2 controls (encryption, audit logs, least privilege) and the GDPR principle of data minimization. In their own words, using Panto let them “turn our security and QA guidelines into part of the automated checks”, all without ever holding onto customer code.

These examples illustrate a simple business truth: Privacy-first design doesn’t hurt productivity; it unlocks trust. Our customers tell us they move faster because engineers can use AI confidently, rather than hesitating and doing manual reviews. We’ve already processed millions of lines of code in production (over 5 million lines reviewed so far), and none of it remains on our side. Each analysis leaves only helpful feedback — no code fragments, no secrets — in the pull request.

Aligning with SOC 2, ISO 27001, and GDPR

Enterprises evaluating AI tools often ask about compliance: “How do you handle audit controls? Are you certified?” Our answer is that we built Panto with compliance in mind from Day 1. Hosting our SaaS on Azure means we inherit industry-standard certifications (as our docs note, Azure has “SOC reports and ISO standards”). On top of that, our zero retention policy addresses key regulatory concerns out of the box.

• SOC 2 & ISO 27001: These frameworks emphasize confidentiality, access controls, and data integrity. By design, Panto meets these requirements: code never persists, all transfers are encrypted, and access is logged. We enforce strict internal controls (limiting who can see customer data) and allow customizable policies. For example, Panto’s policy engine lets each team encode its own compliance rules (even down to regional ones like India’s CERT‑IN), which “become part of Panto’s checks”. This means your security policies — change-control rules, code scanning requirements, and so on — are enforced on every PR, with the same rigor expected in a SOC 2 audit.

• GDPR (Data Protection): GDPR’s principles include data minimization and limiting storage duration. We satisfy these inherently: if we don’t store code, we never accumulate European personal data. The concept is the same as a modern “privacy assurance” approach: one source puts it succinctly, “Users’ personal data is never stored or processed in clear text” to reduce breach risk. In Panto’s case, we never even store code in any form, encrypted or not. In effect, we act as a processor that uses your data transiently, then discards it — the strictest form of GDPR-compliant behavior. (And for peace of mind, you can choose data center regions or on-prem deployment to keep everything within specific jurisdictions.)

In short, zero code retention is not a gimmick. It’s an accelerator for compliance. Instead of retrofitting data-retention policies or audits, customers get a built-in privacy control. Auditors can see that Panto’s architecture literally can’t retain code, which simplifies evidence of meeting confidentiality and storage requirements.

Conclusion: Trust is Built In

At Panto, our philosophy is straightforward: Treat code like private data, because it is. Every architectural choice — from ephemeral compute to encrypted pipelines to on-prem options — is guided by the need to keep your code secure and private. This isn’t just marketing fluff; it’s a business decision. By refusing to store code, we eliminate a whole class of risk, making it easy for engineering teams to adopt AI reviews confidently.

The results speak for themselves. We see engineering orgs shipping features faster and with fewer bugs, yet with none of the worry that “what if the AI stole my secrets?” The promise of AI in code review only holds if we earn your trust. That’s why we made zero code retention a core promise. In the words of our CTO: our goal is that when you use Panto, you can say “Yes, we reviewed 100% of our code with AI, and we never left a copy of it anywhere else.”

Panto can be your new AI Code Review Agent. We are focused on aligning business context with code. Never let bad code reach production again! Try for free today: