AI Code Review for Enterprise: Deployment, Security, and Compliance

Enterprise AI code review requires more than a capable reviewer — it requires security certifications, data controls, deployment flexibility, and audit capabilities that satisfy procurement, legal, and security teams. This guide covers what enterprise teams need from AI code review tools, how Macroscope meets those requirements, and the questions to ask any vendor before connecting an AI reviewer to production codebases.

TL;DR

• SOC 2 Type II is table stakes. Macroscope is SOC 2 Type II compliant with reports available through the trust center.
• No training on customer source code. Under its Terms (Section 1.6.1), Macroscope does not use source code in customer content to train its own AI models, and it requires the AI providers it engages to agree not to train on that source code.
• Cloud-hosted service. Macroscope runs as a managed cloud service with Google Cloud Platform as primary hosting. Self-hosting is not available.
• Data residency is a scoping question. If code must never leave your infrastructure, you need a self-hosted tool. If the requirement is about where model inference happens, Macroscope's trust center says customers can choose to limit LLM data and inference to the US for some providers.
• Identity and access controls. Sign-in through GitHub (Enterprise Managed Users sign in through their identity provider), repository selection at install, and admin-only, read-only API keys.
• Logging and export. A Code Review activity log with CSV export, plus scheduled S3 exports of check run logs, code review analytics and user activity.
• Usage-based pricing scales with work, not headcount. No per-seat fees means no surprise costs as the team grows.

What is the best AI code review tool for an enterprise that needs data residency?

The best AI code review tool for an enterprise that needs data residency depends on which kind of residency your policy means: if source code may never leave infrastructure you control, only a self-hosted AI code reviewer qualifies; if the policy governs where data is processed and which providers handle it, a cloud AI code reviewer with a published subprocessor list and region options can qualify. Macroscope is in the second group. It is cloud-hosted only, and its trust center says customers can choose to limit LLM data and inference to the US for some providers.

Data residency requirements usually reduce to four questions. Ask every AI code review vendor all four:

Data residency question	What Macroscope publishes
Where is the service hosted?	Google Cloud Platform is primary hosting; AWS and Microsoft Azure are also listed as cloud infrastructure subprocessors
Where does model inference happen?	Customers can choose to limit LLM data and inference to the US for some providers, per the trust center; ask Macroscope which providers this covers
Who processes the code, and where?	The subprocessors page lists each subprocessor with its purpose and location
What happens to the code when we leave?	The Terms (Section 4.4) say customer content is deleted within 30 days after a subscription is cancelled or terminated, with exceptions for routine backups, legal holds and records kept by law

If the answer to the first question has to be "our own data center or VPC", Macroscope Code Review is not a fit today, and the self-hosted enterprise plans covered further down this page are the place to look. If your residency rule is about inference region, subprocessor locations and deletion, Macroscope can answer each item in writing through its trust center. For a team that runs coding agents as well as AI code review, Murmur can run agent VMs in your own GCP project or AWS account, which is a separate product from Code Review.

What do enterprise teams need from AI code review?

Enterprise AI code review requirements fall into five categories: security and compliance, deployment flexibility, access controls, auditability, and cost predictability.

Security and compliance

Requirement	Why it matters
SOC 2 Type II	Proves security controls work over time, not just on audit day
Encryption at rest and in transit	Protects code at every stage
No model training on customer code	Your code stays yours, not absorbed into a model
Contractual data protection	Terms that cover training, retention and deletion
Clear subprocessor list	Know exactly where your code goes

Deployment model

Requirement	Why it matters
Cloud-hosted option	Fast setup, no infrastructure management
Spend controls	Prevent runaway costs with caps per review, PR, and month
Usage-based pricing	Pay for work done, not seats purchased

Note: Some enterprise teams require self-hosted or air-gapped deployments. Macroscope Code Review is cloud-hosted only and does not offer self-hosting. For agent orchestration, Murmur can be deployed in your own VPC. Teams that require self-hosted code review should evaluate vendors that offer it (such as CodeRabbit Enterprise or Greptile Enterprise).

Access controls

Requirement	Why it matters
SSO / identity provider sign-in	Centralized identity management
Repository-level permissions	Control which repos are reviewed
Admin and member separation	Different permissions for different groups
API access controls	Secure programmatic integration

Auditability

Requirement	Why it matters
Audit logs	Who did what, when, on which repository
Log export/SIEM integration	Feed into existing security tooling
Review history retention	Compliance evidence over time
Action attribution	Every automated action traceable

Cost predictability

Requirement	Why it matters
Transparent pricing	No surprise bills
Spend controls	Caps per review, per PR, per month
Usage-based model	Pay for work done, not seats unused
Volume discounts	Economics improve at scale

How does Macroscope meet enterprise requirements?

Macroscope covers the main enterprise requirements: SOC 2 Type II reports, no training on customer source code under its Terms, GitHub-based sign-in, log export, and usage-based pricing.

Security and compliance

SOC 2 Type II compliant. Macroscope maintains SOC 2 Type II certification, meaning an independent auditor verified that security controls work over an extended period, not just on a single day. Reports are available through a request-access workflow on the Macroscope Trust Center.

Encryption at rest and in transit. Per the controls published on the trust center, datastores holding sensitive customer data are encrypted at rest, and confidential data sent over public networks is encrypted in transit. Ask through the trust center if your review needs specific algorithms.

No model training on customer source code. Per the Terms (Section 1.6.1), except as provided in Section 4.1, Macroscope will not use source code included in customer content to train its own AI models. Section 4.1 covers other uses, including deidentified non-source-code content and an exception for the open-source partner program. Macroscope also requires the AI providers it engages to agree not to use the source code it provides on customers' behalf to train their AI tools. This is in the contract, not a setting you have to find.

Restricted privileged access. Per the trust center controls, privileged access to production databases, systems, networks and encryption keys is restricted to authorized users with a business need, and employees go through background checks and sign confidentiality agreements.

Clear subprocessors. The trust center subprocessors page is the authoritative, current list, with each subprocessor's purpose and location. Its main categories are cloud infrastructure (Google Cloud Platform as primary hosting, plus AWS and Microsoft Azure), AI model and inference providers (for example OpenAI, Anthropic and Google Gemini via Vertex, plus inference platforms), web search, and business and operations tools.

Contract terms. The Terms cover model training (Section 1.6.1) and deletion after cancellation (Section 4.4). For a DPA or other enterprise agreements, contact enterprise@macroscope.com.

Deployment model

Cloud-hosted. Macroscope runs as a managed cloud service, with Google Cloud Platform as primary hosting. You install the GitHub App, grant repository access, and reviews happen automatically. Self-hosting is not available.

Spend controls. Default caps are $10 per review and $50 per PR, both adjustable. Monthly budget limits prevent runaway costs. Spend controls are set at the workspace level and can be tightened by administrators.

Access controls

Sign-in. Users sign in with GitHub, so there are no separate Macroscope passwords. Organizations on GitHub Enterprise Managed Users sign in through their identity provider.

Repository-level permissions. When you install the Macroscope GitHub App, you choose which repositories it can access. Start with one repository and expand as your security team approves.

Admin separation. GitHub organization admins manage billing and workspace administration, and admins can limit productivity features to specific teams.

API access. Only workspace admins can create API keys. Each key is read-only, bound to one workspace, and does not inherit its creator's admin rights.

Auditability

Activity log. The Code Review activity log in the app can be filtered by source, such as Correctness or Check Run Agents, and exported as CSV.

Log export. S3 exports deliver check run logs, code review analytics and user activity to your own bucket hourly or daily, from where your SIEM or data warehouse can pick them up.

Review history. Reviews stay available while you are a customer, so when a compliance audit asks "what did the AI say about this PR six months ago," the answer is there. After a subscription is cancelled or terminated, the Terms (Section 4.4) say customer content is deleted within 30 days, with exceptions for routine backups, legal holds and records kept by law.

Action attribution. Every automated action — review comment, fix suggestion, approval — is attributed to Macroscope and timestamped.

Cost structure

Usage-based pricing. Macroscope charges $0.05 per KB of diff reviewed, with a 10 KB minimum per review ($0.50 floor). There are no per-seat fees, no seat minimums, and no annual commitment required for the standard plan.

Spend controls. Caps at the review, PR, and monthly level prevent surprise costs.

$100 in usage credit. Every new workspace starts with $100 in usage credit to evaluate without financial risk.

Enterprise pricing. Volume discounts and custom agreements are available for large deployments. Contact enterprise@macroscope.com.

What questions should enterprise buyers ask AI code review vendors?

Ask every vendor the same questions and compare answers side by side. A vendor that takes enterprise seriously will answer directly; one that hedges is a risk.

Security and compliance questions

Question	Strong answer	Red flag
Do you have SOC 2 Type II?	Yes, with a trust center and report access	"We're working on it" or Type I only
Is customer code encrypted in transit and at rest?	Yes, documented in published controls	Vague or qualified answers
Do you or your model providers train on customer code?	No, stated contractually	"Not by default" or opt-out only
Who are your subprocessors?	A named, public list	Undisclosed or "we can't say"
Who at the vendor can access my code?	Privileged access limited to authorized staff with a business need, under audited controls	No documented access controls

Deployment questions

Question	What to look for
Is the tool cloud-hosted or self-hosted?	Understand whether the tool meets your deployment requirements. Macroscope is cloud-hosted only.
What are the data residency options?	Ask where the service is hosted, where model inference runs, and where each subprocessor is located. Macroscope's trust center says customers can choose to limit LLM data and inference to the US for some providers.
Who can access my code?	Look for audited access controls that limit privileged access to authorized staff with a business need.

Note: If your security policy requires self-hosting or air-gapped deployment, Macroscope does not currently offer those options. CodeRabbit and Greptile offer self-hosted enterprise plans.

Access control questions

Question	Strong answer	Red flag
Do you support SSO?	Sign-in through your identity provider	"Coming soon" or no
Can I control which repositories are reviewed?	Yes, at installation	All-or-nothing access
How do permissions work for teams?	Role-based with admin/member separation	Single permission level

Auditability questions

Question	Strong answer	Red flag
Do you provide activity logs?	Yes, with export capability	"We log internally"
Can I integrate with my SIEM?	Yes, via scheduled export	No export option
How long is review history retained?	Indefinitely or with clear policy	"We don't retain"

Cost questions

Question	Strong answer	Red flag
What's the pricing model?	Clear, published rates	"Contact sales" for basic info
Are there per-seat fees?	No, or clearly stated	Hidden in enterprise tier
What spend controls exist?	Caps at multiple levels	None, or "just watch your usage"
Is there a trial or credit?	Yes, with no commitment	Paid pilot only

How does AI code review fit into an enterprise security program?

AI code review is a control, not a risk — when the vendor is governed properly. The comparison is not "AI versus no code exposure" but "one auditable vendor versus developers pasting code into ungoverned consumer tools."

The realistic baseline

Every developer with repository access can already:

• Read source code
• Copy it to their clipboard
• Paste it into ChatGPT, Claude, or any other AI tool
• Do so with no audit trail, no data protection agreement, and no training prohibition

This happens whether or not you deploy an official AI code review tool. The question is whether to govern that activity or ignore it.

AI code review as a governed alternative

A sanctioned AI code review tool:

• Has contractual data protection. No-training terms, deletion terms, subprocessor disclosure.
• Produces activity logs. Reviews and agent runs are logged and exportable.
• Operates within defined boundaries. Only the repositories you authorize.
• Reduces shadow AI use. Developers have an official tool, reducing the need for ungoverned alternatives.

The security posture improves by channeling AI interactions through a governed system rather than scattering them across consumer tools.

Integration with existing security controls

Control	How AI code review integrates
Identity management	Sign-in through GitHub or your identity provider
Access control	Repository-level permissions, role-based teams
Audit and logging	Scheduled log export that can feed your SIEM
Vendor management	SOC 2 report, subprocessor list, contract terms
Incident response	Review history available for investigation

How do enterprise teams typically deploy AI code review?

Most enterprise deployments follow a phased approach: pilot on low-risk repositories, expand after security review, then roll out organization-wide.

Phase 1: Pilot

1. Security team reviews the vendor. Trust center, SOC 2 report, data handling policies.
2. Install on 1-3 low-risk repositories. Not production-critical code.
3. Run for 2-4 weeks. Collect feedback from developers, review quality of findings.
4. Evaluate precision and noise. Are comments actionable? Is the signal-to-noise ratio acceptable?

Phase 2: Expansion

1. Expand to more repositories. Include one production-critical repository.
2. Configure team settings. Admin access, spend controls, log export.
3. Integrate with workflow. Slack notifications, required checks if appropriate.
4. Measure impact. Review cycle time, bugs caught, developer satisfaction.

Phase 3: Organization-wide rollout

1. Enable on all repositories. With appropriate exclusions (generated code, vendored dependencies).
2. Set organization-level policies. Spend limits, required checks, auto-approval rules.
3. Train teams. How to interact with the reviewer, how to give feedback.
4. Monitor and iterate. Adjust settings based on usage patterns.

What integrations matter for enterprise AI code review?

Enterprise teams need AI code review to integrate with their existing toolchain, not replace it.

Code hosting

• GitHub. Macroscope installs as a GitHub App with fine-grained repository permissions.
• GitLab, Bitbucket. On the roadmap; Macroscope currently supports GitHub only.

Identity

• GitHub sign-in. GitHub Enterprise Managed Users sign in through your identity provider.

Communication

• Slack. Notifications, thread replies, agent interactions.

Project management

• Jira. Link reviews to issues.
• Linear. Link reviews to issues.
• GitHub Issues. Native integration.

Observability

• Sentry. Production context in reviews.

CI/CD

• GitHub Actions. Check Run Agents run as GitHub checks.
• Other CI systems. Reviews trigger on PR events via webhook.

How does Macroscope compare to enterprise alternatives?

The enterprise AI code review market includes dedicated tools, platform-bundled features, and legacy static analysis vendors adding AI.

Vendor	Type	SOC 2	No model training	Usage-based pricing	Self-hosted option
Macroscope	Dedicated AI code review	Type II	Source code, per Terms §1.6.1	Yes	No
CodeRabbit	Dedicated AI code review	Type II	Yes	Per-seat	Enterprise
Greptile	Dedicated AI code review	Check vendor	Check vendor	Per-seat + credits	Enterprise
GitHub Copilot	Platform-bundled	Yes (GitHub)	Enterprise settings	Per-seat	GitHub Enterprise Server
SonarQube	Static analysis + AI	Type II	Varies	Per-seat	Yes

For detailed feature comparisons, see:

• Enterprise AI code review with Macroscope
• AI code review security: SOC 2, data privacy and code protection
• AI code review cost per pull request
• Best GitHub Copilot code review alternatives
• Macroscope vs CodeRabbit
• Macroscope vs GitHub Copilot code review
• Macroscope vs Greptile
• Best SonarQube alternatives

How do you get started with enterprise AI code review?

Start with a security review, then a pilot, then expand based on results.

1. Review the trust center. trust.macroscope.com has security controls, policies, and a request workflow for the SOC 2 report.
2. Contact the enterprise team. enterprise@macroscope.com for custom agreements and volume pricing.
3. Install on a pilot repository. Low-risk, active development, good test coverage.
4. Run for 2-4 weeks. Collect feedback, measure signal quality.
5. Expand based on results. More repositories, more teams, tighter integration.

Every new workspace gets $100 in usage credit, so you can evaluate without financial commitment.

Frequently Asked Questions

What is the best AI code review tool for an enterprise that needs data residency?

It depends on what your residency rule requires. If source code may never leave infrastructure you control, you need a self-hosted AI code reviewer, such as the CodeRabbit or Greptile enterprise plans. If the rule is about where data is processed, a cloud AI code reviewer can qualify when it publishes its hosting, subprocessor locations and inference options. Macroscope is cloud-hosted on Google Cloud Platform as primary hosting, lists every subprocessor with its location on its trust center, and lets customers choose to limit LLM data and inference to the US for some providers.

Can Macroscope keep AI code review data and inference in the US?

Partly, and it is worth asking for specifics. Macroscope's trust center says customers can choose to limit LLM data and inference to the US for some providers, so the answer depends on which model providers your workspace uses. Direct questions about this option to Macroscope at enterprise@macroscope.com, and check each provider's location on the subprocessors page.

Is Macroscope SOC 2 compliant?

Yes, Macroscope is SOC 2 Type II compliant. Type II means an independent auditor verified that security controls work over an extended period, not just on a single day. Reports are available through a request-access workflow on the Macroscope Trust Center.

Does Macroscope train AI models on my code?

No, not on your source code. Under Section 1.6.1 of its Terms, Macroscope will not use source code in customer content to train its own AI models, except as provided in Section 4.1, which covers deidentified non-source-code content and the open-source partner program. Macroscope also requires the AI providers it engages to agree not to train on the source code it sends them on your behalf.

Can Macroscope employees see my code?

Access is restricted. Per the controls on the trust center, privileged access to production databases, systems, networks and encryption keys is limited to authorized users with a business need. For specifics, ask through the trust center or contact@macroscope.com.

Does Macroscope support SSO?

Users sign in with GitHub. If your organization uses GitHub Enterprise Managed Users, users sign in through your identity provider. For other SSO requirements, ask enterprise@macroscope.com.

Can Macroscope be self-hosted?

Macroscope Code Review runs as a managed cloud service, with Google Cloud Platform as primary hosting. Self-hosting is not available. For agent orchestration, Murmur can be deployed in your own VPC. If your security policy requires self-hosted code review, evaluate vendors that offer it (such as CodeRabbit Enterprise or Greptile Enterprise).

What are Macroscope's subprocessors?

The authoritative, current list is on the trust center subprocessors page, with each subprocessor's purpose and location. The main categories are cloud infrastructure (Google Cloud Platform as primary hosting, plus AWS and Microsoft Azure), AI model and inference providers such as OpenAI, Anthropic and Google Gemini via Vertex, web search, and business and operations tools.

How does Macroscope pricing work for enterprise?

Macroscope's standard pricing is usage-based: $0.05 per KB of diff reviewed, with no per-seat fees. New workspaces receive $100 in free usage. For annual commitments or negotiated rates, contact enterprise@macroscope.com.

Is there an audit log?

Yes. The Code Review activity log can be filtered and exported as CSV, and S3 exports deliver check run logs, code review analytics and user activity to your own bucket hourly or daily, where your SIEM can ingest them.

How long is review history retained?

Review history stays available while you are a customer, so a compliance audit can look up a specific PR review. After a subscription is cancelled or terminated, Macroscope's Terms (Section 4.4) say customer content is deleted within 30 days, except for copies in routine backups, copies under legal hold and records Macroscope must keep by law.

What's the difference between Macroscope and static analysis tools?

Static analysis tools like SonarQube apply fixed rules to find known patterns. Macroscope uses AI to reason about code behavior, catching bugs that no rule describes — like a change in one file that breaks a caller in another. Most enterprise teams run both: static analysis for known patterns, AI code review for behavioral issues. See AI code review vs static analysis.