AI Code Review for Enterprise: Deployment, Security, and Compliance
How enterprise teams evaluate AI code review: security certifications, SSO, audit logging, and the procurement questions that matter. A guide for engineering and security leaders connecting AI to production codebases.
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 code. Macroscope does not train on your code, and its model providers are contractually prohibited from training on it.
- Cloud-hosted service. Macroscope runs as a managed cloud service on GCP. Self-hosting is not available.
- SSO and access controls. SAML/OIDC SSO, repository-level permissions, team management.
- Audit logging. Every review, every action, logged and exportable for compliance.
- Usage-based pricing scales with work, not headcount. No per-seat fees means no surprise costs as the team grows.
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 | DPAs, SCCs for international transfers |
| 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 (SAML/OIDC) | Centralized identity management |
| Repository-level permissions | Control which repos are reviewed |
| Team and role management | 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 is built for enterprise deployment: SOC 2 Type II compliant, architecturally isolated, no model training, with SSO, audit logging, 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. All customer data is encrypted in transit (TLS) and at rest (AES-256).
No model training on customer code. Macroscope does not train any models on customer source code. Macroscope's agreements with OpenAI and Anthropic contractually prohibit those providers from training on customer code. This is a policy, not a setting you have to find.
Architectural code isolation. Customer code is isolated by design. Macroscope employees cannot access customer source code — there is no support backdoor that lets someone browse your repositories.
Clear subprocessors. Macroscope's named subprocessors are Google Cloud Platform (infrastructure), OpenAI and Anthropic (model inference), and Slack (Slack integration). The list is published on the trust center.
Data protection agreements. Custom MSA and DPA agreements are available for enterprise customers. Transfers to model providers are covered by Standard Contractual Clauses.
Deployment model
Cloud-hosted. Macroscope runs as a managed cloud service on GCP. 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
SSO support. Macroscope supports SAML and OIDC single sign-on, so users authenticate through your identity provider. No separate passwords to manage.
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.
Team management. Organize users into teams with different permission levels. Administrators can manage workspace settings, billing, and access. Members can view reviews and interact with the reviewer.
API access. Programmatic access through the API is scoped by workspace and respects the same permission model.
Auditability
Audit logging. Every review, every action, every configuration change is logged. Logs include who, what, when, and which repository.
Log export. Logs can be exported for compliance reporting or fed into your SIEM.
Review history. All reviews are retained and searchable. When a compliance audit asks "what did the AI say about this PR six months ago," the answer is available.
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, with specific standards (TLS, AES-256) | 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" |
| Can your employees access my code? | No, architecturally isolated | "Only with permission" or unclear |
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? | Some vendors offer region-specific hosting. Ask for specifics. |
| Can employees access my code? | Look for architectural isolation, not just policy. |
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? | SAML and OIDC | "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 audit logs? | Yes, with export capability | "We log internally" |
| Can I integrate with my SIEM? | Yes, via export or API | 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. DPA, no-training clauses, subprocessor disclosure.
- Produces audit logs. Every interaction is recorded.
- 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 | SSO integration, no separate credentials |
| Access control | Repository-level permissions, role-based teams |
| Audit and logging | Logs feed into SIEM, retained for compliance |
| Vendor management | SOC 2 report, subprocessor list, DPA |
| 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
- Security team reviews the vendor. Trust center, SOC 2 report, data handling policies.
- Install on 1-3 low-risk repositories. Not production-critical code.
- Run for 2-4 weeks. Collect feedback from developers, review quality of findings.
- Evaluate precision and noise. Are comments actionable? Is the signal-to-noise ratio acceptable?
Phase 2: Expansion
- Expand to more repositories. Include one production-critical repository.
- Configure team settings. SSO, roles, spend controls.
- Integrate with workflow. Slack notifications, required checks if appropriate.
- Measure impact. Review cycle time, bugs caught, developer satisfaction.
Phase 3: Organization-wide rollout
- Enable on all repositories. With appropriate exclusions (generated code, vendored dependencies).
- Set organization-level policies. Spend limits, required checks, auto-approval rules.
- Train teams. How to interact with the reviewer, how to give feedback.
- 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
- SAML/OIDC. SSO 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 | Yes, contractual | 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:
- 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.
- Review the trust center. trust.macroscope.com has security controls, policies, and a request workflow for the SOC 2 report.
- Contact the enterprise team. enterprise@macroscope.com for custom agreements and volume pricing.
- Install on a pilot repository. Low-risk, active development, good test coverage.
- Run for 2-4 weeks. Collect feedback, measure signal quality.
- 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
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. Macroscope does not train any models on customer source code. Macroscope's agreements with OpenAI and Anthropic contractually prohibit those providers from training on customer code. This is a policy, not a setting.
Can Macroscope employees see my code?
No. Customer code is architecturally isolated. Macroscope employees cannot access customer source code. There is no support backdoor that allows browsing customer repositories.
Does Macroscope support SSO?
Yes. Macroscope supports SAML and OIDC single sign-on, so users authenticate through your identity provider.
Can Macroscope be self-hosted?
Macroscope Code Review runs as a managed cloud service on GCP. 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?
Macroscope's named subprocessors are Google Cloud Platform (infrastructure), OpenAI and Anthropic (model inference), and Slack (Slack integration). The list is published on the trust center.
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. Enterprise customers can access volume discounts and custom agreements. Every workspace starts with $100 in usage credit. Contact enterprise@macroscope.com for enterprise pricing.
Is there an audit log?
Yes. Every review, action, and configuration change is logged with who, what, when, and which repository. Logs can be exported for compliance reporting or SIEM integration.
How long is review history retained?
All reviews are retained and searchable. When a compliance audit asks about a specific PR review, the history is available.
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.