TL;DR
Governance in an AI code generation tool comes down to six checks: who can sign in, what happens to your code, where the model runs, what the agent may do, what record it leaves and which audits the vendor has passed. I ran six tools through that list using public docs in October 2026: GitHub Copilot, Cursor, Augment Code, Factory, Tabnine and Forge. This guide explains each check, covers every tool in turn, and shows how I would choose.
Getting Started: What Governance Means for AI Code Generation
When a developer asks an AI tool for code, three things leave the building or change hands: the code and context you send, the access the tool is given, and the decision to accept what comes back. Governance is the set of controls around those three things. It is also what a security reviewer will ask about before the tool reaches more than a pilot team.
I turned that into six checks, and I used the same six on every tool:
- Identity and access: SSO, provisioning and roles.
- Data policy: retention, training and what can be excluded.
- Where it runs: the vendor cloud, your VPC, your data center or an airgap.
- Agent controls: what an agent can touch and who approves its output.
- Audit record: logs and events you can export.
- Certifications: what the vendor lists, and what that scope covers.
I read vendor docs and enterprise pages and did not test any of these in production. To keep the checks grounded, I will keep one hypothetical team in mind: a software group at a hospital network that must keep inference inside its own environment and show an auditor who approved each change. The details are invented. The constraints are common.
How the Six Tools Compare Across the Six Checks
The short version is that the deployment model separates the field faster than anything else. Two of the six, Factory and Tabnine, document a fully airgapped deployment. Augment sits in between, since its agents can run on your machines while the control plane stays with the vendor. Cursor and Copilot are cloud services. Forge is a hosted application whose Coding Agent works inside your own Git repository.
I got that count by reading each vendor's deployment pages, so treat it as a starting point for a security questionnaire and not a conclusion.
GitHub Copilot Governance Controls You May Already Have
GitHub groups its enterprise controls into policies, audit logs, agent management and MCP allowlists. Admins can block agentic features, review audit logs and set which MCP servers are allowed. If your organization already pays for Copilot, the first job is to see which of these are switched on.
The part I would read twice is content exclusion. It stops Copilot from using chosen files in suggestions, chat answers and code review, and GitHub's documentation lists its gaps plainly. It is not supported in Edit and Agent modes of Copilot Chat in Visual Studio Code and some other editors. It does not apply to symbolic links or repositories on remote filesystems. And Copilot may still use semantic information the IDE supplies indirectly, such as type information.
For the hospital team, that means "this path is excluded" needs a test in each editor developers use, not an assumption. For the cloud agent, GitHub documents write access to trigger it, one branch to push to, no ability to approve or merge, a restricted firewall, and signed commits with session logs.
Cursor Offers Strong Admin Controls but Only in the Cloud
According to Cursor's enterprise page, admins can enforce SSO, disable local login and provision users with SCIM. They can allowlist or blocklist repositories, models and MCP servers and set global agent run settings. With Privacy Mode on for the whole organization, code is never used for training, and Cursor says it holds zero data retention agreements with its model providers. Data is encrypted with AES-256 at rest and TLS 1.2 or higher in transit.
The same page says plainly that Cursor does not offer on-premises deployment today. For the hospital group, that ends the evaluation before the other controls come into play. For a team that accepts a vendor cloud, the list above is strong. Cursor's footer also lists ISO 27001, ISO 42001 and AIUC-1 alongside SOC 2 Type II, and I would ask for the reports behind each.
Augment Code Adds Key Custody and AI-Specific Certification
Augment's security page lists SOC 2 Type II attestation and ISO/IEC 42001 certification, which is a standard for AI management systems. It also describes customer-managed encryption keys, a Proof-of-Possession API so completions work only on code the requester holds locally, and a commitment not to train on customer proprietary data, backed by an indemnification clause in its terms.
Its Cosmos platform adds budget caps per automation or user, plus versioned, auditable Experts and environments. If your policy says you must hold the keys, this is the first tool I would put in front of the security team.
One caution I would pass on: an independent write-up of Augment's materials notes that its certifications do not explicitly test the no-training policy. Treat the certificate as one input and ask for the contract language.
Factory Has the Most Detailed Deployment Model I Read
Factory's enterprise docs describe managed settings in a hierarchy that covers model access, command policies, MCP allowlists, hooks, sandbox policy and retention. The deployment pages say code and files stay local unless the selected context goes to a model or gateway you configured. They offer cloud-managed, hybrid and fully airgapped patterns, and a separate EU deployment that keeps session content in Europe and sends EU inference only to EU-region endpoints. The factory lists SOC 2 Type II, ISO 27001 and ISO 42001.
For the hospital team, the airgapped pattern is the one to test: no outbound internet, models served from endpoints inside the network, and Factory's cloud not reachable at runtime. I would ask for a proof of concept that shows software and configuration arriving through your own artifact repositories.
Tabnine Spans the Widest Range of Deployment Options
Tabnine advertises SaaS, VPC, on-premises and air-gapped deployment, and zero code retention. That is the widest spread of any tool here, and for a team with a hard network boundary it earns a place on the shortlist.
I could not open Tabnine's own trust pages while preparing this piece, so I have not verified its identity controls or certifications. Ask for its certification reports directly instead of relying on a summary from me or anyone else.
Forge Adds Approval Governance Around Code Generation
Forge belongs on this list for a different reason from the other five. It is a governance layer around code generation and not a completion tool, so it answers some of the six checks differently. Its pipeline requires an explicit Approve & Continue on Intent, requirements, Architecture and User Stories, and the docs say regenerating an upstream artifact clears the approvals below it. Artifacts are versioned, and Forge can produce a Requirements Traceability Matrix that links requirements to stories.
That maps onto the hospital team's auditor question directly: who approved the requirement, and who approved the design, before any code was written. Opsera's pages also say every work order is traceable end to end, which is a vendor claim I have not tested.
For existing code, ForgeScore scores a repository across eight dimensions, including Trust Boundaries, System Gravity, Cognitive Load and Future-Proofing, and gives a NIST CSF 2.0 security grade from A to F. The docs say the grade is an input to human decisions and not a certification, and I would repeat that to any auditor.
Code generation happens in the Coding Agent, which works from approved stories in your linked repository and pushes a branch for a pull request, or in your own IDE over MCP. Opsera's software factory FAQ also describes a security agent that checks intent, requirements, architecture and stories as they are produced, which is where I would aim a pilot's questions. On the six checks, I found no certifications listed on the pages I read, so I asked for them.
How to Read a Compliance Badge Without Overreading It
A badge describes the vendor or the codebase, never the code the agent will write for you.
- SOC 2 Type II is an auditor's report on a vendor's controls over a period.
- ISO/IEC 42001 certifies an AI management system.
- Neither certifies that the code an agent writes is safe.
- A NIST CSF grade, like ForgeScore's, describes a codebase and not the vendor.
Ask for the report itself, check its scope, and match it to the product you will deploy.
How to Run the Six Checks as a Vendor Questionnaire
The checks are only useful if they turn into questions a vendor has to answer in writing. This is the version I would send to every tool on a shortlist, with one line per check:
- Identity and access: which SSO and provisioning methods are supported, and which roles can change agent settings?
- Data policy: what is retained, for how long, and is any of it used for training?
- Where it runs: which components run in my network, and what leaves it?
- Agent controls: what can an agent read, write or execute, and who approves the result?
- Audit record: which events can I export, and in what format?
- Certifications: can I see the report, its scope and its date?
Send the same six to each vendor and compare the answers side by side. The differences usually show up in the answers to the third and fourth questions, because those are the ones where a vendor has to say what its product does not do. For the hospital team, a clear "this does not run in your network" is more useful than a long feature list, since it saves a month of pilot work.
I would also keep the replies in one document. When an auditor asks six months later why a tool was chosen, a stored questionnaire with the vendor's own wording is a better answer than a memory of a demo.
How I Would Choose Based on Your Hard Constraint
If inference must stay inside your network, start with Factory or Tabnine and verify the claim in a proof of concept. If you must hold the encryption keys, look at Augment first.
If you are on GitHub and your gap is process and not tooling, set up Copilot's policies, audit log review and branch protection before you buy anything. And if auditors ask who approved a requirement and who approved the design, a pipeline with recorded approvals such as Forge answers a different question from any assistant on this list.
Where to Go From Here With Your Shortlist
The pattern across all six is that identity and data policy are mostly solved, deployment model is where the tools differ most, and approval records are the least developed area. Most of these vendors can show you who logged in and what was retained. Fewer can show you who approved the thing the agent built from.
My suggestion is to write down your one hard constraint, cut the list to two tools that meet it, and ask both for the same evidence pack: certification reports, a deployment diagram and a sample audit export. If approval records are on your list, the Forge documentation shows the stage and approval model in detail, so you can judge it before any call.
FAQ
Which AI code generation tool is the most compliant overall?
None of these pages supports a single ranking. Certifications differ in scope and in what they test, and some tools list none on the pages I read.
Does Privacy Mode or zero retention make a cloud tool safe for regulated code?
It limits retention and training. It does not change where processing happens, so a policy that requires an air gap still rules out cloud-only tools.
Is Forge a replacement for an AI coding assistant?
Forge is the lifecycle layer that works with the assistants your team already uses but not an AI coding agent itself.
Does a NIST CSF grade from ForgeScore count as certification?
No,The docs describe the A to F grade as an input to human decisions and not a certification, and it describes the codebase, not the vendor.
What should I ask for when a vendor lists SOC 2 Type II?
Ask for the report itself, check which systems and period it covers, and confirm that the product you will deploy is inside that scope.
Can Copilot's content exclusion fully hide a directory from the tool?
Not in every case. GitHub documents gaps in some editor modes, for symbolic links and remote filesystems, and for semantic information the IDE passes along.
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