AI coding compliance is not only about whether a model is approved.
For software teams, the compliance question is more operational: can you prove what work was approved, what the agent accessed, what changed, what checks ran, who reviewed it, and what reached production?
This checklist helps security, platform, and engineering leaders evaluate AI coding workflows before scaling them across teams.
1. Approved Work Intake
Check whether AI coding starts from approved work.
Evidence to require:
- source ticket or issue link
- requester identity
- owning team
- approval status
- acceptance criteria
- target repository
- risk classification
Compliance risk increases when agents begin from informal prompts with no connection to the planning system.
MergeLoom’s work intake integrations help preserve the link between approved work and AI coding runs.
2. Repository Access
Check whether repository access is scoped.
Controls to verify:
- approved repository list
- least-privilege credentials
- separate read and write paths where practical
- no direct writes to protected branches
- PR/MR handoff for review
- access review cadence
- logs showing which repositories each run touched
Agents should not inherit broad access just because a user can reach many repositories.
3. Data and Secret Handling
Check whether sensitive data can reach prompts, logs, or command output.
Controls to verify:
- production secrets are not passed to agents
- environment variables are restricted
- command output is redacted where needed
- short-lived credentials are used where practical
- logs avoid raw secret-bearing output
- customer or regulated data is excluded unless explicitly approved
For teams with strict boundaries, customer-hosted execution may be required. Review MergeLoom’s Self Hosted AI coding infrastructure for that deployment pattern.
4. Context Governance
Check whether context sources are approved and recorded.
Evidence to require:
- repository rules used by the run
- documentation sources
- related repositories used for context
- stale or missing context warnings
- untrusted task content separated from system rules
- sensitive sources excluded by policy
Context is both a quality control and a compliance control. If you cannot see what context was used, reviewers and auditors have to guess.
MergeLoom’s Context Engine attaches approved context to runs.
5. Validation Before PR/MR
Check whether AI-generated code is validated before review handoff.
Controls to verify:
- format, lint, typecheck, test, and build commands
- repository-specific validation configuration
- custom policy checks
- diff scope checks
- logged validation output
- clear handling of skipped checks
CI after PR/MR creation still matters. But compliance evidence is stronger when pre-review validation is part of the run record.
For implementation detail, see AI code validation before PR.
6. Repair and Stop Conditions
Check whether repair loops are bounded and recorded.
Controls to verify:
- maximum repair attempts
- repair reasons logged
- files changed during repair visible
- validation rerun after repair
- stop conditions for repeated failure
- stop conditions for missing context or risky scope
Unbounded repair can turn a small ticket into a large unknown change. Compliance needs limits.
7. Human Review and Approval
Check whether human review stays mandatory.
Controls to verify:
- normal GitHub, GitLab, or Azure Repos review flow
- branch protection
- CODEOWNERS or reviewer routing
- required status checks
- no agent self-approval
- merge controlled by approved humans or existing release automation
AI can prepare a change. Humans should still approve the PR/MR.
8. Audit Trail Completeness
Check whether the run can be reconstructed later.
Evidence to require:
- source ticket
- requester
- repository and branch
- context sources
- commands run
- validation and repair output
- files changed
- PR/MR link
- reviewers and approvals
- merge result
MergeLoom’s audit trails and attribution are focused on this delivery-layer evidence.
9. Cost and Outcome Evidence
Compliance and finance teams may also need cost visibility.
Track:
- runs started
- runs stopped
- PRs/MRs opened
- PRs/MRs accepted
- provider and context costs
- repair costs
- cost per accepted outcome
This helps teams avoid paying for repeated failed automation while reporting AI coding usage accurately.
10. Vendor and Deployment Review
Before expanding, review:
- data processing boundaries
- cloud versus self-hosted execution
- identity and access model
- log retention
- audit export
- provider configuration
- incident response process
For vendor questions, use the AI coding vendor evaluation checklist.
11. Operating Review Cadence
Compliance should not be a one-time approval before launch. AI coding workflows change as repositories, teams, providers, validation commands, and work types change.
Set a recurring review with security, platform, and engineering leadership.
Review:
- new repositories added to the approved list
- runs stopped by policy
- validation failures by category
- audit records with missing evidence
- review exceptions or emergency merges
- cost patterns by accepted outcome
- policy changes requested by teams
This cadence keeps compliance tied to how the workflow is actually used. It also gives platform teams a clean way to expand adoption when evidence supports it and tighten controls when repeated failures appear.
Where MergeLoom Fits
MergeLoom gives teams a governed AI coding path from approved work to validated PR/MR, with audit trails, attribution, human review, and outcome-focused cost visibility.
To review your current compliance posture, start with AI Coding Risk Management or book a demo.