AI privacy checks for GitHub pull requests

Stop customer data from leaking into AI tools before merge.

SafeCommit reviews pull requests and blocks high-risk flows where customer data may reach OpenAI, Claude, logs or third-party APIs before code reaches production.

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GitHub AppLLM leakage detectionPII in logsPR blockingAudit evidenceGDPR risk reductionEU AI Act readinessAI Governance

Pull request · SafeCommit review

Required check

safecommit bot commented

src/ai/support-reply.ts · line 40

blocks merge

38 const reply = await openai.chat.completions.create({

39 messages: [{ role: "user",

40 - content: customerTicket.body

40 + content: redactPII(customerTicket.body)

41 }]

42 })

Raw customer data may be sent to an external AI provider

customerTicket.body can contain names, emails, account IDs or sensitive business context. SafeCommit recommends redaction before the provider call.

Fix

Add redaction before the provider call.

Policy

Raw customer data to LLMs is blocked.

Result

Risk fixed before production.

Review completed in 18s · estimated fix time 22 minutes

AI data leakage

Catch customer records before they are sent to LLM providers.

PII in logs

Detect emails, IPs and identifiers in observability payloads.

Vendor APIs

Flag risky data sent to analytics, CRM and external services.

Before merge

Fix the issue while the pull request context is still fresh.

Why now

AI features are creating new privacy risks inside the code review process.

SafeCommit does not promise legal compliance. It helps engineering teams reduce GDPR, EU AI Act and internal AI governance risks by catching risky data flows before production.

GDPR

Identify personal data flows to logs, vendors and AI providers before they ship.

EU AI Act

Create practical engineering controls around AI features and external model providers.

AI Governance

Keep evidence of findings, fixes and exceptions directly from pull request reviews.

Workflow

Privacy guardrails developers can actually use.

SafeCommit runs where engineers already decide what ships: the pull request. Security and compliance teams get evidence without becoming manual reviewers.

Connect or submit a PR

Start with a sample diff, one repository or a limited GitHub beta install.

Analyze changed code

SafeCommit focuses on new risky data flows instead of dumping legacy full-repo noise.

Block high-risk leaks

Only high-confidence AI and PII findings should block merge. Lower-confidence items warn.

Keep audit evidence

Track findings, fixes, exceptions and policies for security and compliance reviews.

Examples

Concrete PR findings, not generic compliance advice.

Each finding explains the risky data flow, the affected code and the suggested fix.

Raw customer data → LLM

blocks merge

const reply = await openai.chat.completions.create({
  messages: [{ role: "user", content: customerTicket.body }]
})

Detects when support tickets, messages or CRM records may be sent to OpenAI, Anthropic or other AI providers without redaction.

PII in logs

high signal

logger.info("signup", {
  email: user.email,
  ip: request.ip
})

Flags emails, IP addresses, account IDs and other personal data before it enters observability pipelines.

Customer data → vendors

policy risk

analytics.track("payment_failed", {
  customerEmail,
  invoiceId
})

Warns when sensitive identifiers are forwarded to analytics, CRM, enrichment or external API services.

Who buys

Built for teams shipping AI features with customer data.

SafeCommit helps engineering move fast while giving CTOs, security and compliance a reliable control point before production.

CTOs

Reduce AI privacy risk without adding another manual review process.

VPs of Engineering

Roll out practical guardrails across repositories and teams.

Tech Leads

Fix risky data flows while the PR context is still fresh.

Security & Compliance

Get evidence of checks, fixes and policy exceptions.

Integrations

Start in GitHub. Expand across engineering workflows.

GitHub PR checks are the wedge. Slack summaries and Jira workflows become the next layer once the PR workflow proves value.

GitHub

Beta focus

PR comments, required checks, repository badges and merge blocking for high-risk findings.

Slack

Coming soon

Private alerts and weekly summaries for security, compliance and engineering leads.

Jira

Coming soon

Convert risky findings into tickets and track policy exceptions through remediation.

Languages

Focused on common SaaS, AI and regulated product stacks.

The beta starts with high-signal checks for modern backend and product code, then expands by language and framework based on real customer repositories.

TypeScript

Node, Next.js

JavaScript

Express, React

Python

Django, FastAPI

Java

Spring

APIs, workers

Ruby

Rails

Elixir

Phoenix

Erlang

OTP systems

Rust

Services & infra

.NET

PHP

Laravel, Symfony

Kotlin

JVM services

Swift

iOS apps

C++

C/C++

Native systems

Scala

Data & backend

Audit-first sales motion

Start with one PR privacy audit.

Send a sample PR or repository pattern. SafeCommit returns a concise report showing which AI, logging and vendor API data flows would be blocked, warned or ignored.

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Sample audit output

Raw data to LLMblocked

PII in logswarning

Estimated fix time22m

Recommended policyBlock raw LLM data

Packages

Start with evidence. Expand to continuous PR protection.

No public pricing while the beta is limited. Choose a scope based on repository access, rollout needs and compliance requirements.

Starter

A focused review of one repository or selected pull requests to find real AI and privacy risks before a broader rollout.

Start here

✓Sample PR or repository review
✓AI provider and logging leakage checks
✓Concise risk report with code examples
✓Recommended blocking policy
✓Founder-led walkthrough call

Recommended

Team

Continuous GitHub PR checks for engineering teams that want to stop risky data handling before merge.

Recommended

✓GitHub PR comments and status checks
✓High-confidence blocking for AI/PII leaks
✓Unlimited PR reviews under fair use
✓Slack alerts for high-risk findings
✓Weekly risk summary and audit trail

Enterprise

For regulated teams that need custom rules, policy exceptions, audit evidence and private deployment options.

Custom rollout

✓Multiple repositories and teams
✓Custom policies and vendor allowlists
✓SSO and role-based access roadmap
✓Longer audit retention and exports
✓Private deployment discussion