gitaiflow — Security and Privacy · gitaiflow

gitaiflow is designed around a local-first execution model. Git discovery, diff generation, filtering, artifact creation, usage accounting, and provider configuration happen locally. The main exter...

Security and Privacy Architecture

flowchart TD
    A["🔐 Sensitive Repository Data"]

    A --> B["🛡️ Protection Before AI Request"]

    B --> C["File Exclusions<br/><small>.env · certificates · generated artifacts<br/>binary / generated files</small>"]
    B --> D["Secret Redaction<br/><small>SECRET_KEY · API_KEY · PRIVATE_KEY<br/>ACCESS_KEY · CLIENT_SECRET</small>"]

    C --> E["📤 Filtered Diff"]
    D --> E

    E --> F{"Where is the AI request sent?"}

    F -->|Local endpoint| G["🖥️ Local AI<br/><small>Ollama / vLLM / LM Studio</small>"]
    F -->|Cloud endpoint| H["☁️ External AI Provider<br/><small>Gemini · OpenAI · OpenRouter<br/>Grok · DeepSeek · Custom</small>"]

    H --> I["⚠️ Provider Data Boundary<br/><small>Provider controls retention,<br/>logging and data-use policies</small>"]

    J["🔑 API Credentials<br/><small>Environment variables / .env</small>"]
    J --> K["AI Client"]
    K --> F

    L["📁 Local Artifacts<br/><small>Diff · JSON · Markdown</small>"]
    E --> L

    M["📊 Local Usage Log<br/><small>Operational metadata only</small>"]

    M --> N{"Telemetry enabled?"}

    N -->|No| O["🚫 No Telemetry Transmission"]
    N -->|Yes| P["📡 Anonymous Telemetry<br/><small>Version · provider · model · counts<br/>duration · OS · success/failure</small>"]

    P --> Q["🌐 Telemetry Endpoint"]

    R["🚫 Never included in telemetry<br/><small>Repository · paths · diff · branch<br/>author · commit message · summary</small>"]
    R -.-> P

    S["⚠️ Best-Effort Protection<br/><small>Redaction is not a complete secret scanner</small>"]
    S -.-> D

    classDef sensitive fill:#fff0f0,stroke:#d64545,stroke-width:2px,color:#5c2020;
    classDef security fill:#fff4df,stroke:#d59a2a,stroke-width:2px,color:#5c420b;
    classDef local fill:#e8f7f1,stroke:#2b9a78,stroke-width:2px,color:#174d3d;
    classDef external fill:#eeeaff,stroke:#7957c7,stroke-width:2px,color:#382568;
    classDef safe fill:#e8f1ff,stroke:#4a78c2,stroke-width:2px,color:#172b4d;
    classDef warning fill:#fff8e8,stroke:#c58a18,stroke-width:2px,color:#654800;

    class A sensitive;
    class B,C,D,J,S security;
    class E,G,L,M,O local;
    class H,I,P,Q external;
    class K,N,R safe;

Data Flow

1. Local Git Data

gitaiflow reads the selected repository and obtains:

changed files
diff content
repository name
current branch
resolved base reference
Git author
change timestamps
file status

These values are collected locally.

2. Diff Filtering and Redaction

Before a normal AI request, the generated diff is filtered.

The implementation excludes sensitive/environmental material including:

text

.env
.env.env.bak
.env.env.decrypted.bak
.certs
change-summary

It also skips configured binary/generated file types such as Markdown, fonts, images, minified assets, and packages.

Sensitive-line matching covers names such as:

text

SECRET_KEY
API_KEY
PRIVATE_KEY
ACCESS_KEY
CLIENT_SECRET

and their lowercase forms. Matching is deliberately pattern-based and only redacts lines that match the implementation's sensitive-line heuristic.

Important: this is best-effort redaction, not a complete secret scanner. A repository can contain credentials under other names or in formats that do not match these patterns. Review the generated diff before sending sensitive code to a third-party provider.

3. AI Provider Boundary

AIClient sends a JSON chat-completions request containing:

the generated system prompt
the filtered/redacted diff
configured model parameters

The destination is determined by AIConfig.

Possible destinations include:

Gemini's OpenAI-compatible API
OpenAI
OpenRouter
Grok
DeepSeek
Ollama
vLLM
LM Studio
another OpenAI-compatible endpoint configured by the user

For cloud providers, the provider can therefore process the repository diff and prompt content. gitaiflow cannot control provider-side retention, logging, training, billing, or privacy policies.

For local providers such as Ollama, the request can remain within the local environment.

Credentials

AI credentials are supplied through environment variables or a .env file discovered from the current directory upward to the repository root.

Typical configuration:

text

AI_API_KEY=<secret>
AI_BASE_URL=<endpoint>
AI_MODEL=<model>

The .env file should not be committed to source control.

gitaiflow does not put the configured API key into the generated JSON or Markdown artifacts.

Local Artifacts

Normal runs create:

text

change-summary/
└── <YYYY>/<MM>/<DD>/
    ├── diff/
    ├── json/
    └── markdown/

The JSON payload intentionally contains Git/application metadata and the generated commit summary. It can include:

repository name
branch
base reference
Git author name/email
changed file paths and statuses
selected provider/model
generated commit title/body

Because these artifacts are local files, their confidentiality depends on the permissions and storage security of the user's machine and repository workspace.

The intermediate diff/ artifacts are also local, but they contain the filtered diff used for the current run. They should be treated as potentially sensitive even after redaction.

Local Usage Log

Usage accounting is stored locally at:

text

~/.gitaiflow/usage.jsonl

The local usage record includes operational fields such as:

timestamp
repository name
target type
provider
model
changed-file count
estimated input/output tokens
duration
success/failure

This local log is not transmitted unless the separate telemetry path is enabled.

Telemetry

Telemetry is disabled unless the user opts in.

Consent can be controlled with:

bash

gitaiflow --telemetry enable
gitaiflow --telemetry disable
gitaiflow --telemetry status
gitaiflow --telemetry history

For a single process/session, GITAIFLOW_TELEMETRY=true|false overrides the stored decision without changing the saved consent.

When telemetry is enabled, the transmitted run payload contains only the documented operational fields:

install ID
event type
timestamp
gitaiflow version
AI provider
model name
target type
changed-file count
estimated input/output token counts
duration
success/failure
operating-system name

The telemetry payload does not contain:

repository path or repository name
file paths
diff contents
generated summary text
Git author
Git branch
commit message

Telemetry uses a short request timeout and failures are non-fatal to the main gitaiflow run.

The durable consent record is stored separately from the local usage directory:

text

~/.gitaiflow_telemetry_consent

This prevents deleting ~/.gitaiflow from unintentionally changing the telemetry decision.

The consent history records the answer, gitaiflow version, and timestamp.

Privacy Boundary

The strongest privacy boundary is the choice of AI provider:

text

Local repository
      │
      ├── local-only processing ──→ Ollama / local compatible endpoint
      │
      └── external processing ────→ configured cloud AI provider

Even with redaction and telemetry disabled, a cloud AI provider still receives the diff and prompt necessary to generate the requested summary. Users should select a provider whose data-handling policy is appropriate for the repository being analyzed.

Security Recommendations

Never commit .env files or API credentials.
Treat change-summary/diff/ and generated JSON/Markdown artifacts as potentially sensitive.
Review redaction-sensitive diffs before sending proprietary or regulated code to a cloud provider.
Prefer a local provider when repository contents must remain inside the local environment.
Keep telemetry disabled when anonymous operational reporting is not desired.
Restrict filesystem access to repositories and generated artifacts according to the sensitivity of the source code.

gitaiflow — Security and Privacy

#Security and Privacy Architecture

#Data Flow

#1. Local Git Data

#2. Diff Filtering and Redaction

#3. AI Provider Boundary

#Credentials

#Local Artifacts

#Local Usage Log

#Telemetry

#Consent Persistence

#Privacy Boundary

#Security Recommendations

Security and Privacy Architecture

Data Flow

1. Local Git Data

2. Diff Filtering and Redaction

3. AI Provider Boundary

Credentials

Local Artifacts

Local Usage Log

Telemetry

Consent Persistence

Privacy Boundary

Security Recommendations