Security

Security guide for using intake in corporate and regulated environments. Covers the threat model, secret management, sensitive data redaction, offline mode, and compliance considerations.

Threat model

Attack surface

Component	Risk	Mitigation
LLM calls	Requirement data is sent to the LLM provider	`redact_patterns` to remove sensitive data; local models for air-gapped environments
Source files	Sources may contain secrets, PII, regulated data	`redact_files` excludes sensitive files; redaction before sending
Connector credentials	API tokens for Jira/Confluence/GitHub	Environment variables, never in `.intake.yaml`; transparent rotation
acceptance.yaml	Checks of type `command` execute arbitrary shell commands	Review checks before running `intake verify`; do not run specs from untrusted sources
Plugin system	External plugins execute Python code	Only install plugins from trusted sources; `intake plugins check` to validate
spec.lock.yaml	Contains hashes and metadata of the sources	Only partial hashes (16 hex chars), not content; safe to commit

Data flow: what is sent to the LLM

Requirement sources
        |
   [ REDACTION ]              <-- security.redact_patterns (regex)
   [ EXCLUSION ]              <-- security.redact_files (glob)
        |
   Clean text + metadata
        |
   [ SENT TO LLM ]           <-- Only init, add, feedback
        |
   JSON response (requirements, design, tasks)
        |
   [ LOCAL GENERATION ]       <-- Everything else is local
        |
   Spec files (Markdown/YAML)

Sent to the LLM:

Text extracted from sources (after redaction)
Tech stack information (auto-detected)
Configured language

NOT sent to the LLM:

Project source code
API keys or credentials
Content of files excluded by redact_files
Verification results (except in feedback)

Remains 100% local:

intake verify — check execution
intake export — file generation
intake show, list, diff — file reading
intake doctor — environment checks
intake task — state management
intake plugins — plugin listing

Secret management

LLM API keys

API keys are always read from environment variables. They are never stored in .intake.yaml:

# .intake.yaml — only the variable NAME, not the value
llm:
  api_key_env: ANTHROPIC_API_KEY

# Set the environment variable
export ANTHROPIC_API_KEY=sk-ant-api03-...

Recommended pattern for teams:

# .env (in .gitignore — never commit)
ANTHROPIC_API_KEY=sk-ant-api03-your-real-key

# .env.example (commit as reference)
ANTHROPIC_API_KEY=sk-ant-api03-REPLACE

Connector tokens

Same pattern for Jira, Confluence, and GitHub:

# .env
JIRA_API_TOKEN=your-jira-token
JIRA_EMAIL=dev@company.com
CONFLUENCE_API_TOKEN=your-confluence-token
CONFLUENCE_EMAIL=dev@company.com
GITHUB_TOKEN=ghp_your-personal-access-token

Variable names are configurable in .intake.yaml via connectors.*.token_env and connectors.*.email_env.

Credential rotation

intake does not cache tokens between executions. When rotating a credential:

Update the environment variable value
The next intake execution will automatically use the new value

Secrets in CI/CD

Platform	Mechanism	Example
GitHub Actions	`secrets.*`	`${{ secrets.ANTHROPIC_API_KEY }}`
GitLab CI	CI/CD Variables (masked)	`$ANTHROPIC_API_KEY`
Jenkins	Credentials plugin	`withCredentials([string(...)])`
Azure DevOps	Variable groups (secret)	`$(ANTHROPIC_API_KEY)`

See CI/CD Integration for complete examples.

Sensitive data redaction

Redaction patterns (redact_patterns)

Patterns are regex applied to the source text before sending it to the LLM:

security:
  redact_patterns:
    # API keys and tokens
    - "sk-[a-zA-Z0-9]{20,}"                    # Anthropic API keys
    - "sk-proj-[a-zA-Z0-9]{20,}"               # OpenAI project keys
    - "ghp_[a-zA-Z0-9]{36}"                     # GitHub PATs
    - "xoxb-[a-zA-Z0-9-]+"                      # Slack bot tokens
    - "AKIA[0-9A-Z]{16}"                         # AWS access keys

    # Financial data
    - "\\b\\d{4}[- ]?\\d{4}[- ]?\\d{4}[- ]?\\d{4}\\b"  # Card numbers
    - "\\b\\d{3}-\\d{2}-\\d{4}\\b"              # SSN (US)

    # Credentials in text
    - "password\\s*[:=]\\s*['\"]?\\S+"           # Passwords in configs
    - "secret\\s*[:=]\\s*['\"]?\\S+"             # Secrets in configs
    - "mongodb(\\+srv)?://[^\\s]+"               # MongoDB connection strings
    - "postgres(ql)?://[^\\s]+"                   # PostgreSQL connection strings
    - "mysql://[^\\s]+"                           # MySQL connection strings

    # Internal data
    - "\\b\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\b"  # Internal IPs

File exclusion (redact_files)

Files that intake will never process as sources:

security:
  redact_files:
    # Defaults
    - "*.env"
    - "*.pem"
    - "*.key"

    # Recommended for enterprise
    - "credentials.*"
    - "secrets.*"
    - "*.pfx"
    - "*.p12"
    - "*.jks"
    - "docker-compose.override.yml"
    - ".env.*"
    - "*.secret"

Verify the redaction

To verify that patterns are working correctly:

Run with --dry-run to see which sources would be processed
Review spec.lock.yaml to confirm which files were processed (only hashes, not content)
Use --preset minimal with a test source to see the output

Offline / air-gapped mode

Commands by connection mode

Command	Requires internet	Offline alternative
`intake init`	Yes (LLM)	Local model (Ollama, vLLM)
`intake add`	Yes (LLM)	Local model
`intake feedback`	Yes (LLM)	Local model
`intake verify`	No	Works offline
`intake export`	No	Works offline
`intake show`	No	Works offline
`intake list`	No	Works offline
`intake diff`	No	Works offline
`intake doctor`	No	Works offline
`intake task`	No	Works offline
`intake plugins`	No	Works offline
API connectors	Yes (external APIs)	Not available offline

Local models for air-gapped environments

# .intake.yaml for Ollama
llm:
  model: ollama/llama3
  api_key_env: DUMMY_KEY    # Ollama does not need a key
  timeout: 300              # Local models can be slower

export DUMMY_KEY=not-needed

# Verify that Ollama is running
ollama list

# Generate spec with local model
intake init "Feature" -s reqs.md

Supported local models (via LiteLLM):

Framework	Model config	Example
Ollama	`ollama/<model>`	`ollama/llama3`, `ollama/mistral`
vLLM	`vllm/<model>`	`vllm/meta-llama/Llama-3-8b`
Local OpenAI-compatible	`openai/<model>` + `api_base`	Any OpenAI-compatible server

Considerations:

Extraction quality depends on the model; larger models produce better specs
Local models can be significantly slower
Adjust timeout in the configuration for slow models

Pattern: pre-generate specs for teams without LLM access

If only one person has LLM access:

# Person with LLM access generates the specs
intake init "Feature" -s reqs.md
git add specs/ && git commit -m "Add feature spec"
git push

# Team without LLM uses verify, export, task (all offline)
git pull
intake verify specs/feature/ -p .
intake export specs/feature/ -f claude-code -o .
intake task list specs/feature/

Auditing and traceability

spec.lock.yaml as an audit artifact

Each spec includes a spec.lock.yaml that records:

Field	Contents	Audit value
`created_at`	ISO timestamp	When the spec was generated
`model`	LLM model used	Reproducibility
`config_hash`	Hash of the configuration	Parameter consistency
`source_hashes`	SHA-256 of each source (16 hex)	Source integrity
`spec_hashes`	SHA-256 of each spec file	Output integrity
`total_cost`	Cost in USD	Expense tracking
`requirement_count`	Number of requirements	Metrics
`task_count`	Number of tasks	Metrics

Pattern: verify source integrity

If sources change after the spec is generated, spec.lock.yaml detects it:

intake show specs/feature/
# If sources changed, shows staleness warning

Change history via git

Specs are text files ideal for versioning:

# Compare versions
intake diff specs/feature-v1/ specs/feature-v2/

# Change history
git log --oneline specs/feature/

# Who changed what
git blame specs/feature/requirements.md

Bidirectional traceability

intake provides complete requirement-to-source traceability:

Original source (reqs.md, jira.json)
    ↓ (recorded in sources.md)
Requirement (FR-001 in requirements.md)
    ↓ (referenced in tasks.md)
Task (Task 1: implement FR-001)
    ↓ (checks in acceptance.yaml)
Verification (check-01: tests pass)
    ↓ (results in verify report)
Compliance evidence

The sources.md file maps each requirement to its original source, providing the traceability that audits require.

Compliance considerations

What intake provides

Capability	Description
Requirement-to-source traceability	`sources.md` maps each requirement to its origin
Immutable audit artifact	`spec.lock.yaml` with hashes and timestamps
Automated verification	`acceptance.yaml` with executable checks
Sensitive data redaction	Configurable `redact_patterns` and `redact_files`
Offline mode	Everything except init/add/feedback works without internet
Local models	Support for Ollama, vLLM (data never leaves the network)
Versioning	Specs as text files, ideal for git
Reproducibility	`spec.lock.yaml` records configuration, model, and costs

What intake does NOT provide

Aspect	Status
Data encryption in transit	Depends on the LLM provider (HTTPS)
Access control for specs	Depends on the file system / git
Access logs	Does not generate logs of who accesses the specs
Compliance certification	intake is not a certified tool
Data encryption at rest	Depends on the file system
Automatic data retention	Has no retention policies; files persist until deleted

Recommendations by framework

Framework	Recommendations with intake
SOC2	Use `redact_patterns` + commit `spec.lock.yaml` + maintain git history of specs + JUnit in CI as evidence
HIPAA	Local model (air-gapped) + aggressive PHI redaction + never use connectors with patient data + manually review specs
ISO 27001	Document intake in asset inventory + use `enterprise` preset + enable `generate_lock` + redact internal IPs and hostnames
GDPR	Redact PII from sources before processing + local model if data cannot leave the jurisdiction + document legal basis for processing