pyveil: PII and secret redaction for Python AI agents

Stop sensitive data before it reaches an LLM, tool, MCP resource, memory store, log, or trace.

Documentation · PyPI · Cookbook · Detection reference · Security

pyveil is local, dependency-free redaction middleware for LLM applications and AI agents. It replaces high-confidence PII and credentials with deterministic, scoped HMAC placeholders before data crosses an application boundary.

No network calls. No reversible vault. No raw values in findings by default.

Try It

pip install pyveil
pyveil demo

Or run the synthetic demo in an isolated environment:

uvx pyveil demo

before: Email alice@example.com, call 010-1234-5678, and use API key sk-proj-...
after:  Email [EMAIL:...], call [PHONE:...], and use API key [API_KEY:...]
found:  API_KEY, EMAIL, PHONE

Protect An LLM Call

Put pyveil immediately before the provider call. The same code works with OpenAI, Azure OpenAI, Anthropic, Gemini, LiteLLM, or an internal gateway.

from pyveil import Channel, Veil

veil = Veil.high(
    secret=b"tenant-or-run-secret",
    scope="tenant/session",
)

messages = [
    {"role": "user", "content": "Email alice@example.com about my account."},
]

safe = veil.redact_data(messages, channel=Channel.PROMPT_INPUT)
response = call_llm(safe.data)  # Your provider SDK call

The provider receives the same list and dictionary shape, with sensitive values replaced before serialization or transmission.

Why pyveil

Known Names And Domain IDs

Regex cannot discover arbitrary names or addresses. When your application already knows a value is sensitive, teach that value to pyveil without adding an NER model:

from pyveil import CustomRule, Veil

rules = [
    CustomRule.exact("PERSON", ["Alice Kim", "Hong Gildong"]),
    CustomRule("CUSTOMER_ID", r"\bCUS-[A-Z0-9]{8}\b", rule_id="customer_id"),
]

veil = Veil.high(
    secret=b"tenant-secret",
    scope="tenant/session",
    rules=rules,
)

result = veil.redact_text("Alice Kim owns CUS-A1B2C3D4.")
print(result.text)
# [PERSON:...] owns [CUSTOMER_ID:...].

Custom patterns are trusted application code. Keep them narrow and test them against realistic positive and negative samples.

Agent Boundaries

Classic masking often stops at text input. Agents move data across several surfaces, so channels are first-class policy inputs:

user / retrieval / tool / resource data
                    |
                  pyveil
                    |
model / tool / MCP / memory / trace / log boundary

Detection Board

Core detection is intentionally conservative and high precision:

See the full redaction reference for examples, validation rules, LOW masking output, and limitations.

HIGH And LOW

Use HIGH at model, agent, tool, MCP, memory, trace, and external log boundaries. It produces stable HMAC placeholders such as [EMAIL:a13f7c91b0d2].

Use LOW only for human-facing previews where preserving shape is useful:

alice@example.com  -> al***@e******.com
010-1234-5678      -> 010-****-5678
4242 4242 4242 4242 -> **** **** **** 4242

Credential-like values remain aggressively hidden in both levels.

Policy

The default high policy redacts supported findings and blocks credentials in tool.call.arguments:

from pyveil import Action, Channel, Entity, Policy, Veil

policy = Policy.default_high().override(
    Channel.PROMPT_INPUT,
    Entity.EMAIL,
    Action.PASS,
)

veil = Veil.high(secret=b"tenant-secret", policy=policy)

When both policy and level are supplied, the explicit policy decides channel levels and actions. Build one Veil per tenant, session, or run and reuse it.

CLI

Use stdin for shell pipelines, files for preflight checks, and JSON output for structured automation:

export PYVEIL_SECRET="tenant-or-run-secret"
export PYVEIL_SCOPE="tenant/session"

printf 'Email alice@example.com' | pyveil redact -
pyveil redact request.json --channel tool.call.result --format json
pyveil scan prompt.txt --format json
pyveil init
pyveil test-config pyveil.yaml

scan emits finding metadata without raw sensitive values. JSON-shaped input is parsed and traversed structurally.

Integration Recipes

The cookbook covers prompts, tools, MCP, memory, logging, tracing, JSON, and CLI workflows.

Pick The Right Tool

Choose pyveil when you want a small local boundary filter for structured PII, credentials, known values, and domain identifiers across agent context flows.

Choose Presidio, GLiNER, or another NER-backed system when you need broad semantic discovery of unknown people, organizations, locations, or addresses. Choose an enterprise DLP product when you need managed policy, document/image coverage, incident workflows, or compliance reporting.

These tools can be layered. pyveil does not claim perfect recall.

Safety Contract

• Raw sensitive values are not stored in Finding objects by default.
• Placeholders use HMAC-SHA256 with a caller-provided secret and scope.
• Credential-like values can be blocked before model-controlled tools execute.
• max_input_chars bounds work performed on text and structured payloads.
• The core makes no network calls and has no required third-party dependency.
• pyveil has no reversible vault or unmasking API.

pyveil is not a compliance guarantee, enterprise DLP system, secret-scanning replacement, or prompt-injection firewall. Read the threat model, known limitations, and security policy before production use.

Guides

• Complete manual
• English video guide
• Korean video guide
• AGENTS.md for coding agents
• llms.txt for LLM-readable navigation
• Contributing

Development

uv run --extra dev ruff check .
uv run --extra dev mypy pyveil tests
uv run --extra test pytest

CI runs the test suite on Python 3.8 through 3.14. The core remains typed, dependency-free, and MIT licensed.