Skip to main content
Yanked

This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Reason given by maintainers: Project renamed to pygovernor

svitch

DPDP-compliant AI data security for Indian enterprises.
Zero dependencies. Runs locally. Works with any LLM provider.

pip install svitch

PII Detection and Redaction

Detects and redacts Indian PII (Aadhaar, PAN, UPI, IFSC, mobile, GST, bank accounts) and global PII (email, IP) — entirely locally, no network calls.

import svitch

# Detect
entities = svitch.detect("Customer Aadhaar: 2345 6789 0123, PAN: ABCDE1234F")
# [Entity(type='AADHAAR', value='2345 6789 0123', ...), Entity(type='PAN', ...)]

# Redact (token replacement)
result = svitch.redact("Call me on 9876543210, UPI: rahul@okicici")
result.text   # "Call me on [MOBILE_IN], UPI: [UPI_ID]"
result.count  # 2
result.clean  # False

# Redact (partial mask)
result = svitch.redact("Aadhaar: 2345 6789 0123", replacement="mask")
result.text   # "Aadhaar: XXXX XXXX 0123"

Wrap any LLM client

PII is redacted from every prompt before it leaves your network, and from every response before it's stored or displayed.

import svitch, openai

client = svitch.wrap(openai.OpenAI())
# Use exactly like openai.OpenAI() — PII is handled automatically

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": "Assess loan for Aadhaar 9876 5432 1098, PAN ABCDE1234F"
        # ↑ redacted to [AADHAAR_IN], [PAN_IN] before reaching OpenAI
    }]
)

Anthropic:

import svitch, anthropic
client = svitch.wrap(anthropic.Anthropic())

Agent Audit Trail

Records every agent decision in an immutable, hash-chained audit log.
Required by DPDP §8 and the RBI FREE AI Framework.

from svitch_tracer import SvitchTracer

tracer = SvitchTracer(agent_id="loan-processor-v2")

with tracer.run() as run:
    run.data_access(
        source="crm",
        fields_accessed=["name", "income", "aadhaar"],
        purpose="loan_processing",
        data_principal_id="CUST-5821",
    )

    run.llm_call(
        provider="openai",
        model="gpt-4o",
        prompt="Assess eligibility for [AADHAAR_IN] applicant",   # already redacted
        response="Eligible. Score: 72/100.",
        redact_pii=True,
    )

    run.decision(
        reason="Score above threshold (70)",
        outcome="approve",
        confidence=0.87,
    )

    # Human-in-the-loop checkpoint — required for high-risk decisions
    run.human_checkpoint(
        question="Approve ₹5L loan for this applicant?",
        approved=True,
        reviewer_id="anand.k",
    )

# Verify the hash chain is intact
valid, err = run.verify()
assert valid, f"Audit chain broken: {err}"

Configuration

export SVITCH_TRACER_URL=https://agent-tracer.vercel.app  # default (hosted)
# or point to your self-hosted Agent Tracer

Supported PII types

Type Pattern
AADHAAR 12-digit, masked and unmasked
PAN ABCDE1234F format
UPI_ID handle@provider
IFSC 11-character bank code
MOBILE_IN 6xxx–9xxx Indian mobile
BANK_ACCOUNT 9–18 digit account numbers
GST 22AAAAA0000A1Z5
EMAIL RFC 5322
IPV4 IPv4 addresses

License

Apache 2.0 — svitch.ai · DPDP Guide · GitHub

Release files for svitch 0.1.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for svitch 0.1.5
File Size Uploaded
svitch-0.1.5.tar.gz 20.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for svitch 0.1.5
File Interpreter ABI Platform
svitch-0.1.5-py3-none-any.whl Python 3 none any Details

Total release size: 44.9 kB

Release files / svitch-0.1.5.tar.gz

Download URL svitch-0.1.5.tar.gz
Size 20.4 kB
Tags Source
SHA-256 checksum
How to use checksums
f643ca0a1b47e72955fb39b896a69e7e05621f8a501b8a1acb6b9d0dd89d57a3
BLAKE2b-256 checksum
How to use checksums
78b183340591645a4e60560b1885ce9aa866548ddb2452010de5fdcb942440af
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 19, 2026.

Transparency log

Release files / svitch-0.1.5-py3-none-any.whl

Download URL svitch-0.1.5-py3-none-any.whl
Size 24.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
447d44bba9d4d33a2b97326e94eee75c0527c5465bfc6b94633ffff3c39471c9
BLAKE2b-256 checksum
How to use checksums
060577e913d8d467f0ab639e598c343fb32d57839ec52079e31033baa3d6b8c8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 19, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.1.5 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page