armos 1.0.0

Automatic PII masking for OpenAI and Anthropic SDKs

Armos

PII never reaches your LLM. One line of code.

Armos wraps the OpenAI and Anthropic SDKs to automatically detect and mask personally identifiable information (PII) before it leaves your server — and restore the real values in the response. Your application code changes by exactly one word.

---

The problem

Every time your application calls an LLM, it sends raw text to a third-party server. If a user's message contains their name, Aadhaar number, email, PAN card, or credit card — that data leaves your infrastructure.

This matters for:

• Healthcare apps — patient names, dates of birth, medical IDs
• Fintech apps — PAN, Aadhaar, bank details
• Customer support tools — names, emails, phone numbers, addresses
• Any app where users type free text that gets sent to OpenAI or Anthropic

Most teams know this is a risk. Few have time to build a proper masking layer before shipping. Armos is that layer, pre-built.

---

How it works

Detection runs entirely on your machine. Presidio + spaCy analyse the text locally. No data is sent to any Armos server — there is no Armos server. The vault (token ↔ real value map) lives in your process memory, or optionally in your own Redis instance.

---

Why Armos over alternatives?

vs. building your own: A custom masking layer takes weeks to build correctly and months to handle edge cases. Armos is a pip install.

vs. LLM Guard: LLM Guard focuses on prompt injection and toxicity — not PII masking. Different problem.

vs. Presidio directly: Presidio detects PII but doesn't handle tokenization, vault management, or SDK integration. Armos wraps all of that.

Indian PII first-class: Aadhaar and PAN detection built in. No competitor handles Indian identifiers reliably.

---

Quickstart

Install

pip install armos

For Redis-backed persistence across requests:

pip install armos[redis]

Note: On first use, download the spaCy language model:

python -m spacy download en_core_web_lg

OpenAI

# Before
from openai import OpenAI
client = OpenAI()

# After — one import added, one word changed
from openai import OpenAI
from armos import ArmosOpenAI

client = ArmosOpenAI(OpenAI())

# Everything else is identical
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": "Summarise the case for patient John Smith, Aadhaar 2345 6789 0123"
    }]
)

# Real values are restored in the response automatically
print(response.choices[0].message.content)

Anthropic

from anthropic import Anthropic
from armos import ArmosAnthropic

client = ArmosAnthropic(Anthropic())

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": "Patient John Smith, DOB 12/04/1982, PAN ABCDE1234F"
    }]
)

print(message.content[0].text)  # real values restored

OpenAI Responses API

response = client.responses.create(
    model="gpt-4o",
    input="Patient John Smith, Aadhaar 2345 6789 0123 — summarise in one line."
)
print(response.output[0].content[0].text)  # real values restored

Embeddings

# PII is masked before the text is sent for embedding
result = client.embeddings.create(
    model="text-embedding-3-small",
    input="John Smith's email is [e-mail]"
)
# Works with list input too
result = client.embeddings.create(
    model="text-embedding-3-small",
    input=["[e-mail]", "no pii here"]
)

With Redis (persistent vault across requests)

# Token mappings survive across processes and requests
client = ArmosOpenAI(OpenAI(), store="redis", redis_url="redis://localhost:6379")
client = ArmosAnthropic(Anthropic(), store="redis", redis_url="redis://localhost:6379")

# Custom TTL (default: 24 hours)
client = ArmosOpenAI(OpenAI(), store="redis", redis_url="redis://localhost:6379", vault_ttl=3600)

Standalone (any LLM or framework)

from armos import Armos

guard = Armos()

result = guard.mask("Patient John Smith, Aadhaar 2345 6789 0123, email [e-mail]")
print(result.text)
# → "Patient [PII:NAME:a1b2c3d4], Aadhaar [PII:AADHAAR:b2c3d4e5], email [PII:EMAIL:e5f6g7h8]"

print(result.has_pii)  # True

restored = guard.demask(result.text)
print(restored)
# → "Patient John Smith, Aadhaar 2345 6789 0123, email [e-mail]"

---

What gets detected

Entity	Token	Example
Person name	[PII:NAME:…]	John Smith
Email address	[PII:EMAIL:…]	[e-mail]
Phone number	[PII:PHONE:…]	[phone]
Aadhaar number	[PII:AADHAAR:…]	2345 6789 0123
PAN card	[PII:PAN:…]	ABCDE1234F
Credit / debit card	[PII:CARD:…]	4111 1111 1111 1111
IP address	[PII:IP:…]	192.168.1.100
API keys & secrets	[PII:APIKEY:…]	sk-abc123… / AKIA… / ghp_…

---

Token design

Tokens are deterministic and normalisation-aware:

"john smith"  →  [PII:NAME:a1b2c3d4]  ← stored: "john smith"
"John Smith"  →  [PII:NAME:a1b2c3d4]  ← same token, vault unchanged
"JOHN SMITH"  →  [PII:NAME:a1b2c3d4]  ← same token, vault unchanged

All casing variants of the same name map to one token. The LLM sees one consistent entity across a conversation — not three different people. De-masking restores the first-seen value.

---

Vault options

Option	Default	Use when
In-memory	Armos()	Single request or single process
Redis	Armos(store="redis", redis_url="redis://…")	Multi-turn conversations, multiple workers, or across requests

In-memory vault is zero configuration and the default. Redis vault persists token mappings so a token created in request 1 can be de-masked in request 5.

---

Token overhead

Masking replaces PII values with tokens like [PII:NAME:a1b2c3d4]. These are longer than the original values, adding a small number of tokens to each request. Measured with GPT-4 tokenization (cl100k_base):

Entity type	Example	Original tokens	Masked tokens	Overhead
NAME	John Smith	2	10	+8
EMAIL	[e-mail]	3	13	+10
AADHAAR	2345 6789 0123	8	13	+5
PAN	ABCDE1234F	4	11	+7
PHONE	[phone]	8	12	+4
IP	192.168.1.100	7	11	+4
Average		6	11	+5

In practice: a message with 4 PII entities adds ~20 tokens to the request, plus a one-time 13-token system hint injected when PII is detected. For a typical 200-token prompt this is a ~15% increase — negligible against LLM pricing at scale.

---

Performance

Detection and masking run entirely in-process with no network calls. Benchmarked on Apple M-series (50 runs, median / p95):

Text size	Memory — p50	Memory — p95	Redis — p50	Redis — p95
Short (~20 tokens)	2.5 ms	2.7 ms	3.6 ms	3.9 ms
Medium (~60 tokens)	6.0 ms	6.4 ms	8.6 ms	9.0 ms
Long (~150 tokens)	13.3 ms	13.9 ms	19.4 ms	20.5 ms

Redis overhead is the localhost round-trip cost (~1–2 ms per vault operation). Both are negligible relative to LLM response times (typically 500 ms–5 s).

---

Known limitations

1. Indian name accuracy — en_core_web_lg is trained on English text; Indian names have lower recall than Western names. Fine-tuning planned.
2. Token length — [PII:NAME:a1b2c3d4] is 18 chars vs John (4 chars). Near context-window limits this may push content over. Rare in practice.
3. Casing: first-seen wins — De-masking always restores the first-seen casing of an entity. Use consistent casing in your prompts for exact restoration.
4. Streaming not supported — stream=True passes through without masking. (planned)
5. Async clients not supported — AsyncOpenAI, AsyncAnthropic pass through without masking. (planned)

---

Contributing

Armos is open source and MIT licensed. Issues and pull requests welcome.

git clone https://github.com/armos-ai/armos-python
cd armos-python
pip install -e ".[dev,all]"
python -m spacy download en_core_web_lg
pytest tests/ -v

License

MIT