I need a compliance tool that scans all inputs/outputs to our LLM for PII and blocks it.

If you need a compliance layer that scans every LLM input and output for PII and blocks (or redacts) violations, you’re looking for an LLM security gateway / AI DLP layer rather than just a regex scanner.

A typical architecture:

User / App
    |
    v
+-----------------------+
| LLM Compliance Proxy  |
|                       |
| 1. Input PII scan     |
| 2. Policy evaluation  |
| 3. Redact or block    |
| 4. Send to LLM        |
| 5. Output PII scan    |
| 6. Redact or block    |
| 7. Audit logging      |
+-----------------------+
    |
    v
LLM Provider (OpenAI, Azure OpenAI, Anthropic, etc.)

Capabilities you should require

1. Input inspection

Detect before data leaves your environment:

• Names
• Email addresses
• Phone numbers
• Home addresses
• Government IDs (SSN, passport, driver's license)
• Payment card data
• Health information (PHI)
• Customer/account identifiers
• Internal confidential fields

Actions:

• Block request
• Redact PII before sending
• Allow with audit event
• Require approval

2. Output inspection

Scan the model response before users receive it:

Example:

Model output:

Customer John Smith's SSN is 123-45-6789.

Policy result:

BLOCKED:
Detected:
- PERSON_NAME
- US_SSN

Reason:
Restricted PII disclosure

3. Context and RAG scanning

Don’t only scan the user prompt and final answer. Also inspect:

• Retrieved documents
• Vector database chunks
• Agent tool calls
• Memory writes
• System-generated summaries

Microsoft specifically recommends scanning prompts, outputs, memory writes, retrieved context, logs, and tool outputs as part of AI data-loss prevention controls.

Tool options

Open-source building blocks

• Microsoft Presidio — good foundation for detecting and anonymizing PII in text, images, and structured data. Microsoft GitHub - Deploy as a service in your network
• Add custom recognizers
• Integrate into an LLM gateway

A simple flow:

Prompt
 |
 v
Presidio Analyzer
 |
 +-- PII found --> Block/redact
 |
 +-- Clean --> LLM
 |
 v
Presidio Analyzer
 |
 +-- PII found --> Block/redact output

Commercial LLM security gateways

• Lakera provides LLM input/output guardrails that can detect PII and sensitive data leakage and enforce policies such as blocking or masking.
• Microsoft Purview DLP can apply sensitive-data controls across AI applications and detect sensitive information in AI interactions.

Recommended production design

For an enterprise deployment, I’d implement:

                    ┌──────────────┐
                    │ Applications │
                    └──────┬───────┘
                           |
                           v
              ┌──────────────────────┐
              │ LLM Policy Gateway   │
              └──────────────────────┘
                 |              |
                 v              v
          Input Scanner     Output Scanner
                 |              |
                 v              v
          PII Engine      PII Engine
                 |
        ┌────────┴────────┐
        │ Policy Decision │
        └────────┬────────┘
                 |
       Allow / Redact / Block
                 |
                 v
              LLM API

Minimum controls I would include

If you are building this yourself, a common stack is:

• API gateway: Envoy / Kong / FastAPI
• PII engine: Presidio or Azure Language PII detection
• Policy engine: Open Policy Agent (OPA)
• Logging: Splunk / Sentinel / Datadog
• Storage: encrypted audit store

The key design decision is whether you want a proxy that sits in front of all LLM calls (usually best for governance) or SDK middleware embedded into each application (usually faster to deploy but harder to enforce universally).

If you are building this yourself, a common stack is:

To scan and block or mask PII (Personally Identifiable Information) across both inputs (prompts) and outputs (responses) in an LLM architecture , you need a guardrail scanner or an AI proxy gateway.

The top tools available for this use case range from open-source toolkits to enterprise gateways:

1. Dedicated Open-Source Scanners & Frameworks

• LLM Guard : A comprehensive open-source tool specifically designed for securing LLM interactions. It includes robust, zero-dependency input and output scanners that explicitly detect, redact, or block PII (names, phone numbers, SSNs, credit cards, etc.) before prompts hit the model or responses reach the user.
• Microsoft Presidio : The industry standard for PII detection and anonymization. It scans text using NLP and regex, assigns risk scores, and can either mask, replace, or encrypt PII. It is heavily modular and integrates nicely into custom python pipelines or gateways.
• Guardrails AI : A flexible validator framework featuring a community hub with specific PII and risk-mitigation validators that can wrap around your LLM calls to catch and block sensitive data.

2. Infrastructure & Proxy Gateways (Plug-and-Play)

If you want a centralized proxy that sits between your app and LLM providers (like OpenAI, Anthropic, or local models) to handle compliance transparently:

• LiteLLM Gateway + Microsoft Presidio : LiteLLM supports native integration with Microsoft Presidio. You can configure the proxy gateway to intercept calls, scrub/mask PII on the fly for inputs and outputs, and block requests that violate your privacy thresholds.
• Cloud-Native / Enterprise Alternatives : If you are in heavily regulated environments (HIPAA, SOC2) and need a managed commercial solution rather than self-hosting, tools like AWS Bedrock Guardrails or Nightfall AI provide enterprise DLP (Data Loss Prevention) specifically built for LLM inputs and outputs.

To help narrow down the best implementation, tell me:

• Are you looking for an open-source self-hosted library/gateway or a managed enterprise SaaS?
• Do you want to strictly block the request, or mask/anonymize the PII so the LLM can still process the text?

Yes. What you want is essentially an LLM gateway/DLP layer that sits between your application and the model provider:

User / App
    │
    ▼
┌──────────────────────────────┐
│       LLM Compliance         │
│          Gateway             │
│                              │
│  1. Scan input for PII       │
│  2. Block / redact           │
│  3. Send clean request       │
│  4. Scan model output        │
│  5. Block / redact           │
│  6. Audit decision           │
└──────────────────────────────┘
    │                    ▲
    ▼                    │
   LLM ──────────────────┘

Tools I'd shortlist

• lakera.ai — probably the closest match if you have multiple LLM providers. Its Guard API can screen both inputs and outputs, with PII/data-leakage policies that can block or mask detected data. It also supports custom detectors and can cover tool calls/responses for agents.
• Amazon Bedrock Guardrails — a strong choice if you're already on AWS. Its sensitive-information filters explicitly support PII detection on both prompts and model responses, with Block or Mask actions and custom regex entities.
• Microsoft Foundry Guardrails — attractive if you're primarily in Azure/Microsoft. Its guardrail architecture supports intervention at user input, tool call/response, and final output stages.
• Amazon Comprehend PII detection — useful if you want to build the gateway yourself rather than buy an LLM-security product. It exposes PII entity detection through an API, which you can put in front of and behind your LLM calls.

For your requirement, I'd architect it this way

Don't just scan the final chat response. Put enforcement at every boundary:

1. Inbound user input → detect PII → block/redact.
2. RAG/context retrieved from databases → scan before sending to the LLM.
3. Tool/function arguments → scan before external transmission.
4. LLM output → scan before returning to the user.
5. Tool responses → scan before feeding them back into the model.
6. Logging/telemetry → store only redacted content where possible.

That last point matters: a system that blocks PII from reaching the user but logs the unredacted prompt/output has not really solved the compliance problem.

Also, I'd use block vs. redact by policy, rather than making everything a hard block. For example:

One important caveat: PII detectors are probabilistic, so no commercial tool should be treated as a mathematically complete guarantee that "all PII" is caught. AWS, for example, explicitly describes its PII detection as context-dependent ML detection. AWS Documentation For compliance, combine ML detection with deterministic regex/checksum rules, organization-specific identifiers, allowlists, and adversarial testing.

If you're building this for an enterprise with multiple LLM providers, I'd start by evaluating Check Point AI Guardrails vs. a small in-house gateway using a dedicated PII detector. If you're all-in on AWS or Azure, their native guardrails are worth evaluating first.

To secure your LLM pipeline against data leaks and regulatory violations, you need an LLM input/output guardrail or proxy . These tools intercept prompts before they hit the model (input-side scanning/masking) and check responses before they return to the user (output-side blocking/redaction).

Top-tier solutions available for this include:

• Enterprise & Commercial Platforms:
  • Llama Guard / Meta Security: Open-weight models fine-tuned specifically for safeguarding LLM inputs and outputs against policy violations.
  • Galileo Protect: Offers robust evaluation, real-time input/output monitoring, and PII/toxicity detection in the inference path.
  • Arthur Shield: Focuses heavily on pre-LLM PII redaction and enterprise guardrails to ensure sensitive data doesn't leave your perimeter.
  • Aporia / Lakera Guard: Specialized real-time guardrails designed to block prompt injections, toxic content, and PII leaks.
• Open Source & Developer Frameworks:
  • NVIDIA NeMo Guardrails: An open-source toolkit for easily adding programmable guardrails to LLM-based applications.
  • LlamaIndex / LangChain Guardrails integration: Local middleware hooks that allow custom regex, Presidio, or named entity recognition (NER) models to scrub PII on the fly.
  • Microsoft Presidio: An open-source PII detection and anonymization service that integrates seamlessly into custom LLM gateways.

If you'd like to narrow down the best fit, let me know:

• Are you looking for an open-source library to host yourself or a fully managed SaaS platform?
• Do you prefer to completely block the request when PII is found, or dynamically mask/redact it so the conversation continues?
• Are there specific regulations (GDPR, HIPAA, SOC2 ) you need out-of-the-box compliance reporting for?

Yes. If your requirement is “scan every LLM input and output for PII, then block the interaction when policy is violated,” there are several good options:

My first choice: Check Point AI Guardrails

Check Point AI Guardrails is particularly close to your stated requirement. Its Guard API can screen the input before it reaches the LLM and the output before it reaches the user/downstream system. In Enforce mode, a detected policy violation produces a flagged result that your application can use to block the request. It can also return locations of detected PII so you can mask it instead.

It also handles agent workflows, including user input → LLM → tool calls → tool responses → LLM output, which is important if “all inputs/outputs” really means the entire AI pipeline rather than just chat prompts.

Strong alternative: Protecto

Protecto is worth evaluating if your primary concern is privacy/compliance rather than broader AI security. It says it scans prompts, RAG context, tool results, agent actions, and final responses, with policies that can mask, block, allow, or unmask sensitive data. It also provides audit records of scan/mask/block decisions.

If you want to build it yourself

OpenAI Privacy Filter is now an interesting building block: OpenAI released it in April 2026 as an open-weight model specifically for detecting/redacting PII in unstructured text. You'd put it in a gateway around your LLM calls and implement the blocking policy yourself.

OpenAI also has OpenAI Guardrails with both an input “Mask PII” guardrail and an output “Contains PII” guardrail.

For an enterprise compliance architecture, I'd shortlist Check Point AI Guardrails and Protecto first. The key evaluation criteria I'd insist on are:

1. Pre-LLM blocking — PII must not reach the model/provider.
2. Post-LLM blocking — PII generated by the model must not reach users/downstream systems.
3. RAG/tool coverage — retrieved documents and tool inputs/outputs need scanning too.
4. Custom entity detection — employee IDs, customer IDs, policy numbers, proprietary identifiers, etc.
5. Fail-closed behavior — inability to scan should not silently allow the request.
6. Auditability — policy, detector, decision, timestamp, and workflow should be recorded without logging the actual PII.
7. Latency and deployment model — especially if you're sending sensitive data through a third-party SaaS scanner.

If you tell me your LLM stack (OpenAI/Azure OpenAI/Anthropic/Bedrock/etc.) and whether you need SaaS or self-hosted, I can narrow this to the 2–3 best fits and compare their PII coverage, latency, deployment, compliance certifications, and pricing.

To scan and block or redact Personally Identifiable Information (PII) across both inputs and outputs in an LLM architecture, you need an AI Gateway or a Runtime Guardrail Proxy that sits inline between your application and the LLM provider (like OpenAI, Anthropic, or self-hosted models).

Depending on whether you want an open-source self-hosted stack or a managed enterprise platform , several tools handle bi-directional PII interception, masking, and blocking at inference time.

1. Open-Source & Self-Hosted Gateways (Privacy-First)

If data sovereignty is a priority and you cannot let raw prompts touch external scanning APIs, these run entirely inside your own infrastructure:

• Occludra Gateway : An open-source, self-hosted AI security proxy and firewall. It sits inline, is fully OpenAI-SDK compatible, and performs real-time PII redaction, prompt injection blocking, and secret leak detection before data ever leaves your network.
• Microsoft Presidio : An enterprise-grade, open-source context-aware PII detection and anonymization service. While it is a library rather than a ready-to-go proxy, it is the underlying engine used by many LLM application layers to analyze, mask, or block entities (SSNs, emails, names) using NLP and regex.
• LLM Guard : A comprehensive open-source toolkit designed to secure LLM interactions. It offers robust input and output scanners specifically built to detect PII/PHI, secrets, and malicious prompts, and can be deployed as a microservice or integrated into backend routing.

2. Managed & Enterprise Compliance Platforms

If you require out-of-the-box compliance reporting (SOC 2, HIPAA, GDPR) and centralized governance dashboards:

• LiteLLM Proxy Guardrails : If you are already routing requests through LiteLLM, they support native pre-call and post-call PII masking integrations (using Presidio or specialized security partners like Pillar Security) to block or redact fields dynamically on both inputs and outputs.
• TrueFoundry MCP Gateway : Provides infrastructure-level input/output guardrails and PII redaction tailored heavily toward agentic workflows and tool-calling architectures.
• AWS Bedrock Guardrails : If you are hosted within the AWS ecosystem, Bedrock allows you to configure specific PII filters (emails, names, phone numbers, credit cards) to automatically mask or block inputs and model responses across supported foundation models.

Key Architectural Choices: Block vs. Redact

When implementing your compliance tool, decide on the intervention strategy per entity type:

• Blocking (Reject): The request/response is immediately terminated with an error code (e.g., HTTP 400/500) if a high-risk entity like an SSN or API key is detected.
• Redacting/Masking (Sanitize): Replaces the PII with placeholders (e.g., [EMAIL] ) so the LLM retains contextual awareness without storing or processing the raw data.

To help narrow down the best choice, tell me:

• Are you deploying self-hosted/open-source or looking for a managed cloud service?
• Which LLM providers or frameworks (e.g., OpenAI, LangChain, LlamaIndex, custom microservices) are you currently using?

Yes. What you want is essentially an LLM privacy/compliance gateway that sits between your application and the model provider:

App → PII scanner/policy engine → LLM → output scanner → App

For a production system, I’d shortlist these:

My recommendation

If you want something you can deploy quickly: look at Check Point AI Guardrails. Its Guard API accepts LLM interactions and can flag them according to policy; your application can then block the interaction. It can also return detected PII locations so you can mask the sensitive portions.

If compliance/auditability is the primary requirement: Protecto is particularly relevant. It describes scanning prompts, RAG context, tool results, agent actions, memory, and final responses, with policies that can mask or block data and audit records of those decisions.

If you need everything inside your own infrastructure: Microsoft Presidio is a strong foundation. It detects PII using configurable recognizers and supports redaction, replacement, masking, hashing, and encryption. It's explicitly designed to be customized, though its maintainers warn that automated PII detection cannot guarantee finding everything.

Architecture I'd use

Don't just scan the user's prompt and model response. Put the policy layer around every boundary:

                    ┌──────────────────────┐
User ──────────────►│                      │
                    │   Privacy Gateway    │
RAG ───────────────►│                      │
                    │  PII detect          │
Tools ─────────────►│  policy evaluation   │
                    │  block / redact      │
Memory ────────────►│  audit               │
                    └──────────┬───────────┘
                               │
                         sanitized input
                               │
                               ▼
                         ┌───────────┐
                         │    LLM    │
                         └─────┬─────┘
                               │
                         raw response
                               │
                               ▼
                    ┌──────────────────────┐
                    │   Output Guard       │
                    │                      │
                    │ PII detection        │
                    │ policy evaluation    │
                    │ BLOCK if violation   │
                    └──────────┬───────────┘
                               │
                               ▼
                            User

I'd make blocking the default for high-risk entities (SSNs, payment-card data, credentials, government IDs, etc.), while using tokenization/masking for lower-risk PII where the LLM still needs the semantic context.

One important point: don't rely on a single regex/PII detector for compliance. Even Presidio explicitly cautions that automated detection isn't guaranteed to catch all sensitive information.

If you tell me your stack (e.g. Python/Node, OpenAI/Anthropic/Azure, AWS/Azure/GCP, and whether data must stay on-prem), I can narrow this to the best 1–2 options and show exactly where to put the scanner.

To scan and block or redact Personally Identifiable Information (PII) in LLM traffic, you need an AI Gateway or a Security Proxy/Guardrail layer that sits inline between your application and the LLM provider (OpenAI, Anthropic, local models, etc.).

Depending on whether you want an open-source self-hosted tool, an API gateway, or a managed cloud service, here are the top industry-standard compliance tools for this exact job:

• LLM Guard by Protect AI (Open-Source)
  • What it is: A comprehensive open-source security toolkit designed specifically for LLM interactions. It provides dedicated Input and Output Scanners.
  • How it handles PII: It uses Named Entity Recognition (NER) and pattern matching to detect and sanitize/redact PII (names, SSNs, credit cards, phones, etc.) or outright block requests/responses containing sensitive entities.
• LiteLLM Proxy with Presidio Integration (Open-Source / Enterprise)
  • What it is: An open-source proxy/gateway that standardizes calls to over 100 LLMs. It features built-in guardrail integrations using Microsoft Presidio for PII detection.
  • How it handles PII: It intercepts payloads at the proxy layer, scans incoming prompts and outgoing completions via Presidio, and supports both masking/redaction and hard blocking modes depending on your configuration.
• AWS Bedrock Guardrails (Cloud Managed)
  • What it is: If you are building on AWS or can route your multi-model traffic through Bedrock, its native guardrails feature robust PII filtering.
  • How it handles PII: It detects more than 50 types of sensitive information (including medical, financial, and personal identifiers) and lets you choose whether to block or anonymize/mask the content dynamically.
• NVIDIA NeMo Guardrails (Open-Source Framework)
  • What it is: An open-source toolkit for adding programmable guardrails to LLM conversational systems.
  • How it handles PII: Allows you to define custom rails (colloquially called input/output rails) that invoke PII detection flows to halt conversation execution or scrub data before it hits the core model logic.
• WSO2 AI Gateway / Gravitee AI Gateway (Enterprise API Management)
  • What it is: Enterprise API gateways equipped with dedicated AI/LLM extension policies.
  • How it handles PII: Features native config-driven PII masking and prompt guardrails that inspect the request/response JSON payloads inline at the API gateway boundary before external transit.

To help narrow down the best choice, tell me:

• Do you need a self-hosted open-source tool or a managed cloud service?
• Are you looking to hard-block the request entirely, or would you prefer re-versible masking/redaction so the LLM still gets functional context?

To scan and block PII (Personally Identifiable Information) in real time for LLM inputs and outputs, you need an AI Gateway or an LLM Firewall featuring data loss prevention (DLP) and inline proxy capabilities.

The industry standard solutions for this requirement fall into three categories depending on whether you want a managed proxy, an open-source framework you host, or an API-based guardrail.

Top LLM PII Compliance and Gateway Tools

• Microsoft Presidio (GitHub Repository)
  • Type: Open-source Python library / self-hosted service
  • How it works: It acts as a modular framework to detect PII (using a hybrid stack of regex, pattern matching, and NER/DeBERTa models) and anonymize or mask it. You can easily integrate it into your application middleware before requests hit your LLM client.
• Aide/AI Security Gateways (AI Security Gateway)
  • Type: Specialized inline AI Proxy / Gateway
  • How it works: Sits between your application and model providers (OpenAI, Anthropic, etc.). It scans inputs and outputs in-memory for over 30 PII entity types with sub-50ms latency, offering choices to strictly block or reversibly mask/redact.
• Prediction Guard (Prediction Guard)
  • Type: Self-hosted or managed compliance control plane
  • How it works: Built specifically for heavily regulated enterprises (HIPAA, GDPR) needing robust, system-level PII detection and immutable audit logs that integrate directly into LLM application pipelines.
• NeMo Guardrails (NVIDIA) (GitHub Repository)
  • Type: Open-source toolkit
  • How it works: Programmable guardrails for conversational systems that allow you to define topical, safety, and jailbreak/PII-blocking flows natively inside your application architecture.

Architecture Options: Block vs. Redact

When configuring your compliance tool, decide on your operational mode:

1. Validation / Blocking Mode: If PII confidence is above a threshold, the gateway intercepts the payload and drops it entirely (returning an error like an HTTP 446).
2. Mutation / Reversible Masking Mode: The tool replaces raw PII with typed tokens (e.g., [EMAIL_1] ) before sending it to the LLM, and rehydrates the model's output back to the original text before it reaches the end user.

If you'd like to narrow down the right choice, tell me:

• Do you need a fully self-hosted/open-source tool or a managed cloud gateway?
• Are you looking to completely block the request, or mask/redact the PII dynamically?

Yes. What you want is an LLM gateway / DLP guardrail that sits between your application and every model provider, with both input and output enforcement.

A strong architecture is:

App → LLM compliance gateway → model provider → gateway → App

At each direction, the gateway should:

• Detect PII: names, emails, phones, SSNs, addresses, financial identifiers, etc.
• Block rather than merely log when policy requires it.
• Optionally redact/tokenize instead of blocking.
• Apply different policies by application, user, data class, or environment.
• Scan tool calls and tool responses too if you're running agents.
• Produce an audit event without unnecessarily storing the raw PII.
• Work across OpenAI, Anthropic, Azure, local models, etc.

Tools worth evaluating

OpenAI Guardrails now has explicit input/output PII controls: its input side includes “Mask PII Hybrid,” while output includes “Contains PII Hybrid,” which can block responses containing configured PII types.

Microsoft Foundry Guardrails supports scanning at user input and output, with blocking actions; for agents it also supports tool-call and tool-response intervention points.

Azure Databricks Unity AI Gateway has built-in PII templates specifically for input or output, including both PII redaction and PII blocking.

There are also dedicated LLM gateway/DLP products that put enforcement directly in the request path, such as LLM Gateway's enterprise guardrails, which advertises PII/secrets detection with block, redact, or pass-through modes.

My recommendation

If you're building this for an enterprise environment, I'd avoid putting the PII detector inside each individual LLM integration. Put it at a central gateway:

                   ┌─────────────────────┐
                   │   Compliance/DLP    │
                   │      Gateway        │
                   │                     │
User/App ─────────►│ INPUT PII scan      │
                   │ Secrets scan        │
                   │ Policy evaluation   │
                   │        │            │
                   │        ▼            │
                   │     LLM provider    │
                   │        │            │
                   │        ▼            │
                   │ OUTPUT PII scan     │
                   │ Policy evaluation   │
                   └─────────┬───────────┘
                             │
                             ▼
                           App

The important design point is fail closed for the policies that matter: if the scanner can't determine whether a payload is compliant, don't send it to the model.

Also, don't confuse provider privacy controls with PII blocking. For example, OpenAI says API/business inputs and outputs aren't used for model training by default, and offers additional retention controls, but that's different from your application preventing an SSN from reaching the model in the first place.

If you tell me your stack (AWS/Azure/GCP, language, LLM providers, and whether you need HIPAA/SOC 2/GDPR), I can narrow this down to 2–3 concrete products and recommend the best fit.