I need a compliance tool that scans all inputs/outputs to our LLM for PII and blocks it.
Data as of Sep 21, 2026 · Based on 298 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
Data as of Sep 21, 2026 · Based on 298 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
To scan and block personal data in LLM interactions, you can use specialized security platforms or flexible open-source toolkits. 
Brands AI recommends here
Named in 48% of answers
Named in 41% of answers
Lakera Guard excels at providing low-latency, real-time scanning for PII, prompt injections, and data leaks. It is best for teams requiring active protection that blocks sensitive content consistently across LLM interactions.
Named in 36% of answers
Named in 27% of answers
Source | Citation share |
|---|---|
Top 12 LLM Security Tools: Paid & Free (Overview) | Lakera – Protecting AI teams that disrupt the world.https://www.lakera.ai/blog/llm-security-tools | 4% |
 Security & Guardrails - Langfusehttps://langfuse.com/docs/security-and-guardrails | 4% |
 How to avoid sensitive data/PII being part of LLM training data?https://www.reddit.com/r/LocalLLaMA/comments/18s1lvj/how_to_avoid_sensitive_datapii_being_part_of_llm/ | 2% |
 Securing and enhancing LLM prompts & outputs: A guide using Amazon Bedrock Guardrails and open-source solutions - DEV Communityhttps://dev.to/aws-builders/securing-and-enhancing-llm-prompts-outputs-a-guide-using-amazon-bedrock-and-open-source-solutions-3akc | 2% |
 Pillar Security - LiteLLMhttps://docs.litellm.ai/docs/proxy/guardrails/pillar_security | 2% |
 GitHub - rpgeeganage/pII-guard: 🛡️ PII Guard is an LLM-powered tool that detects and manages Personally Identifiable Information (PII) in logs — designed to support data privacy and GDPR compliance · GitHubhttps://github.com/rpgeeganage/pII-guard | 2% |
 LLM Privacy Protection: Strategic Approaches For 2025https://www.protecto.ai/blog/llm-privacy-protection-strategies-2025/ | 2% |
 AI Security Tools: 11 Best for LLM & GenAI in 2026 - Mindgardhttps://mindgard.ai/blog/best-ai-security-tools-for-llm-and-genai | 2% |
 10 best LLM security tools to use in 2026https://nexos.ai/blog/llm-security-tools/ | 2% |
 AI and Data Protection: Strategies for LLM Compliance and Risk Mitigation | Proofpoint UShttps://www.proofpoint.com/us/blog/dspm/ai-and-data-protection-strategies-for-llm-compliance-and-risk-mitigation | 2% |
Yes. The cleanest pattern is a **PII gateway around the LLM**, rather than relying on the model itself to police PII: ``` User/App
Yes. The cleanest pattern is a PII gateway around the LLM, rather than relying on the model itself to police PII:
User/App
│
▼
┌─────────────────────┐
│ PII Input Scanner │ ← detect PII
└─────────┬───────────┘
│ PII found?
┌───┴───┐
YES NO
│ │
BLOCK ▼
┌───────┐
│ LLM │
└───┬───┘
│
▼
┌──────────────────┐
│ PII Output Scanner│ ← detect PII
└────────┬─────────┘
│
PII found?
┌─────┴─────┐
YES NO
│ │
BLOCK ▼
Return output
Don't scan only the user prompt and final answer. For an agentic system, inspect:
This matters because, for example, AWS explicitly notes that its Bedrock sensitive-information filter doesn't inspect PII in model-generated tool-call arguments, tool results, or tool definitions.
I'd also make BLOCK the default, rather than silently redacting, if your compliance requirement is "PII must not enter or leave the LLM." Detection systems aren't perfect—Presidio itself warns that automated detection cannot guarantee finding all sensitive information.
A production policy could therefore look like:
PII policy
├── EMAIL_ADDRESS → BLOCK
├── PHONE_NUMBER → BLOCK
├── SSN → BLOCK
├── CREDIT_CARD → BLOCK
├── BANK_ACCOUNT → BLOCK
├── PASSPORT → BLOCK
├── DOB → BLOCK
├── ADDRESS → BLOCK
├── PERSON_NAME → BLOCK
├── IP_ADDRESS → configurable
└── Custom identifiers → BLOCK
If you tell me your stack (Python/Node/Java), LLM provider, and whether this needs to run on-prem/VPC, I can sketch the actual middleware/API and deployment architecture.
To scan and block PII across both inputs (prompts) and outputs (responses) in an LLM pipeline, you need an **AI Gateway or Guardrail Middleware** that sits inline between your application and your LLM provider (like OpenAI, Anthropic, or a local…
To scan and block PII across both inputs (prompts) and outputs (responses) in an LLM pipeline, you need an AI Gateway or Guardrail Middleware that sits inline between your application and your LLM provider (like OpenAI, Anthropic, or a local model).
Depending on whether you want an open-source self-hosted tool or an enterprise platform, here are the top options available:
block or redact rather than log-only so that leaking PII actively halts the request cycle.To scan and block PII (Personally Identifiable Information) across LLM inputs and outputs, you need an **LLM Guardrails / Proxy Gateway** layer . Depending on whether you want an open-source library to embed in your code, an API gateway proxy, or a dedicated enterprise platform, several strong options are available.…
To scan and block PII (Personally Identifiable Information) across LLM inputs and outputs, you need an LLM Guardrails / Proxy Gateway layer . Depending on whether you want an open-source library to embed in your code, an API gateway proxy, or a dedicated enterprise platform, several strong options are available.
To help narrow down the ideal fit, let me know:
If you need to **scan every LLM input and output for PII and block/redact it**, you’re looking for an **LLM security gateway / AI guardrail layer** rather than just a PII library. A typical architecture: ```
If you need to scan every LLM input and output for PII and block/redact it, you’re looking for an LLM security gateway / AI guardrail layer rather than just a PII library.
A typical architecture:
User / App
|
v
[LLM Compliance Gateway]
|
+--> Input PII Scanner
| - Detect PII
| - Block / redact / tokenize
|
v
LLM Provider (OpenAI, Azure OpenAI, Anthropic, etc.)
|
v
[Output PII Scanner]
| - Detect leakage
| - Block response
| - Redact entities
|
v
User
A common open-source approach:
Microsoft Presidio for PII detection and anonymization. It supports text, images, and structured data, with customizable recognizers.
Add a policy engine:
Allow: customer name in a support response
Mask: email addresses (j***@example.com)
Block: SSNs, payment cards, health records
Audit: log detection type without storing the PII itself
Example flow:
request = user_prompt
pii = pii_scanner.detect(request)
if pii.contains_blocked_entities():
return "Request blocked due to sensitive data policy"
sanitized_prompt = pii_scanner.redact(request)
response = llm.generate(sanitized_prompt)
output_pii = pii_scanner.detect(response)
if output_pii.contains_blocked_entities():
return "Response blocked due to sensitive data policy"
return response
Bidirectional scanning
Before sending data to the model
Before returning model output
Multiple PII detectors
Regex (SSN, credit cards, phone numbers)
ML/NLP entity detection (names, addresses)
Custom recognizers (employee IDs, customer numbers)
Policy controls
Block
Redact
Replace with tokens
Require human approval
No sensitive logging
Store: "Detected EMAIL entity"
Avoid storing: "[email protected]"
Context awareness
A random 9-digit number is not always an SSN
A company name may not be personal data
Audit trail
User/app identity
Model used
Policy triggered
Entity category detected
Timestamp
For an enterprise deployment:
If you are building this internally, a Presidio-based gateway is a reasonable starting point. If you need centralized governance across many apps and models, an AI security gateway or enterprise DLP platform is usually easier to operate.
To scan and block or mask Personally Identifiable Information (PII) across all LLM inputs and outputs, you need an **LLM Guardrail / Security Gateway** . Depending on whether you want an open-source library to embed in your stack or a commercial enterprise firewall, several top-tier options exist to handle…
To scan and block or mask Personally Identifiable Information (PII) across all LLM inputs and outputs, you need an LLM Guardrail / Security Gateway . Depending on whether you want an open-source library to embed in your stack or a commercial enterprise firewall, several top-tier options exist to handle this.
To help narrow down the right choice, tell me:
For an LLM application, you typically want a **PII firewall / AI gateway** that sits between users, your application, and the model provider. It should inspect **both directions**: 1. **Inbound scan (user → LLM)** - Detect PII before it reaches the model.
For an LLM application, you typically want a PII firewall / AI gateway that sits between users, your application, and the model provider. It should inspect both directions:
A common architecture:
User
|
v
API Gateway / LLM Firewall
|
+--> PII Detector
| |
| +--> allow
| +--> redact
| +--> block + audit
|
v
LLM Provider
|
v
Response Scanner
|
+--> allow
+--> redact
+--> block
|
v
User
Support at least:
Use multiple detectors:
Example policies:
policies:
customer_chat:
input:
action: redact
pii:
- email
- phone
- address
output:
action: block
pii:
- ssn
- credit_card
internal_agent:
input:
action: allow
audit: true
Examples:
Before:
My name is Jane Smith.
My SSN is 123-45-6789.
After:
My name is [PERSON].
My SSN is [SSN_REDACTED].
or tokenized:
My name is {{user_48392}}.
My SSN is {{ssn_92831}}.
Tokenization is useful when the model needs to reason over data without seeing the raw identifier.
For an enterprise LLM gateway, I would usually combine:
API Gateway
|
+-- Presidio / PII engine
|
+-- Policy engine
|
+-- Audit logger (PII-safe)
|
+-- LLM provider
A minimal production MVP would be:
That gives you a controllable "PII firewall" without modifying the underlying LLM.
Yes. What you want is essentially a **PII policy enforcement layer / LLM gateway** that sits between your applications and the model provider. ### Recommended architecture ```
Yes. What you want is essentially a PII policy enforcement layer / LLM gateway that sits between your applications and the model provider.
┌──────────────────────┐
User / App ────────►│ LLM Compliance │
│ Gateway │
│ │
│ 1. PII detection │
│ 2. Policy engine │
│ 3. Block/redact │
│ 4. Audit logging │
└──────────┬───────────┘
│
sanitized request
│
▼
┌───────────┐
│ LLM │
└─────┬─────┘
│
model response
│
┌──────────▼───────────┐
│ Output PII scan │
│ Block / redact │
│ Audit │
└──────────┬───────────┘
│
▼
User
The important part is to scan both directions. A model can reproduce PII that wasn't present in the immediate prompt, so output scanning should be an independent enforcement point.
I'd implement the policy roughly as:
| Stage | Detection | Action |
|---|---|---|
| Incoming request | PII | Block |
| Incoming request | Allowed pseudonym/token | Allow |
| Model output | PII | Block |
| Model output | Uncertain detection | Block or human-review |
| Logs/traces | PII | Never store raw content |
| RAG documents | PII | Scan before indexing |
| Tool/function arguments | PII | Scan before execution |
Don't rely solely on regex. Use detectors + validation + custom organization-specific patterns. For example, employee IDs, customer IDs, account numbers, internal case numbers, and proprietary identifiers often aren't covered adequately by generic PII detectors.
One particularly useful pattern is tokenization rather than simply rejection:
User:
"What's the status of Jane Smith, SSN 123-45-6789?"
Gateway:
"What's the status of [PERSON_1], SSN [SSN_1]?"
↓
LLM
↓
Gateway verifies output contains no prohibited
PII before returning it.
If the LLM needs to reason about the identity consistently across a conversation, deterministic/pseudonymous tokens can preserve that relationship without exposing the underlying identifier. Google's DLP supports cryptographic tokenization for this use case.
If your requirement is "PII must never reach the LLM provider", don't put the PII scanner after the model call. The gateway has to inspect/sanitize the request before forwarding it. And make sure the gateway itself doesn't log the unsanitized request.
If you tell me which LLMs you're using (OpenAI, Anthropic, Azure OpenAI, Bedrock, Gemini, or multiple) and whether you're AWS/Azure/GCP/on-prem, I can recommend a concrete production architecture and specific tool stack.
To secure your LLM application against PII leakage, you need an **inline guardrail layer** that inspects payloads *before* they hit the model (input) and *before* they return to the user (output).[](https://appsecsanta.com/ai-security-tools)…
To secure your LLM application against PII leakage, you need an inline guardrail layer that inspects payloads before they hit the model (input) and before they return to the user (output).
Depending on whether you want an open-source library to self-host or a commercial enterprise platform, the top tools available for scanning, blocking, and redacting PII include:
Implementation Architecture
When building this out, configure your pipeline to handle violations via one of two operational modes:
<REDACTED_EMAIL> ) before passing the sanitized text downstream.If you'd like to narrow this down, let me know:
Yes. What you want is essentially an **LLM DLP/PII gateway** that sits between your application and the model provider: ``` User / App
Yes. What you want is essentially an LLM DLP/PII gateway that sits between your application and the model provider:
User / App
│
▼
┌──────────────────────┐
│ PII/DLP Gateway │
│ │
│ Detect PII │
│ Custom sensitive data│
│ Policy evaluation │
│ Audit/logging │
└──────────┬───────────┘
│
BLOCK / REDACT
│
▼
LLM API
│
▼
┌──────────────────────┐
│ Output Scanner │
│ PII / secrets / DLP │
└──────────┬───────────┘
│
BLOCK / REDACT
│
▼
User/App
If your requirement is literally "PII must never reach the LLM and must never leave the LLM", I'd use:
Gateway → deterministic rules/regex → Presidio → custom recognizers → LLM → output scanner → policy enforcement
For example:
| Detection | Input | Output |
|---|---|---|
| SSN | Block | Block |
| Credit card | Block | Block |
| Redact/block | Block | |
| Phone | Redact/block | Block |
| Customer ID | Custom rule | Block |
| API keys/secrets | Block | Block |
| Internal account numbers | Custom rule | Block |
| Names/addresses | Policy-dependent | Policy-dependent |
One important caveat: no automated PII detector guarantees 100% detection. Presidio itself explicitly warns about false negatives, so for a compliance control you should layer deterministic patterns, custom entity recognizers, and potentially a second detection engine rather than treating one scanner as a perfect security boundary.
If you tell me your LLM stack (OpenAI/Azure/Anthropic/etc.), language (Python/Node/Java), deployment (AWS/Azure/GCP/on-prem), and whether you need HIPAA/SOC 2/GDPR, I can recommend the specific architecture and give you a working gateway implementation.
To scan and block or mask Personal Identifiable Information (PII) across all LLM inputs (prompts/RAG data) and outputs (completions), you need an **LLM security gateway** or a **guardrails framework**.[](https://www.getmaxim.ai/articles/the-best-llm-guardrails-platforms-for-enterprise-ai-in-2026/)…
To scan and block or mask Personal Identifiable Information (PII) across all LLM inputs (prompts/RAG data) and outputs (completions), you need an LLM security gateway or a guardrails framework.
The top tools available for this depend on whether you want an infrastructure proxy (no code changes) or an application-layer framework.
If you are building your own middleware, these are the underlying engines you use to actually recognize the entities:
To help narrow down the best fit, tell me:
