Best PII-redaction/content filtering for LLM features?

Choosing the right PII (Personally Identifiable Information) redaction and content filtering stack for LLM features depends on whether you prefer an open-source developer-controlled library or a managed enterprise security proxy/guardrail.

1. Open-Source & Local Foundations (Best for Custom Control)

• Microsoft Presidio — Best for entity extraction & redaction. An open-source, highly customizable data protection toolkit. It combines pattern matching (regex/checksums) with Named Entity Recognition (NER via spaCy or HuggingFace) to detect and anonymize PII locally. It also features a built-in anonymizer that can replace, mask, or hash sensitive data.
• GLiNER (PII variants) — Best for zero-shot flexibility. A newer open-source approach using zero-shot Named Entity Recognition. Instead of being constrained to rigid predefined entity types, you can pass arbitrary or custom PII/entity categories at inference time, making it powerful for niche data formats.
• PII Masker — Lightweight Python-native utility. Powered by models like DeBERTa-v3, this open-source pipeline automates smart text splitting, entity recognition, and local masking/tokenization with minimal setup overhead.

2. Managed & Enterprise Guardrails (Best for Turnkey Security)

• Lakera Guard — Top-tier for production security proxies. Excels at real-time screening of user inputs and LLM outputs. It provides comprehensive protection against prompt injections, jailbreaks, data leakage (PII), and toxic content violations via an easy-to-integrate API.
• NVIDIA NeMo Guardrails — Best for conversational applications. An open-source toolkit programmable with "Colang" to securely guide LLM dialogs, block off-topic or toxic content, and seamlessly intercept inputs/outputs before they hit your core models.
• Llama Guard (Meta) — Best open-model guardrail. A fine-tuned Llama model specialized for content safety classification. You can host it yourself as an input/output gatekeeper to evaluate safety categories matching standard taxonomy guidelines.

3. Application & Observability Layer Masking

• Langfuse (PII Masking) — Best for logging & traces. If your primary concern is preventing sensitive prompt or trace data from leaking into your LLM observability/analytics pipelines, Langfuse handles built-in regex pattern scrubbing and structured field masking right inside the SDK before data ever leaves your process.

Practical Implementation Architecture

In production, the most resilient architecture is a two-tier gateway proxy:

1. Inbound (Pre-LLM): Pass user prompts through a local instance of Microsoft Presidio or a dedicated API guardrail like Lakera to strip out emails, names, and keys, retaining a mapping dictionary if you need to deanonymize the output later.
2. Outbound (Post-LLM): Run the generated response back through your filter to ensure the model didn't accidentally regurgitate memorized PII or drift into unsafe territory.

If you can share where your app is hosted (e.g., AWS, On-prem, Azure) and whether your primary concern is user-to-LLM privacy or preventing model toxicity , I can help narrow down the optimal tool stack.

If you mean “what should I put in front of an LLM to prevent PII and unsafe content from entering/leaving the model?”, I’d use a layered filter, not one product.

My current shortlist

What I'd actually build

For a production LLM feature, I'd do:

User input
    │
    ▼
[PII detector/redactor]
    │
    ├── secrets/API keys ───────► BLOCK
    ├── high-risk PII ──────────► REDACT/TOKENIZE
    └── allowed entities ───────► preserve
    │
    ▼
[Prompt injection / content safety]
    │
    ▼
LLM
    │
    ▼
[Output PII + safety filter]
    │
    ▼
User

For a new system, I'd lean toward OpenAI Privacy Filter + deterministic regex/rules + an output check. Privacy Filter is particularly interesting because it can run locally, so the unredacted text doesn't have to leave your infrastructure just to be filtered.

The important distinction is that PII filtering and content moderation are different problems. OpenAI's Moderation API, for example, classifies harmful content; it isn't a replacement for PII detection.

Don't just replace PII with [REDACTED]

For LLM features such as summarization, support agents, or RAG, pseudonymization/tokenization is often better:

"My name is Jane Smith and my account is 847291."

        ↓

"My name is <PERSON_1> and my account is <ACCOUNT_1>."

Maintain the mapping outside the LLM:

PERSON_1  → Jane Smith
ACCOUNT_1 → 847291

That lets the model reason about relationships while never seeing the actual identifiers. It also makes output rehydration possible when appropriate.

One important caveat

Don't treat any detector as a security boundary by itself. Even Presidio's documentation explicitly warns that automated PII detection isn't guaranteed to catch everything.

I'd therefore test against your actual data, especially:

• names embedded in prose
• addresses
• phone/email
• SSNs/national IDs
• financial/account numbers
• API keys and passwords
• medical identifiers
• proprietary customer identifiers
• multilingual text
• OCR/transcribed text
• adversarially formatted PII

If you tell me your stack (Python/TypeScript, OpenAI/Anthropic/Gemini, cloud/on-prem, and whether you need HIPAA/GDPR/etc.), I can narrow this down to a concrete architecture and benchmark the options on accuracy, latency, cost, and deployment model.

What I'd actually build

For a production LLM feature, I'd do:

User input
    │
    ▼
[PII detector/redactor]
    │
    ├── secrets/API keys ───────► BLOCK
    ├── high-risk PII ──────────► REDACT/TOKENIZE
    └── allowed entities ───────► preserve
    │
    ▼
[Prompt injection / content safety]
    │
    ▼
LLM
    │
    ▼
[Output PII + safety filter]
    │
    ▼
User

For a new system, I'd lean toward OpenAI Privacy Filter + deterministic regex/rules + an output check. Privacy Filter is particularly interesting because it can run locally, so the unredacted text doesn't have to leave your infrastructure just to be filtered.

The important distinction is that PII filtering and content moderation are different problems. OpenAI's Moderation API, for example, classifies harmful content; it isn't a replacement for PII detection.

Don't just replace PII with [REDACTED]

For LLM features such as summarization, support agents, or RAG, pseudonymization/tokenization is often better:

"My name is Jane Smith and my account is 847291."

        ↓

"My name is <PERSON_1> and my account is <ACCOUNT_1>."

Maintain the mapping outside the LLM:

PERSON_1  → Jane Smith
ACCOUNT_1 → 847291

That lets the model reason about relationships while never seeing the actual identifiers. It also makes output rehydration possible when appropriate.

One important caveat

Don't treat any detector as a security boundary by itself. Even Presidio's documentation explicitly warns that automated PII detection isn't guaranteed to catch everything.

I'd therefore test against your actual data, especially:

The best approach for PII redaction and content filtering in LLM applications is a hybrid architecture combining a dedicated privacy SDK at the application edge with an LLM gateway or cloud-native guardrail . Relying solely on system prompts or model-level refusal is insecure because raw data has already traversed external boundaries or logs.

Top PII-Redaction and Content Filtering Tools

Recommended Implementation Strategy

1. Edge Anonymization (The Ingestion Layer):
   • Intercept user input before it goes to the LLM or embedding/vector pipeline.
   • Use Microsoft Presidio to scan, detect, and swap out PII with localized synthetic placeholders (e.g., changing real names/phones to format-matching fake ones) so the LLM retains contextual reasoning without leaking real data.
2. Model Gateway Enforcement (The Control Plane):
   • Route traffic through an enterprise proxy or use managed options like Amazon Guardrails for Bedrock to catch residual leakage in both inputs and model outputs.
3. Logging & Observability Sanitation:
   • Scrub traces, telemetry, and monitoring payloads after inference execution so PII isn't inadvertently persisted in text monitoring tools or vector databases.

If you want, let me know:

• Are you deploying self-hosted (open-source) or using a cloud provider (AWS, Azure, GCP)?
• Do you need to support multilingual PII or specialized domains like HIPAA/Healthcare?

I can provide a tailored architectural blueprint or code snippet.

If you mean “what should I use in production to prevent PII/secrets from entering or leaving an LLM?”, I’d recommend a layered pipeline, not a single content filter.

My shortlist

AWS, for example, explicitly supports both locating and redacting PII, including names, addresses, phone numbers and financial identifiers.

Architecture I'd use

User input
   │
   ▼
[Deterministic secrets/PII scanner]
   │
   ├── API keys, JWTs, passwords, SSNs, CCs, etc.
   │
   ▼
[PII NER detector]
   │
   ├── names, addresses, organizations, emails, etc.
   │
   ▼
[Redact / tokenize]
   │
   ▼
        LLM
   │
   ▼
[Output scanner]
   │
   ├── PII
   ├── credentials/secrets
   ├── policy violations
   └── prompt-injection/exfiltration indicators
   │
   ▼
Response

Don't rely on the system prompt to prevent PII leakage. OWASP specifically warns that prompt-level restrictions can be bypassed and recommends input validation/sanitization plus output filtering.

One important design choice

For PII, I'd prefer tokenization over simple [REDACTED] when the LLM needs to reason about relationships:

“John Smith called from 404-555-1234” becomes:

“<PERSON_1> called from <PHONE_1>” Then you maintain a secure mapping outside the LLM and restore the values afterward if appropriate.

That lets the model reason about repeated entities without ever seeing the actual identity.

For content filtering

I'd separate PII detection from LLM safety/content moderation.

PII detectors are good at things like:

• names
• emails
• phone numbers
• addresses
• SSNs
• credit cards
• bank accounts

They aren't designed to reliably detect things like:

• prompt injection
• jailbreaks
• malware requests
• self-harm content
• sexual content
• data-exfiltration attempts
• malicious instructions hidden in retrieved documents

OWASP recommends combining deterministic validation/filtering with semantic/model-based guardrails, and also scanning retrieved content and tool outputs, not just the user's prompt.

What I'd pick

If you're building a new LLM product today, my default would be:

Presidio + custom regex/secret detectors + a dedicated LLM guardrail + output scanning.

If you're already deeply invested in AWS/GCP, I'd usually choose their managed DLP service instead of operating your own PII infrastructure.

And for an agent/RAG system, I'd make output validation mandatory: OWASP specifically recommends validating model outputs and dynamically redacting PII/secrets according to the user's authorization level.

If you tell me your stack (OpenAI/Anthropic/Gemini, Python/Node, AWS/GCP/Azure, RAG/agents or plain chat), I can give you a concrete production-grade PII/content-filtering architecture and tool comparison.

Then you maintain a secure mapping outside the LLM and restore the values afterward if appropriate.

That lets the model reason about repeated entities without ever seeing the actual identity.

For content filtering

I'd separate PII detection from LLM safety/content moderation.

PII detectors are good at things like:

For LLM features, the “best” PII redaction/content filtering stack depends heavily on your threat model. There is no universally best detector: production systems usually combine deterministic rules + ML/NLP detection + policy enforcement + output filtering.

A practical ranking by use case:

Philterd PortEden## My default architecture for LLM applications

User input
    |
    v
PII/content policy gateway
    |
    +--> Detect PII
    |       - regex/checksum (emails, cards, IDs)
    |       - NER/context models (names, locations)
    |       - custom business identifiers
    |
    +--> Replace with tokens
    |       "Jane Smith" -> <PERSON_123>
    |
    v
LLM / RAG / Agent tools
    |
    v
Output scanner
    |
    v
Authorized re-identification (optional)

The important design choice: do not just delete PII. For many LLM features, tokenization/pseudonymization preserves usefulness:

• "Customer Jane Smith requested refund" → "Customer <PERSON_123> requested refund"

Then keep the mapping in a separate secured store, not in the model context or logs.

Shortlist

1. Microsoft Presidio — best open-source starting point

Good for:

• internal copilots
• RAG pipelines
• customer support assistants
• custom entity types

Strengths:

• self-hosted
• customizable recognizers
• supports combining regex, NLP, and ML approaches

Weakness:

• requires tuning; it will miss domain-specific PII unless you add recognizers. The project itself notes automated detection is not guaranteed.

2. Google Cloud Sensitive Data Protection / AWS Comprehend — best managed options

Good for:

• companies already standardized on a cloud provider
• compliance-heavy environments
• large-scale document processing

AWS Comprehend, for example, provides PII entity detection and redaction capabilities for text workflows.

Tradeoff:

• raw text generally has to pass through the managed service, which may matter for strict data-boundary requirements.

3. Commercial AI privacy layers

Consider these when:

• missing PII is unacceptable
• you need audit trails, policy controls, or support
• you process regulated data

Examples include:

• Private AI
• Nightfall AI
• Skyflow (more of a privacy vault/tokenization approach)

Philterd## Content filtering (non-PII)

PII redaction is only one layer. For LLM safety, add:

• Prompt injection detection
• Secrets scanning (API keys, credentials)
• Toxicity/abuse filtering
• Data-loss prevention rules
• Jailbreak detection
• Output validation

A common mistake is treating “moderation” as equivalent to “privacy.” They solve different problems.

What I would pick

• Startup / SaaS LLM feature: Presidio + custom rules + output scanner.
• Enterprise on AWS: Comprehend + application-level tokenization.
• Enterprise on GCP: Sensitive Data Protection + gateway layer.
• Healthcare/finance: Evaluate commercial de-identification vendors against your own corpus.

Before choosing, benchmark on your real prompts. Measure:

• false negatives (PII leaks)
• false positives (destroying useful context)
• latency
• multilingual performance
• whether redaction hurts model quality

A 95% detector that works on your actual traffic is usually better than a “top-ranked” tool tested on unrelated datasets.

Selecting the best PII redaction and content filtering for LLM features depends on whether you need an open-source library to embed in your codebase, a gateway/proxy to sit in front of multi-model traffic, or a managed enterprise platform.

1. Open-Source Libraries (Best for custom code & self-hosting)

• • LLM Guard (by Protect AI): Highly modular, fast, and purpose-built for production LLMs. It covers PII masking/redacting, prompt injection, toxic language, and jailbreak detection. It can run as an independent microservice or a Python package.
• • NVIDIA NeMo Guardrails: Excellent for conversational applications. It uses programmable rails (Colang) to control both topical boundaries and safety/PII filters. Best if you are already in an ecosystem heavily leveraging orchestration frameworks.
• • Presidio (by Microsoft): While not exclusively an LLM tool, Microsoft Presidio is the industry gold standard for programmatic PII detection and anonymization. It integrates cleanly into LLM pipelines to scrub inputs/outputs using custom NER (Named Entity Recognition) models and regex.

2. LLM Gateways & Proxies (Best for API management & zero-code routing)

• • Portkey / LiteLLM: Popular LLM gateways that support plug-and-play security hooks. They allow you to route traffic across providers (OpenAI, Anthropic, local models) while applying virtual keys, caching, and fallback PII masking filters.
• • Aporia / Arthur Shield: Act as real-time firewalls for LLM applications. They intercept requests pre-LLM to run PII redaction and prompt injection checks, and post-LLM to inspect responses for data leakage or hallucinations.

3. Enterprise Platforms & Cloud Native (Best for corporate compliance & security teams)

• • Microsoft Azure AI Content Safety: Offers robust text and image filtering, tightly integrated with Azure OpenAI, but flexible enough for custom endpoints. Includes severity-level scoring for hate speech, violence, sexual content, and self-harm.
• • Lakera Guard: Deeply focused on security vulnerabilities, prompt injection, and data privacy compliance. It’s widely used in enterprise setups requiring low-latency inline scanning.
• • Cloudflare AI Gateway: Great for edge-level visibility. If you manage applications globally, Cloudflare allows you to log, cache, and apply basic security rules/rate-limiting at the edge before requests hit LLM APIs.

Key Capabilities to Look For

1. Bidirectional Masking (Rehydration): Ensure the tool doesn't just redact (destroy) data, but can mask incoming PII with tokens (e.g., <PHONE_1> ) and un-mask/rehydrate the final response back to the user if your UI requires context.
2. Latency Overhead: Regex-based PII scrubbers add <5ms, whereas heavy NLP/transformer-based entity recognition can add 50–150ms per request. Test against your UX constraints.

If you can share whether you are looking for an open-source Python library , an infrastructure proxy/gateway , or a fully managed cloud service , I can narrow down the ideal integration architecture for your stack.

The best PII-redaction and content-filtering tools for LLM features depend on whether you need a dedicated cloud guardrail API, an open-source self-hosted proxy, or a native cloud provider service . Top solutions balance precise entity recognition (names, SSNs, financial data) with low-latency toxicity and jailbreak screening.

Top Solutions Comparison

Key Architectural Approaches

• In-Perimeter Redaction: Tools like Philterd AI run locally or in your private cloud to scrub identifiers (names, phone numbers, locations) and swap them with placeholders ([PERSON]) before data reaches third-party LLM APIs.
• Cloud Native Guardrails: Built-in services like AWS Bedrock Guardrails or Azure AI Content Safety evaluate prompts and model outputs inline for policy violations, PII, and prompt injections.
• Gateway-Level Policy Enforcement: API gateways like Gravitee or specialized AI gateways intercept traffic to dynamically block or redact sensitive data according to GDPR, HIPAA, or CCPA frameworks.

Factors to Evaluate

1. Data Residency: If your compliance requirements dictate that raw data cannot leave your local infrastructure, choose an on-premise proxy over a cloud-hosted guardrail.
2. Modality Support: Ensure the tool can parse the data formats you use (text, structured JSON, or multi-modal images).
3. Latency Overhead: Real-time chat applications require lightweight regex or fast transformer-based PII scrubbers to keep response times acceptable.

If you want to narrow this down, tell me:

• Are you deploying self-hosted models or calling external APIs (like OpenAI or Anthropic)?
• What specific compliance frameworks (HIPAA, GDPR, SOC2) do you need to satisfy?

If you're building LLM features, I’d separate PII protection from content safety rather than looking for one “filter.”

My shortlist

What I'd deploy

User input
   │
   ├── deterministic secrets/PII patterns
   ├── PII NER/classifier
   ├── prompt-injection/jailbreak detection
   ├── content-safety classifier
   │
   ▼
[ redact / tokenize / block ]
   │
   ▼
     LLM
   │
   ├── output PII scan
   ├── content-safety scan
   ├── policy/business-rule validation
   │
   ▼
Response

For PII specifically, I'd favor tokenization over simple [REDACTED] replacement when the model needs to reason about the same person across a conversation. For example:

"Email John at [email protected]"
        ↓
"Email <PERSON_1> at <EMAIL_1>"

Maintain the mapping outside the LLM and restore it only after the model response passes your checks.

Which product I'd pick

• AWS stack: Amazon Bedrock Guardrails is particularly attractive because it combines sensitive-information detection/masking with content filters, denied topics, word filters, prompt-attack detection, and grounding checks.
• Azure stack: Azure AI Content Safety / Foundry guardrails is strong if you're already using Azure/OpenAI. It supports configurable input/output content filtering and Prompt Shields.
• Cloud-independent/self-hosted: Microsoft Presidio is probably where I'd start for PII, combined with your own regex/rules and a separate safety classifier.
• AWS but outside Bedrock: Amazon Comprehend can detect PII and locate entities, with support for English and Spanish; its redaction functionality is oriented toward document/batch workflows.

One important distinction: PII detection ≠ secrets detection ≠ content moderation ≠ prompt-injection detection. A production system should generally have separate detectors for each.

If you tell me your stack (OpenAI API, Azure OpenAI, AWS, GCP, self-hosted, etc.) and whether this is chat, RAG, agents/tool-calling, or document processing, I can give you a concrete recommended architecture and compare the top 3 options on precision/recall, latency, cost, languages, and deployment/privacy.

For PII specifically, I'd favor tokenization over simple [REDACTED] replacement when the model needs to reason about the same person across a conversation. For example:

"Email John at [email protected]"
        ↓
"Email <PERSON_1> at <EMAIL_1>"

Maintain the mapping outside the LLM and restore it only after the model response passes your checks.

Which product I'd pick

Choosing the best PII redaction and content filtering for LLM features depends heavily on whether you want a self-hosted open-source scanner , a programmable dialogue controller , or a managed enterprise API.

The top tools available are categorized below by their strengths:

1. Dedicated Open-Source PII & Security Scanners

• LLM Guard (by Protect AI): The gold standard for open-source zero-dependency input/output scanning. It features robust, built-in PII anonymization/redaction (using NER models and regex), alongside detection for prompt injections, jailbreaks, and toxicity.
  • Best for: Local self-hosting, high privacy (keeping data on-prem), and raw scanning speed before data hits an LLM.

2. Output Structure & Validation Frameworks

• Guardrails AI: An open-source Python framework that uses a composable validator architecture. You wrap your LLM inputs and outputs with specific validators (including PII detection, toxic language, and schema enforcement).
  • Best for: Developers who want programmatic control inside Python codebases to catch and fix bad outputs or mask inputs on the fly.

3. Dialogue Flow & Heavyweight Guardrails

• NVIDIA NeMo Guardrails: An open-source toolkit (Apache 2.0) that uses a specialized language (Colang ) to control conversational flow, topic enforcement, and jailbreak/PII prevention.
  • Best for: Enterprise conversational agents where you need strict topical boundaries alongside security filtering.

4. Enterprise Security Firewalls & Managed APIs

• Lakera Guard: Operates as a real-time enterprise AI security firewall via a single API call to screen for prompt injections, PII exposure, and inappropriate content.
  • Best for: Teams wanting a managed, zero-maintenance API firewall that plugs cleanly into existing application layers without heavy custom infrastructure.
• Azure AI Content Safety: A cloud-native solution providing comprehensive text and image moderation, severity ratings, and safety blocklists integrated into the enterprise stack.
  • Best for: Organizations already building on Azure or needing enterprise compliance SLAs out-of-the-box.

Community Perspectives

When implementing these layers, developers often emphasize the importance of local masking control versus cloud convenience:

“Companies that back open-source products often provide a paid SAAS version with more capabilities. Especially for PII masking, I prefer a local solution; it feels weird to send your data to a remote service for masking, only to have an LLM search for that PII.”

“Enterprises evaluate guardrails on six criteria... Latency and false positives, not whether a model stopped an attacker, are the top pain points, and SOC 2 and EU AI Act readiness are now baseline, not differentiators.”

To help narrow down the ideal choice, tell me:

• Do you prefer an open-source self-hosted tool or a managed commercial API?
• Is your primary concern input PII masking (before sending to the LLM) or output content filtering?

If you mean PII detection/redaction + broader content filtering around LLM inputs/outputs, my default recommendation is:

My shortlist

1. Best general-purpose: Microsoft Presidio

microsoft.github.io is probably where I'd start.

It detects PII using a combination of predefined/custom recognizers, regex, NER and contextual logic, and you can add your own entity recognizers.

Its anonymizer supports:

• Redaction
• Masking
• Replacement
• Hashing
• Encryption
• Reversible deanonymization

That last part is particularly useful for LLM applications: you can turn

"Email John at [email protected]" into

"Email <EMAIL_1>" send the sanitized prompt to the model, then restore the value in the response if appropriate.

Architecture I'd use:

User
  ↓
Input validation
  ↓
PII detector ───────→ audit/metrics
  ↓
PII anonymizer
  ↓
Prompt-injection / safety filter
  ↓
LLM
  ↓
Output safety filter
  ↓
PII detector
  ↓
De-anonymize approved placeholders
  ↓
User

This is much safer than simply asking the LLM, "please don't expose PII."

2. Best enterprise DLP: Google Sensitive Data Protection

cloud.google.com is stronger if you're already on GCP or need enterprise DLP capabilities across lots of data sources.

It supports inspection plus de-identification techniques such as redaction and tokenization/pseudonymization, including preserving referential consistency where that's useful for analytics or AI workloads.

I'd favor it over Presidio when you're dealing with large-scale enterprise DLP, many data types, cloud data stores, and centralized compliance controls.

3. Best AWS-native option

docs.aws.amazon.com can identify PII and supports real-time detection; AWS also provides redaction workflows. It covers things such as addresses, bank-account numbers and phone numbers.

It's attractive if your LLM stack is already heavily AWS-centric.

One caveat: for interactive LLM applications, I'd pay particular attention to the operational characteristics of the redaction path—AWS's documented direct redaction workflow is asynchronous—so it isn't necessarily the cleanest synchronous middleware for every chat request.

But don't use PII detection as your entire content filter

This is the important distinction.

PII redaction answers:

"Does this text contain an email address, SSN, phone number, person's name, etc.?" Content safety answers things like:

"Is this request attempting prompt injection?" "Is this sexual/violent/hateful content?" "Is this asking for prohibited assistance?" "Is this sensitive internal company information?" "Is the model output leaking secrets?" You generally want separate detectors/policies for those.

For example:

                    ┌── PII / secrets
                    │
Input ──→ Gateway ──┼── Prompt injection
                    │
                    ├── Abuse / safety
                    │
                    └── Business-specific policy
                              ↓
                             LLM
                              ↓
                    ┌── PII / secrets
                    │
Output ─────────────┼── Safety
                    │
                    └── Policy validation
                              ↓
                            User

What I'd build today

For a typical production LLM feature, I'd use:

Presidio + deterministic rules + an LLM/content-safety classifier.

Specifically:

1. Regex/deterministic rules for things like SSNs, credit cards, API keys, employee IDs, etc.
2. Presidio for contextual PII such as names, addresses, organizations, phone numbers, etc.
3. Custom recognizers for your company's proprietary identifiers/secrets. Presidio is explicitly designed to support custom recognizers.
4. Semantic safety/content classifier for categories that aren't really PII.
5. Run filtering both before and after the LLM.
6. Prefer tokenization/placeholders over irreversible deletion when the model needs the information to perform its task.
7. Maintain a PII test corpus and measure false negatives—not just precision.

The last point is critical: no PII detector is perfect. Presidio itself notes the fundamental false-positive/false-negative tradeoff.

If I had to pick one: Presidio for the PII layer, with a separate content-safety/guardrail layer around it. For a large enterprise already standardized on GCP, I'd seriously consider Sensitive Data Protection instead.

Architecture I'd use:

User
  ↓
Input validation
  ↓
PII detector ───────→ audit/metrics
  ↓
PII anonymizer
  ↓
Prompt-injection / safety filter
  ↓
LLM
  ↓
Output safety filter
  ↓
PII detector
  ↓
De-anonymize approved placeholders
  ↓
User

This is much safer than simply asking the LLM, "please don't expose PII."

2. Best enterprise DLP: Google Sensitive Data Protection

cloud.google.com is stronger if you're already on GCP or need enterprise DLP capabilities across lots of data sources.

It supports inspection plus de-identification techniques such as redaction and tokenization/pseudonymization, including preserving referential consistency where that's useful for analytics or AI workloads.

I'd favor it over Presidio when you're dealing with large-scale enterprise DLP, many data types, cloud data stores, and centralized compliance controls.

3. Best AWS-native option

docs.aws.amazon.com can identify PII and supports real-time detection; AWS also provides redaction workflows. It covers things such as addresses, bank-account numbers and phone numbers.

It's attractive if your LLM stack is already heavily AWS-centric.

One caveat: for interactive LLM applications, I'd pay particular attention to the operational characteristics of the redaction path—AWS's documented direct redaction workflow is asynchronous—so it isn't necessarily the cleanest synchronous middleware for every chat request.

But don't use PII detection as your entire content filter

This is the important distinction.

PII redaction answers:

"Does this text contain an email address, SSN, phone number, person's name, etc.?" Content safety answers things like:

"Is this request attempting prompt injection?" "Is this sexual/violent/hateful content?" "Is this asking for prohibited assistance?" "Is this sensitive internal company information?" "Is the model output leaking secrets?" You generally want separate detectors/policies for those.

For example:

                    ┌── PII / secrets
                    │
Input ──→ Gateway ──┼── Prompt injection
                    │
                    ├── Abuse / safety
                    │
                    └── Business-specific policy
                              ↓
                             LLM
                              ↓
                    ┌── PII / secrets
                    │
Output ─────────────┼── Safety
                    │
                    └── Policy validation
                              ↓
                            User

What I'd build today

For a typical production LLM feature, I'd use:

Presidio + deterministic rules + an LLM/content-safety classifier.

Specifically: