Best PII-redaction/content filtering for LLM features?

If you mean PII redaction + content safety for production LLM features, I’d avoid looking for one “best filter.” The strongest setup is layered.

My shortlist

What I'd build

For most SaaS products:

User input
   │
   ▼
[PII detector/redactor]
   │
   ├── block high-risk PII
   └── replace with tokens: <EMAIL_1>, <PHONE_1>
   │
   ▼
[Prompt-injection / malicious-content filter]
   │
   ▼
[RAG retrieval]
   │
   ▼
[LLM]
   │
   ▼
[Output PII + safety filter]
   │
   ▼
User

Presidio is particularly attractive if you need control over exactly what constitutes PII. NeMo Guardrails can integrate Presidio and supports PII detection/masking on inputs, outputs and retrieved content, in addition to content safety, jailbreak protection and agent/tool controls.

For an AWS-heavy stack, Amazon Comprehend is convenient: it provides real-time PII detection and supports PII types including names, addresses, phone numbers and account numbers. One important caveat is that its built-in redaction workflow is asynchronous rather than real-time, so I'd verify that it fits an online LLM request path before choosing it as the primary runtime scrubber.

One important design choice

I strongly prefer tokenization over simply deleting PII when the LLM still needs to reason about the information.

For example:

Original:
"Email John at [email protected] about his $4,200 invoice."

LLM input:
"Email <PERSON_1> at <EMAIL_1> about his $4,200 invoice."

Maintain the mapping outside the LLM boundary:

<PERSON_1> → John
<EMAIL_1>  → [email protected]

Then restore tokens only after the model response passes your output checks.

That gives you much better privacy guarantees than asking the model itself to "please don't expose PII."

My picks by scenario

• Maximum control / privacy: Presidio + custom rules + a content-safety classifier.
• Building an agent: NeMo Guardrails is probably my first OSS choice because it can enforce controls around input, retrieval, execution and output rather than merely scanning the final prompt.
• AWS enterprise: Comprehend for PII + your existing AWS safety controls.
• Structured-output application: Guardrails AI is useful as an additional output-validation layer.
• Security-sensitive external-facing product: use a dedicated PII/security gateway plus application-level authorization; don't expect a content filter to solve prompt injection or data exfiltration by itself. Current guidance strongly favors defense-in-depth rather than a single guardrail.

If you tell me your stack (e.g. OpenAI/Anthropic + Python/Node + AWS/Azure/GCP) and whether you need HIPAA/SOC2/GDPR-level controls, I can give you a concrete recommended architecture and compare the top 3 options on latency, cost, accuracy, and deployment model.

What I'd build

For most SaaS products:

User input
   │
   ▼
[PII detector/redactor]
   │
   ├── block high-risk PII
   └── replace with tokens: <EMAIL_1>, <PHONE_1>
   │
   ▼
[Prompt-injection / malicious-content filter]
   │
   ▼
[RAG retrieval]
   │
   ▼
[LLM]
   │
   ▼
[Output PII + safety filter]
   │
   ▼
User

Presidio is particularly attractive if you need control over exactly what constitutes PII. NeMo Guardrails can integrate Presidio and supports PII detection/masking on inputs, outputs and retrieved content, in addition to content safety, jailbreak protection and agent/tool controls.

For an AWS-heavy stack, Amazon Comprehend is convenient: it provides real-time PII detection and supports PII types including names, addresses, phone numbers and account numbers. One important caveat is that its built-in redaction workflow is asynchronous rather than real-time, so I'd verify that it fits an online LLM request path before choosing it as the primary runtime scrubber.

One important design choice

I strongly prefer tokenization over simply deleting PII when the LLM still needs to reason about the information.

For example:

Choosing the right PII redaction and content filtering stack for LLM features depends on whether you want an open-source library to embed in code, a self-hosted proxy/gateway , or a managed cloud API.

The top-tier options available for production use are categorized below by architecture and use case:

1. Dedicated Open-Source PII Masking & Scanners

• Best for: Local privacy control, fine-grained regex + NLP recognition, and zero vendor lock-in.
• Microsoft Presidio: The gold standard for open-source PII detection and anonymization. It uses a combination of regex, pattern recognition, and Named Entity Recognition (NER via spaCy) to identify and mask PII (names, locations, financial data) in unstructured text. Highly extensible.
• LLM Guard (by ProtectAI): A comprehensive open-source scanner toolkit designed explicitly as a WAF (Web Application Firewall) for LLMs. It includes over 15 input scanners (detecting PII, prompt injections, secrets, toxic language) and 20 output scanners.
• Philter AI Proxy: An open-source, self-hosted proxy tailored explicitly to strip PII from outbound LLM prompts, RAG data ingestion pipelines, and fine-tuning sets before data leaves your perimeter.

2. Programmable & Application-Level Guardrail Frameworks

• Best for: Orchestrating conversational safety, blocklisting topics, and integrating directly into agent/LLM runtimes.
• NVIDIA NeMo Guardrails: An open-source toolkit that lets you programmatically define topical, safety, and PII-redaction flows using a specialized modeling language (Colang). Great if you need deep, behavior-based conversational control inside Python apps.
• Guardrails AI: An open-source framework featuring a massive community hub of validators. It allows you to wrap LLM input/output with structural, toxicity, and PII checks using a simple declarative syntax.

3. Enterprise & Cloud-Native APIs

• Best for: Plug-and-play security with zero infrastructure overhead, high throughput, and compliance guarantees.
• Lakera Guard: A high-performance, real-time security API purpose-built to block prompt injections, jailbreaks, and data/PII leakage. Ideal for multi-language stacks (Python, JavaScript/REST).
• AWS Bedrock Guardrails / Azure AI Content Safety: Cloud-native managed options. If you are already building inside AWS or Azure, these allow you to configure PII masking (names, emails, phone numbers) and content filters (hate speech, sexual content, prompt attacks) natively via configuration rather than extra microservices.

Architectural Recommendation

• If you want a lightweight, customizable Python pipeline , pair Microsoft Presidio (for PII scrubbing) with LLM Guard (for prompt injection/toxicity filtering).
• If you require centralized compliance enforcement where individual app teams can't bypass safety rules, look toward a proxy or gateway layer (like Philter AI Proxy or an enterprise gateway pattern).

To help narrow down the ideal tool, tell me:

• Are you deploying self-hosted LLMs or using third-party APIs (OpenAI, Anthropic)?
• Is your stack primarily Python , or do you need a language-agnostic REST API/Proxy?
• Is this for a regulated industry (HIPAA, GDPR, FINRA)?

When handling PII redaction and content filtering for LLM applications, the ideal stack depends on whether you prefer an open-source developer framework , a managed cloud API , or an LLM-specific security gateway.

Top Open-Source & Self-Hosted Frameworks

• Microsoft Presidio : An open-source Python framework tailor-made for PII detection and anonymization. It uses a combination of regex, pattern recognition, and NER (Named Entity Recognition via spaCy) models, making it highly customizable and great for self-hosting.
• Philter (Philterd) : An open-source, Apache-2.0 licensed tool designed specifically for deep PII and PHI (protected health information) redaction. It works well for heavy text processing and running completely inside your own cloud infrastructure.
• NeMo Guardrails / Llama Guard : If your content filtering needs extend beyond PII into topical guardrails, jailbreak prevention, and toxicity filtering, open-source orchestration guardrails from NVIDIA and Meta allow you to intercept prompts and responses locally.

Top Managed Cloud APIs & Enterprise Tools

• Google Cloud Sensitive Data Protection (DLP) : The gold standard for heavy-duty, highly accurate enterprise inspection and de-identification if you are operating within GCP.
• Microsoft Azure AI Language (PII Detection) : Excellent native cloud service counterpart for Azure stacks.
• Private AI : A popular commercial choice offering containerized or API-based PII de-identification supporting over 50 languages and numerous entity types.
• Tonic Textual : Specifically engineered free-text de-identification built explicitly with LLM and RAG pipelines in mind.

LLM Gateway & Observability Masking

• Langfuse Masking : If your primary concern is logging, tracing, and debugging LLM calls without leaking production data to observability platforms, implementing client-side SDK masking ensures sensitive properties or regex patterns are stripped before traces leave your environment.

To help narrow down the best choice, tell me:

• Are you looking for an open-source library to self-host, or a managed API/SaaS?
• Do you need filtering strictly for PII/PHI , or also for toxicity/jailbreaks?

If you mean production guardrails for LLM inputs/outputs—PII redaction plus broader content filtering, I’d shortlist these:

My recommendation

For most teams: Presidio + an LLM security/guardrail layer.

microsoft.github.io is particularly good when PII detection/redaction itself is the primary requirement. It supports recognizers based on NER, regex, rules and checksums, and lets you customize entity types. It can also replace, redact, mask, hash, or encrypt detected PII.

For example:

Input:
"Email Alice at [email protected] and call her at 415-555-1234."

↓ PII detector

"Email <PERSON> at <EMAIL_ADDRESS> and call her at <PHONE_NUMBER>."

If you need prompt injection + PII/data leakage + toxic/unsafe content + agent/tool protection, I'd look at lakera.ai rather than building all of that yourself. Its guardrails cover prompt attacks, PII/data leakage, content violations and agent/tool interactions, with policies that can block or mask detected information.

Architecture I'd use

User / RAG / Tool
       │
       ▼
┌─────────────────────┐
│ Input PII detection │  ← Presidio
│ + prompt injection  │  ← Guardrail service
└──────────┬──────────┘
           │
           ▼
        LLM call
           │
           ▼
┌──────────────────────┐
│ Output PII / safety  │
│ / data-loss filtering│
└──────────┬───────────┘
           │
           ▼
         User

Important: don't only filter the user's prompt. In RAG/agent systems, PII can enter through retrieved documents, tool responses, and tool calls. Modern guardrail systems therefore need to inspect those paths too.

Also, don't treat any PII detector as a complete privacy boundary. Presidio explicitly warns that automated detection cannot guarantee finding all sensitive information.

If I were choosing today

• Open-source / self-hosted: Presidio
• Enterprise LLM security: Lakera / Check Point AI Guardrails
• Need both: Presidio for deterministic PII handling + Guardrails for behavioral/content security
• Highly regulated data: keep the first PII-redaction stage inside your own infrastructure before data reaches the LLM provider, and use pseudonyms/tokens rather than irreversible redaction when the model needs to reason about identity consistently.

If you tell me your stack (AWS/Azure/GCP, Python/TS, OpenAI/Anthropic/etc.) and whether you need input, output, RAG, or agent filtering, I can narrow this down to 2–3 concrete choices and compare accuracy, latency, cost, and self-hosting.

For most teams: Presidio + an LLM security/guardrail layer.

microsoft.github.io is particularly good when PII detection/redaction itself is the primary requirement. It supports recognizers based on NER, regex, rules and checksums, and lets you customize entity types. It can also replace, redact, mask, hash, or encrypt detected PII.

For example:

Input:
"Email Alice at [email protected] and call her at 415-555-1234."

↓ PII detector

"Email <PERSON> at <EMAIL_ADDRESS> and call her at <PHONE_NUMBER>."

If you need prompt injection + PII/data leakage + toxic/unsafe content + agent/tool protection, I'd look at lakera.ai rather than building all of that yourself. Its guardrails cover prompt attacks, PII/data leakage, content violations and agent/tool interactions, with policies that can block or mask detected information.

Architecture I'd use

User / RAG / Tool
       │
       ▼
┌─────────────────────┐
│ Input PII detection │  ← Presidio
│ + prompt injection  │  ← Guardrail service
└──────────┬──────────┘
           │
           ▼
        LLM call
           │
           ▼
┌──────────────────────┐
│ Output PII / safety  │
│ / data-loss filtering│
└──────────┬───────────┘
           │
           ▼
         User

Important: don't only filter the user's prompt. In RAG/agent systems, PII can enter through retrieved documents, tool responses, and tool calls. Modern guardrail systems therefore need to inspect those paths too.

Also, don't treat any PII detector as a complete privacy boundary. Presidio explicitly warns that automated detection cannot guarantee finding all sensitive information.

If I were choosing today

Choosing the best PII redaction and content filtering for LLM features depends on whether you are looking for an open-source developer framework , a lightweight local proxy , or a fully managed enterprise SaaS platform.

A production-grade architecture typically combines deterministic rules (Regex) for structured data like credit cards and SSNs with context-aware ML/NLP models for unstructured names, addresses, and medical identifiers.

Top Open-Source & Developer Frameworks

• Microsoft Presidio : An open-source Python framework and SDK widely considered the gold standard for customizable PII detection, anonymization, and synthesis. It plugs seamlessly into custom LLM and RAG pipelines (and natively integrates with frameworks like LlamaIndex).
• OpenAI Privacy Filter : An open-weight, high-throughput small model designed specifically for context-aware span-tagging and local PII masking before data ever egresses to an API. It handles complex span boundaries exceptionally well without needing heavy compute.
• Philter (by Philterd) : A robust, self-hosted open-source Java/Python engine built explicitly to scrub PII and PHI (Protected Health Information) from free-form text in secure or air-gapped cloud environments.

Top Managed Cloud & Enterprise Ecosystems

• Google Cloud Sensitive Data Protection (DLP) : Excellent for enterprise-scale API inspection and automated risk-scoring of structured/unstructured repositories.
• Microsoft Purview Information Protection : The definitive choice if your stack lives entirely within the Azure/Microsoft ecosystem, offering seamless compliance and AI governance.
• Private AI / Protecto AI : Commercial de-identification platforms available via APIs or deployable containers that specialize in sanitizing high volumes of unstructured text, audio, or document data for LLM and RAG workflows.

Developer Perspectives on PII Redaction

“Production systems should combine regex + ML models — regex for structured PII (fast and auditable), models for freeform text entities.”

To help narrow down the best fit, tell me:

• Are you deploying self-hosted/on-premise or using a cloud API?
• Is your primary target real-time chat text or batch document processing (RAG)?

Yes. For LLM features that need both PII protection and content safety, I’d avoid treating this as one “moderation API.” The strongest architecture is PII detection/redaction + safety/content filtering + application-level policy enforcement.

My shortlist

1. Best overall for an application you control: Microsoft Presidio

Presidio is particularly attractive if PII must be removed before it ever reaches your LLM provider. It's open source, customizable, and can recognize both standard and application-specific sensitive entities. Microsoft explicitly cautions that automated PII detection isn't guaranteed to catch everything, so it should be one layer rather than the entire security system.

A typical pipeline:

User input
   ↓
PII detector / recognizer
   ↓
Deterministic redaction or tokenization
   ↓
Content-safety / prompt-injection filter
   ↓
LLM
   ↓
Output PII scan
   ↓
Output safety filter
   ↓
User

I'd choose this if you're building a vendor-neutral LLM platform and care about keeping raw PII outside third-party model APIs.

2. Best if you're already on AWS: Bedrock Guardrails

This is probably the easiest production answer if your LLM workloads are already in AWS.

Bedrock Guardrails can inspect both prompts and model responses, with configurable sensitive-information policies that can either block or mask PII. It also supports custom regex patterns, which is useful for things like internal customer IDs, account numbers, ticket numbers, etc.

One important caveat: AWS says the PII filter doesn't inspect tool_use output parameters, and invocation logs can contain the original unmasked request. So don't interpret “Guardrails enabled” as “PII can never leak.”

3. Best Microsoft stack: Azure AI Language + Azure AI Content Safety

For an Azure-heavy enterprise, I'd combine Microsoft's PII capabilities with Content Safety rather than trying to make one component do everything.

This becomes particularly compelling if you're also using Microsoft Purview. Purview can apply sensitive-information classification/DLP policies to AI interactions and can help prevent sensitive information from being processed or exposed by AI applications.

4. Best for large-scale DLP: Google Sensitive Data Protection

If your requirement is broader than “protect the chat prompt”—for example, scan documents, databases, logs, embeddings pipelines, and AI inputs/outputs—Google's DLP/Sensitive Data Protection approach is worth considering.

I'd put it in the same architectural category as Presidio, but with more emphasis on enterprise-wide data discovery/governance.

5. Amazon Comprehend is good, but I'd distinguish it from Bedrock Guardrails

Comprehend provides PII detection and toxicity/prompt-safety capabilities. It can identify PII such as names, addresses, bank-account numbers and phone numbers.

However, there's an important distinction: Comprehend's PII redaction is asynchronous, whereas Bedrock Guardrails is designed to intervene directly in LLM requests/responses. AWS specifically notes that Comprehend PII redaction isn't supported in real-time mode.

So for an interactive LLM feature, I'd generally favor Bedrock Guardrails over Comprehend.

---

What I'd actually deploy

For a serious production LLM feature, my preference would be:

PII

Presidio/custom recognizers → deterministic replacement/tokenization

Content safety

Managed safety classifier → block/allow/escalate

Prompt injection

Dedicated prompt-injection detection + strict tool permissions

LLM

Model provider

Output

PII scan → safety scan → policy enforcement

And importantly, scan both directions. Don't only sanitize the user's prompt. LLMs can reproduce PII from context, RAG documents, conversation history, or tool responses.

For agents, I'd go one step further and put controls between agents/tools as well as at the user boundary. Recent research has demonstrated that protecting only the initial prompt can leave unmonitored channels between agents and tools.

My picks by scenario

• Startup / SaaS, cloud-neutral: Presidio + a content-safety API
• AWS: Bedrock Guardrails
• Azure/Microsoft enterprise: Azure AI + Purview
• Highly regulated / sensitive PII: self-hosted Presidio or Private AI + independent safety layer
• Large enterprise DLP across many data sources: Google Sensitive Data Protection or Microsoft Purview
• Need <100 ms latency: favor local/self-hosted classifiers rather than making multiple remote moderation calls; recent research is specifically targeting this latency problem.

If you tell me your stack (AWS/Azure/GCP, model provider, language/runtime, and whether PII must never leave your VPC), I can narrow this to 2–3 concrete architectures and compare cost, latency, accuracy, and deployment complexity.

Choosing the best PII redaction and content filtering stack for LLM features depends on whether you prefer an open-source developer framework , a ready-to-use API/proxy gateway , or a fully managed cloud service.

The top-tier options are categorized below by their deployment model and architecture.

Open-Source & Self-Hosted Frameworks (Best for Custom Control)

• Microsoft Presidio : Widely considered the industry standard open-source framework for backend PII scrubbing. It combines pattern matching (regex), custom dictionaries, and Named Entity Recognition (NER via spaCy or transformers) to detect and anonymize PII. Highly extensible, but requires managing your own microservice infrastructure.
• OpenAI Privacy Filter : An open-weight, high-throughput model designed specifically for local, context-aware PII detection and masking in unstructured text. Because it runs locally, data never leaves your environment before downstream LLM processing.
• LLM Guard (by ProtectAI) : A comprehensive open-source security toolkit tailored for LLM interactions. It includes built-in PII/ANSSI masking alongside prompt injection detection, jailbreak prevention, and toxicity filtering.

Dedicated AI Security & Gateway Proxies (Best for Drop-In Integration)

• Private AI : A commercial de-identification engine available via API or container. It supports over 50 languages and boasts high accuracy on complex or edge-case unstructured data entities.
• Tonic Textual : Built specifically for de-identifying unstructured text before it hits LLM prompts or RAG embeddings, maintaining structural realism if pseudonymization is required.
• Portkey / Lakera Guard / Bedrock Guardrails : If you are routing traffic through an AI Gateway or specific cloud ecosystems (like AWS Bedrock), these platforms offer policy-driven content filtering, guardrails, and real-time masking natively at the proxy layer.

Enterprise Cloud Native (Best for Enterprise Compliance)

• Google Cloud Sensitive Data Protection (DLP) and Microsoft Azure AI Language (PII Detection) : Robust, managed cloud APIs that scale effortlessly if your data stack already lives entirely within GCP or Azure.

Key Architectural Best Practices

1. Layer Regex + NER : Never rely solely on an LLM or a simple regex parser for redaction. Use regex for structured patterns (credit cards, SSNs, phone numbers) and NER/small transformer models for contextual entities (names, unstructured locations).
2. Check Metadata : Don't just scrub the body of a document or prompt; PDF properties, DOCX tracking tags, and file metadata leak PII just as frequently.

If you want to narrow this down, tell me:

• Are you looking for an open-source self-hosted library or a managed commercial API?
• Does your app process real-time chat prompts or batch documents/RAG pipelines?

If you mean “what should I put in front of an LLM to detect/redact PII and filter unsafe content?”, there isn't one universal winner. My shortlist:

My recommendation

For serious production systems, I'd use a layered approach rather than one filter:

User input
   │
   ▼
PII / secret detector
   │
   ├── redact/tokenize sensitive data
   │
   ▼
Content-safety / prompt-injection filter
   │
   ▼
LLM
   │
   ▼
Output safety + PII scan
   │
   ▼
User

Presidio is particularly attractive if PII handling is a core requirement. Its analyzer combines recognizers including regex, deny lists, checksums, rules, NER models, and contextual information, while the anonymizer supports replacement, masking, redaction, hashing, and encryption.

If you're already on AWS, Bedrock Guardrails is probably the easiest all-around choice. It handles both harmful-content categories and PII, with configurable block/mask behavior and custom regexes.

One important caveat: AWS explicitly notes that its PII detector is probabilistic and context-dependent, so I wouldn't treat any LLM-facing PII detector as a perfect security boundary.

Cloudflare AI Gateway is interesting if you want one enforcement layer across multiple model providers; its guardrails operate as a proxy around providers including OpenAI, Anthropic, and others.

One architectural distinction I'd strongly recommend

Don't conflate PII redaction with content moderation.

For example:

• [email protected] → PII detector
• SSN 123-45-6789 → PII detector
• Ignore previous instructions... → prompt-injection detector
• hate/sexual/violent content → moderation classifier
• API keys / credentials → secret detector
• proprietary documents → DLP/access-control layer

For PII that the LLM actually needs to reason about, I'd generally tokenize rather than simply [REDACTED]:

"Email John at [email protected]"

→ "Email [PERSON_1] at [EMAIL_1]"

LLM processing

→ "Email John at [email protected]"

That preserves useful semantics while keeping the actual identifier outside the model context.

If you tell me your stack (e.g. OpenAI/Anthropic + Python/TypeScript + AWS/GCP/Azure/self-hosted), I can narrow this to the 2–3 best choices and compare latency, accuracy, cost, and architecture.

If you're building LLM features that process user/customer content, I’d avoid treating this as just “PII regex.” The best production setup is usually a layered pre-LLM privacy gateway.

My shortlist

My default recommendation: Presidio + deterministic tokenization + a second detector.

Presidio separates detection from anonymization, supporting regex/rules/NER-based recognizers and operations such as replacement, redaction, hashing, masking, and encryption.

Architecture I'd use

User input
   │
   ▼
┌──────────────────────────────┐
│  1. Secret / credential scan │  ← API keys, JWTs, passwords
└──────────────┬───────────────┘
               ▼
┌──────────────────────────────┐
│  2. PII detection            │
│     Presidio + custom NER    │
│     + regex/checksum rules   │
└──────────────┬───────────────┘
               ▼
┌──────────────────────────────┐
│  3. Deterministic tokenization│
│  Alice → <PERSON_7>          │
│  555-1234 → <PHONE_2>        │
└──────────────┬───────────────┘
               ▼
             LLM
               │
               ▼
┌──────────────────────────────┐
│  4. Output scan              │
│  Prevent PII/secrets leaking │
└──────────────┬───────────────┘
               ▼
       Authorized detokenization

The tokenization piece is important. If you simply replace everything with [REDACTED], you can destroy useful context. For example:

“John told Sarah to send the invoice to John.”

becomes much more useful as:

<PERSON_1> told <PERSON_2> to send the invoice to <PERSON_1>.

You can maintain the mapping outside the LLM boundary and restore it only after generation, when appropriate.

Don't rely on one detector

This is the biggest practical lesson.

Use different mechanisms for different classes:

• Regex/checksum: SSNs, credit cards, phone numbers, emails, API keys
• NER: names, organizations, locations
• Contextual/custom recognizers: employee IDs, account numbers, internal identifiers
• Secret scanners: API keys, tokens, credentials
• Document/OCR scanning: screenshots, PDFs, images
• Second-pass semantic detector: unusual or domain-specific PII

Presidio explicitly supports custom recognizers and multiple detection approaches, which makes it a good foundation rather than necessarily the entire solution.

For cloud-heavy environments, Google's Sensitive Data Protection is another strong option; it supports detecting sensitive data and transformations including masking, deletion, surrogate replacement, and encryption.

For LLM features specifically, I'd add two things

1. Treat secrets separately from PII.

An API key isn't necessarily “PII,” but sending it to an LLM can be considerably worse. Scan for:

• API keys
• OAuth tokens
• JWTs
• private keys
• database credentials
• cloud credentials
• connection strings
• session tokens

2. Test against adversarial PII.

Don't benchmark only:

My SSN is 123-45-6789

Also test:

one two three - four five - six seven eight nine

123 45 6789

john dot smith at example dot com

PII embedded in markdown/code/JSON

PII in screenshots

multilingual names/addresses

PII split across messages

PII that requires context to recognize

Recent research specifically evaluating privacy-preserving LLM request techniques found that no single technique dominates; combining local detection/redaction with other transformations can substantially reduce leakage.

What I'd choose

If you're asking “what should I actually deploy?”:

Startup / normal SaaS:
→ Presidio + custom recognizers + secret scanner + reversible tokenization

Enterprise/GCP:
→ Google Sensitive Data Protection + application-level tokenization

Highly regulated / very high-risk:
→ Local/self-hosted detector → deterministic tokenization → LLM → output DLP, with a strong labeled evaluation set.

And I would not make an LLM itself the primary redactor. Let the model detect ambiguous cases if useful, but make the actual replacement/removal deterministic. That gives you reproducibility, auditability, and much lower risk of the “redactor” accidentally rewriting the content it's supposed to protect.

Choosing the best PII redaction and content filtering for LLM features depends heavily on whether you need an open-source developer library , a self-hosted privacy proxy , or a fully managed enterprise gateway/guardrail.

Production systems typically combine deterministic regex (for structured data like credit cards and SSNs) with context-aware NER/small language models (for names, locations, and unstructured mentions).

Top Open-Source & Developer-First Tools

• Microsoft Presidio — The foundational open-source Python SDK for PII detection and anonymization . It combines regex, custom pattern recognizers, and spaCy/Transformers NER. Highly modular, though older spaCy pipelines can struggle with dense or domain-specific text unless paired with modern zero-shot models.
• GLiNER-PII / NVIDIA-optimized variants — A newer favorite for zero-shot named entity recognition supporting 50+ entity types . It offers significantly higher recall and flexibility on out-of-distribution text compared to traditional regex-heavy or rigid spaCy frameworks.
• Philter (Philterd) — An open-source, self-hosted proxy and redaction framework designed specifically for PII and PHI (Protected Health Information). Excellent for medical or highly regulated environments where data must be scrubbed before hitting an LLM or vector store.
• OpenAI Privacy Filter — A high-performance, long-context (up to 128k tokens) bidirectional token-classification model designed for fast, single-pass PII detection and span masking.

Top Enterprise & Managed Platforms

• AWS Bedrock Guardrails — Best if you are embedded in the AWS ecosystem. It provides native dual-stage evaluation (blocking PII/prompts on input and filtering toxic or leaked data on output), plus image/multimodal content analysis.
• Azure AI Language (PII Detection) / Microsoft Purview — Strongest enterprise ecosystem choice for Azure-centric stacks, offering robust pre-built entity detection, compliance logging, and seamless integration with enterprise data governance.
• LiteLLM Guardrails / Bifrost / Maxim AI — Modern LLM gateway and observability layers that natively integrate PII masking (via Presidio or custom backends), prompt injection defense, and content safety moderation right into your routing proxy.
• Private AI / Tonic Textual — Enterprise-grade commercial APIs/containers fine-tuned for high-accuracy multilingual PII de-identification across dozens of languages without leaking data to third parties.

Implementation Strategies: Masking vs. Rehydration

When configuring your filter, choose your strategy based on user experience needs:

• Entity Masking / Tokenization: Replaces real data with typed placeholders (e.g., <PERSON_1>, <EMAIL_ADDRESS>).
• Rehydration (Reversal): Storing a temporary, encrypted session map to swap the original PII back into the LLM's response before displaying it to the user (crucial for conversational or customer-support interfaces).
• Synthetic Data Replacement: Swapping real identifiers for realistic fake data of the same type so the LLM can maintain logical reasoning (e.g., replacing a real name with a fake name).

If you want to narrow this down, tell me:

• Are you deploying self-hosted (open-source) or looking for a managed SaaS/Cloud API?
• Do you need bi-directional rehydration (putting the real names back in the model's response) or just one-way scrubbing?