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Comparison

Written by the Data Prism maintainer; as of 2026-09-23.

People evaluating a privacy or safety layer for LLM agents often land on Data Prism next to a handful of other projects. This page places it next to the ones it gets confused with most, states what each one actually does in its own words, and says plainly where Data Prism does not fit. Every sentence below about another project links to that project's own docs or repository, with the date it was read. Nothing here judges another project's quality, accuracy or performance — only what it is built to do.

Different layers

What it operates on Typical integration Language / runtime
Data Prism Structured JSON responses from an internal REST API, before an MCP client or LLM agent sees them MCP server, or embedded as a Spring Boot starter, in front of the source API Java, Spring Boot
Microsoft Presidio Free text and images (presidio-analyzer/anonymizer/image-redactor); tabular or semi-structured JSON data (presidio-structured) Python library, an HTTP service, or Docker/Kubernetes Python
LLM Guard The prompt sent to an LLM and the completion it returns Python scanners wrapped around a model call Python
NeMo Guardrails The dialog between an application and an LLM (topics, flows, tool calls) Python library configured with Colang rails Python
MCP gateways / proxies The MCP transport: which servers and tools a client can reach, secrets, auth A gateway process in front of one or more MCP servers Varies by project

These are different layers of the same stack, not substitutes for one another: several of them could sit in front of the same LLM agent at once.

Microsoft Presidio

Presidio is an open-source framework that identifies and de-identifies personal data using named-entity recognition, regular expressions, rule-based logic and checksum recognizers, distributed as separate Python packages: presidio-analyzer for text, presidio-anonymizer for de-identifying what the analyzer finds, presidio-image-redactor for images, usable from Python or PySpark, or as a Docker/Kubernetes service (Presidio docs, accessed 2026-09-23: https://data-privacy-stack.github.io/presidio/). Presidio's own documentation states plainly that, because it uses automated detection, "there is no guarantee that Presidio will find all sensitive information" and that additional systems and protections should be employed (same page, accessed 2026-09-23). The presidio-anonymizer package applies operators — including replace, mask, hash, redact and encrypt — to detected entities, and can also reverse (deanonymize) an encryption operation (Presidio anonymizer docs, accessed 2026-09-23: https://data-privacy-stack.github.io/presidio/anonymizer/). A fourth package, presidio-structured, extends this to tabular formats and semi-structured JSON: it uses presidio-analyzer to map columns or keys to the PII entities they contain, then presidio-anonymizer to de-identify the values found, with its documented examples built around pandas DataFrames (Presidio structured docs, accessed 2026-09-23: https://data-privacy-stack.github.io/presidio/structured/). Presidio's own documentation calls this de-identification and anonymization — terms used there in their general sense, not the GDPR Article 4(5) sense that shapes how Data Prism describes its own pseudonyms (see the FAQ); this page takes no position on whether Presidio's output meets that or any other legal definition.

Use Presidio instead when you need entity-level PII detection — names, locations and more, found by NER, regex or checksum rather than by a fixed data-classification catalogue — over free text or images with presidio-analyzer/image-redactor, or over tabular or JSON data with presidio-structured, typically from a Python or PySpark stack, rather than a Java/Spring MCP server sitting in front of a live REST API.

LLM Guard

LLM Guard is a Python security toolkit, installed with pip install llm-guard, whose own documentation describes it as offering sanitization, harmful-language detection, data-leakage prevention and resistance to prompt-injection attacks, through separate input and output scanners applied to the prompt sent to an LLM and the completion it returns (LLM Guard docs, accessed 2026-09-23: https://protectai.github.io/llm-guard). Its Anonymize input scanner detects PII entities — including person names, emails, phone numbers, credit card numbers and IP addresses — in that prompt text itself, before it reaches the model (LLM Guard Anonymize scanner docs, accessed 2026-09-23: https://github.com/protectai/llm-guard/blob/main/docs/input_scanners/anonymize.md).

Use LLM Guard instead when you need to scan free-text prompts and completions themselves — including ones a human typed directly — for PII or harmful content in a Python stack, rather than pseudonymise structured fields coming out of an internal API before an agent reads them.

NeMo Guardrails

NeMo Guardrails is an open-source Python toolkit for adding programmable guardrails to LLM-based conversational applications: rails that keep a model on-topic, follow a predefined dialog path, or use a particular language style, configured with the Colang language and installed with pip install nemoguardrails (NeMo Guardrails README, accessed 2026-09-23: https://github.com/NVIDIA-NeMo/Guardrails). It requires Python 3.10 through 3.13 (same README, accessed 2026-09-23).

Use NeMo Guardrails instead when what you need to control is the shape of the conversation itself — topics, dialog flow, tool-call policy — in a Python application, rather than what a structured API response is allowed to expose to the model in the first place.

MCP gateways and proxies

An MCP gateway or proxy sits in front of one or more MCP servers and gives a client a single place to reach them. Docker's MCP Gateway, one example of this category, aggregates multiple MCP servers behind one interface, manages each server's container lifecycle and isolation, handles secrets and OAuth flows for the servers behind it, and provides dynamic tool discovery, logging and call tracing (Docker MCP Gateway README, accessed 2026-09-23: https://github.com/docker/mcp-gateway). Its security documentation describes a --block-secrets option, enabled by default, that "scans tool-call arguments and text responses for secret-like values before and after tool execution" (Docker MCP Gateway security docs, accessed 2026-09-23: https://github.com/docker/mcp-gateway/blob/main/docs/security.md) — a scan for secret-shaped values passing through the gateway, not the data-classification and pseudonymisation Data Prism applies to a source API's own response fields.

Use an MCP gateway or proxy instead when what you need is a single endpoint for multiple MCP servers, credential and OAuth handling for those servers, or tool discovery across them — not a privacy layer for what one server's tool call returns.

Where Data Prism does not fit

  • Free-text prompts. Data Prism classifies and pseudonymises fields in a structured API response (architecture.md); it is not built to scan an arbitrary free-text prompt or completion the way LLM Guard's Anonymize scanner or Presidio's analyzer does.
  • Python stacks. Data Prism ships as Java/Spring Boot artifacts — a standalone MCP server or a Spring Boot starter (README.md). There is no Python package, and nothing here integrates with a Python LLM application the way Presidio, LLM Guard or NeMo Guardrails do.
  • Anonymisation or re-identification needs. Data Prism's pseudonyms are still personal data under GDPR Article 4(5) — see the FAQ. Re-identification is never exposed as an MCP tool at all — a stated boundary, not just an unbuilt feature — and today that boundary is enforced only in prose: the separate, authorised re-identification application it describes does not exist yet (architecture.md).
  • Non-JSON sources without a Java adapter. The YAML-only connector covers flat or one-level-nested JSON REST responses only (protect-your-own-api.md); a source that is not JSON, or nests deeper than one level, needs a reviewed Java DataSourceAdapter (extending.md) — there is no no-code path for it.