A practical comparison of tools that protect source code and sensitive data from leaking to AI assistants — across deployment model, target user, and lifecycle coverage.
The problem
When developers use AI coding assistants — Claude Code, Cursor, GitHub Copilot, Cody, Aider — they implicitly send source code, comments, and configuration values to a remote server they do not control. For most companies that is a regulatory, contractual, or competitive risk: customer data inside test fixtures, IP inside class names, credentials inside config files, business logic spelled out in comments.
A growing market of products tries to address some part of this. They are not all the same product. Several are not even in the same category. This article maps 13 of them across three axes that actually matter at decision time:
- Deployment model. Does the data leave your network, and to whom?
- Target user. Is this a developer's tool that disappears into the IDE, or a CISO's tool that sits at the network gateway?
- Lifecycle coverage. Does it just block or redact on the way out, or does it round-trip — obfuscate, let the AI work, apply changes back to the real code, and verify the build?
The third axis is where most products in this space stop short.
The three axes in detail
Axis 1 — On-premise vs SaaS
If your threat model is "no proprietary code on third-party servers", then a product that requires sending your prompts through its own SaaS before forwarding to OpenAI/Anthropic is a substitution, not a solution: you swapped one third party for another. The configurations that actually fit this threat model are:
- Strict on-premise: the product runs as a binary, container, or library inside your network. Examples: Presidio, LLM Guard, NeMo Guardrails, PromptCape (CLI mode), Quieta.
- Hybrid proxy: the engine runs locally and sanitizes prompts; the LLM call goes out to whatever provider you choose. Examples: PromptCape (proxy mode), ChatWall Box, ZeusLock self-hosted.
- SaaS-only: the product itself is a cloud service. Examples: GCP DLP, Lakera Guard, Skyflow, Cypher AI, Code Integrity.
A SaaS-only choice is not wrong — it is a different trade-off. If your data already lives in GCP, GCP DLP is a sensible choice. If you cannot send code to a third-party cloud at all, it is a non-starter.
Axis 2 — CISO tool vs developer tool
Two product shapes coexist in this market and they are easy to confuse:
- CISO/governance tools sit at the network egress. They scan outbound prompts for PII, secrets, and policy violations; log hits; raise alerts; produce audit trails. They are bought by security teams and imposed on developers — often visible to the dev only as "the thing that occasionally blocks my prompt." Adoption is enforced top-down.
- Developer tools integrate where the developer already works (IDE terminal, CLI, plugin, library). They aim to be invisible — same workflow, same commands, the obfuscation happens transparently. Adoption is bottom-up, driven by zero added friction.
Most products are clearly one or the other. A few try to be both and end up not great at either. When a developer tool adds friction (extra step, copy-paste, context switch), developers route around it. When a CISO tool is exposed too directly to developers without sandboxing, it gets disabled or bypassed.
Axis 3 — Coverage of the full cycle
For non-code data (a customer name in a chat prompt), redact-on-the-way-out is enough. For code, you need round-trip: the AI's response has to land in the repo with the original names, comments, and structure restored — and the result must compile and pass tests. Otherwise the developer manually patches the AI's output, which destroys the productivity gain that motivated the AI in the first place.
Specifically, full-cycle code protection requires:
- Forward obfuscation that does not break framework conventions (Spring Data derived queries, JPA entity names in JPQL, Lombok-generated accessors, Jackson serialization, Bean Validation, OpenAPI schemas)
- An obfuscated workspace where the code still compiles and tests still pass
- Reverse application that only modifies AI-changed lines (preserves comments and formatting on untouched lines)
- Resolution of names the AI invented during its work (variables it created based on the obfuscated identifiers it saw)
- Build/test verification on the de-obfuscated source
Most products in this list cover none of that. That is not a flaw — they are solving a different problem. Conflating "we redact PII in chatbot prompts" with "we let developers safely use Cursor on a closed-source codebase" leads to bad procurement decisions.
Comparison table
| Product | Deployment | OSS | Domain | Target user | Full code cycle? | Distinguishing feature |
|---|---|---|---|---|---|---|
| ChatWall | Browser extension only (Firefox) | Partial (source-available) | DLP for AI web chat (browser overlay) | End-user | Partial (chat-overlay reversal) | Local-only token substitution for ChatGPT/Claude.ai/Gemini web UIs; very early adoption stage |
| Cypher AI | Hybrid (client-side encryption + vendor or sovereign compute) | No | Encrypted LLM inference (TFHE) | CISO | No | Customer-managed keys; multi-scheme FHE (TFHE / BFV / CKKS / Paillier); 128-bit post-quantum lattice-based; ~400× speedup vs Microsoft SEAL at 10M records; deployments in defense, Tier-1 finance, biometric infrastructure |
| Google Cloud DLP | SaaS (GCP) | No | Generic PII redaction + tokenization | CISO | Partial (de-id reversible, not code-aware) | 150+ infoTypes; native to BigQuery / GCS |
| Lakera Guard | SaaS | No | Prompt-injection guardrails | Mixed | No | Real-time AI/agent attack detection |
| Limina AI | On-prem (VPC container) | No | PII / PHI / PCI redaction | CISO | Partial (PII reversal, not code) | Context-aware detection across 50+ entity types and 52 languages; healthcare/finance positioning |
| LLM Guard (Protect AI) | On-prem | Yes (MIT) | LLM I/O guardrails | Developer | Partial (code is banned, not obfuscated) | ~35 input/output scanners around an LLM call |
| Microsoft Presidio | On-prem | Yes (MIT) | Generic PII redaction | Mixed | Partial (no code reverse) | Pluggable PII recognizers in Python; broadly deployed |
| NVIDIA NeMo Guardrails | On-prem | Yes (Apache 2.0) | Dialogue safety | Developer | No (out of scope) | Programmable Colang DSL for conversational containment |
| PromptCape | On-prem (CLI + proxy) | No | Code obfuscation | Developer | Yes | Java & Python; obfuscate → AI → apply → build verify; Cursor/VS Code terminal integration |
| Quieta | On-prem (desktop app) | No | Generic PII redaction | End-user | Partial (copy-paste workflow) | macOS/Windows app; "paste → mask → AI → restore" in one click, fully local |
| Skyflow | SaaS | No | Data vault + tokenization | CISO | No | "LLM Privacy Vault" for sensitive customer data |
| TitanOne | Both (self-hosted + SaaS) | No | PII redaction for AI | CISO | Partial (no code awareness) | Context-preserving substitution + re-enrichment of LLM responses with original values |
| ZeusLock (Zeus DLP) | Both (EU SaaS + sovereign on-prem) | No | DLP + secrets + Shadow AI detection | Mixed | Partial (no source-code awareness) | Browser extension + CLI + IDE + MCP coverage; blocks AI tools that train on data (Shadow AI Detection) |
Reading the table by axis
By deployment
Strict on-premise (no third-party server in the loop except the LLM you choose to call): Presidio, LLM Guard, NeMo Guardrails, PromptCape, Quieta, Limina (VPC container), ChatWall (purely local extension) — plus the on-prem deployment options of TitanOne, ZeusLock, and Cypher AI (for defense and regulated industries).
SaaS-only (your prompts/data hit the vendor's cloud first, then maybe go to an LLM): GCP DLP, Lakera Guard, Skyflow. Cypher AI is SaaS-by-default but with a cryptographic twist — prompts arrive encrypted under TFHE so the vendor sees no plaintext even on its own servers.
Hybrid proxy (engine runs locally, LLM call goes out to whatever provider you pick): PromptCape, ZeusLock self-hosted. This configuration gives you both control and access to frontier models — the engine is yours, the model is theirs, and the bridge is yours.
By target user
Developer-first (IDE-seamless): PromptCape (Cursor/VS Code terminal profile), LLM Guard (library wrapping LLM calls in your code), NeMo Guardrails (library or local server for LLM apps), ZeusLock (browser extension + CLI + IDE plugins). These integrate where the developer already is and add little or no friction.
CISO/governance-first: Skyflow, GCP DLP, Lakera, Limina, TitanOne, Cypher AI. Strong dashboards, policies, audit trails, compliance certifications. Developers see them as the thing that gates their prompts; adoption requires top-down enforcement.
End-user / individual: Quieta (desktop app for pre-paste anonymization), ChatWall (Firefox extension for browser chat overlays). Bought and installed by individual users one at a time, no enterprise console.
Generic / mixed: Presidio (library that can be wrapped in either direction).
By coverage of the full cycle
For source code specifically, only PromptCape advertises the full cycle: obfuscate the project → AI iterates on the obfuscated workspace → apply only AI-changed lines back → verify the source still compiles and tests still pass. Everything else in this list either:
- (a) does redaction without round-trip (Presidio, GCP DLP, Lakera, ChatWall, ZeusLock when applied to code-containing prompts),
- (b) does round-trip but only on free-text data — not on code that has to compile against framework conventions (Limina, TitanOne, Quieta), or
- (c) is in a different category entirely (NeMo dialogue safety, Cypher FHE inference, LLM Guard input/output scanning).
That gap exists because the round-trip on code is much harder than on text. You have to handle Spring Data derived queries (findByActiveTrue is the query), JPA entity name strings in @Query annotations, Lombok-generated accessor names, comment stripping vs. preserving line counts, AI-invented variable names, build artifacts that should not flow back, and you have to verify the result compiles and passes tests. A redaction library is a few hundred lines; the round-trip is an order of magnitude more work.
Categorical, not directly comparable
It is misleading to put all 13 products in one ranked list — they live in different categories. Roughly:
| Category | Products | What they actually do |
|---|---|---|
| Generic PII / DLP | Presidio, GCP DLP, Limina, TitanOne, Quieta, ChatWall, ZeusLock | Detect sensitive entities in text and redact, mask, or tokenize them |
| LLM I/O guardrails | LLM Guard, Lakera Guard, NeMo Guardrails | Sit between app and LLM; detect prompt injection, jailbreaks, scan input/output for policy violations |
| Data vaults / tokenization | Skyflow | Store sensitive data in a vault; replace with tokens for downstream use including AI |
| Encrypted inference | Cypher AI | Run inference on data that stays encrypted end-to-end (FHE) |
| Code obfuscation for AI coding | PromptCape | Obfuscate source before AI sees it; round-trip back with build verification |
If your problem is "we send PII into a chatbot," go to row 1. If your problem is "prompt-injection or jailbreaks in our AI app," go to row 2. If your problem is "our source code is being trained on by a model vendor," only row 5 answers the question.
A pragmatic decision matrix
Protecting customer PII inside chat or RAG prompts → CISO-driven. Skyflow, GCP DLP, Limina, TitanOne, ZeusLock. Pick on deployment constraints (regulated cloud vs. on-prem VPC), language coverage, and existing data infrastructure.
Protecting against prompt-injection and jailbreaks in your AI app → developer-driven. LLM Guard (free, OSS) or NeMo Guardrails (free, OSS) for in-process; Lakera (commercial SaaS) for managed.
Protecting AI agents that call tools (MCP-style integrations) → developer + CISO. NeMo Guardrails for input-side containment; ZeusLock for MCP-protocol monitoring at the network layer.
Cryptographic guarantee that even the LLM provider — and even the privacy vendor itself — cannot read the data → CISO-driven. Cypher AI: prompts are encrypted client-side under TFHE with customer-managed keys; inference runs on encrypted tensors; only the user decrypts the output. Strong fit for defense and regulated finance. Alternative: self-host a private LLM via AWS Bedrock / Azure OpenAI / Vertex with private endpoints — gives you trust-based isolation rather than mathematical isolation, but no FHE overhead.
Protecting your team's source code while still using Claude Code / Cursor / Copilot every day → developer-driven, full cycle. PromptCape. Java & Python today, more languages on the roadmap. The unique slot in this list — no other product in our set round-trips real code through framework conventions and verifies the build.
Just a desktop or browser tool to clean text before pasting into a web AI → individual user. Quieta (desktop, macOS/Windows), or ChatWall (Firefox extension; very early stage at the time of writing).
Honesty about gaps in this comparison
- The line between "AI security" and "data privacy" is fuzzy and many products straddle it. We have categorized by primary marketing claim, not by every adjacent capability.
- Pricing is omitted from the table because it changes constantly and most enterprise products do not publish it. Where pricing is publicly listed, it is mentioned in the text.
- This is not exhaustive. Notable adjacent products not included: GitHub Copilot Enterprise data controls, Anthropic / OpenAI zero-retention enterprise tiers, AWS Bedrock / Azure OpenAI / Vertex private deployments, Tabnine self-hosted, Cody Enterprise, Sourcegraph Cody on-prem.
- All products were assessed from public web pages on a single research pass. Vendor positioning shifts quickly in this market — verify before purchase.
Conclusion
The AI privacy / code-protection space is crowded but not duplicative. Most products are solving genuinely different problems and only collide on the executive's PowerPoint slide labelled "AI Security."
If you are a CISO setting policy on AI use across the organization, your shortlist is in the generic PII/DLP and LLM I/O guardrails rows. Pick the one that fits your existing stack and compliance regime — most of them are good at what they do.
If you are a developer who wants to keep using AI coding assistants without sending real source code to a third party, the field narrows fast. The full cycle — obfuscate before, work transparently in the IDE, apply back, and verify the build — is currently only addressed end-to-end by PromptCape. That is a technical gap, not a marketing one: round-tripping code through framework conventions and a 3-way merge is harder than redacting names from free text, and the rest of the market has reasonably chosen the easier problem.
Both directions are valid and not in competition with each other. A mature AI strategy probably involves one product from each row of the categorization above: a DLP layer at egress, a guardrail layer around your AI apps, a tokenization layer for stored sensitive data, and — if developers in your org code in the IDE every day with AI assistants — a code-obfuscation layer that closes the loop between the IDE and the model.
PromptCape is open for trial at https://promptcape.com/ — free for 3 months, no credit card required. The companion deep-dive on what makes the Java cycle hard is in Java Code Obfuscation for AI Assistants: Ensuring the Full Cycle Works.
References
Links to the 13 products reviewed in this article, in the order they appear in the comparison table. All URLs verified May 2026.
- ChatWall — Firefox extension for browser AI chat anonymization
- Cypher AI — TFHE-based encrypted LLM inference with customer-managed keys; multi-scheme FHE (TFHE/BFV/CKKS/Paillier); 128-bit post-quantum; ~400× faster than Microsoft SEAL on 10M records; NVIDIA Inception member, validated by 2 independent security agencies
- Google Cloud DLP / Sensitive Data Protection — managed PII redaction and tokenization
- Lakera Guard — real-time AI/agent attack detection
- Limina AI — context-aware PII / PHI / PCI redaction in VPC containers
- LLM Guard — input/output scanners around LLM calls (Protect AI)
- Microsoft Presidio — open-source PII redaction framework
- NVIDIA NeMo Guardrails — programmable conversational containment
- PromptCape — Java & Python code obfuscation proxy for AI coding assistants
- Quieta — local-only desktop anonymizer (macOS / Windows)
- Skyflow — data vault and tokenization
- TitanOne — AI Data Firewall with context-preserving substitution and response re-enrichment
- ZeusLock / Zeus DLP — DLP, secrets detection, and Shadow AI Detection for AI tooling
Top comments (1)
The link to llm-guard has changed and the repo is under : github.com/protectai/llm-guard