Best AI Data Security Software for Enterprises in 2026: Our Top 7 Picks for Securing AI Data

Comparing the best AI data security software for enterprises of 2026: 1. Enigma Vault (NoPII), 2. Protecto, 3. Skyflow, 4. Limina (formerly Private AI), 5. Nightfall AI, 6. Prompt Security (SentinelOne), and 7. Lakera (Check Point).

About the author: Abhilash Kumar, Chief Growth Officer at Enigma Vault

I lead growth, positioning, and partnerships at Enigma Vault, after running marketing at Amnic, Element5, and KenSci. Much of my week goes to working sessions with security and AI leaders who want LLMs in production without sending customer data to a model provider, so I see where AI security programs stall: the proxy nobody routed traffic through, the redaction that broke the answer, the audit question nobody could answer. I wrote this guide with our product and security team, who run a PCI DSS Level 1 vault with SOC 2 Type II controls.

Disclosure: I work at Enigma Vault, which appears first in this list. Every platform here is judged on the same criteria, and the review ratings come from G2 and Capterra.

Leading AI Data Security Software at a Glance

Here’s how the 7 platforms stack up for a CISO or Head of AI deciding where each one sits in the data path: browser and endpoint, application proxy, privacy vault, or model firewall. Every rating, price and feature was checked on October 16, 2026.

Mapping which AI data flows your program has to cover? Tell us where customer data meets your models and we’ll walk through the gaps with you. Get in touch

What Is AI Data Security Software?

AI data security software detects sensitive data and tokenizes, redacts, or blocks it before it reaches LLMs, AI apps, and agents, so enterprises can use AI without exposing customer data.

Key Features to Look for in AI Data Security Software

In any demo, these are the things worth checking first:

• A clear place in the data path: Know whether it protects your own API calls, employee browser use, data at rest, or model inputs, because each one covers a different leak.
• Reversible tokenization: A stand-in value your app can swap back keeps answers useful, while a blank like [REDACTED] can’t be restored.
• Consistent identities across turns: The same customer should map to the same token in message one and message ten, or the model loses the thread.
• Fail-closed behavior: If detection fails, the request should stop rather than pass raw data through.
• Detection you can measure: Published entity types, accuracy testing, and custom rules for your own identifiers, since anything unrecognized can slip by.
• Enterprise controls and governance: SSO, roles, per-request audit logs, and policy by app or team, mapped to OWASP, NIST AI RMF, and EU AI Act work.
• Deployment and licensing that fit: SaaS, your VPC, or on-premises, and a price model (per user, per token, platform credits) your finance team can forecast.

Related reading: where PII ends up after you send a prompt, tokenization vs. masking vs. redaction for AI prompts, and why redaction breaks multi-turn LLM workflows.

How We Tested and Ranked AI Data Security Software

Each platform was scored on six criteria, weighted toward the two questions a CISO gets asked first: does the data stay protected, and does the AI still work?

Sources and method. Every figure comes from the vendor’s own product, documentation, trust, and pricing pages, acquisition announcements and SEC filings, or review pages on G2 and Capterra (plus Gartner Peer Insights for added context), read on October 16, 2026. We didn’t run hands-on tests of competing products. Compliance status is what each vendor states, so ask for current reports before you sign.

The 7 Best AI Data Security Software for 2026

NoPII is a reverse proxy you put in front of your LLM API traffic by changing your SDK’s base URL. It detects 30+ configurable entity types and fourteen families of secrets, swaps each value for a deterministic token so it’s tokenized before it reaches the model provider, and restores the real values in the response.

One proxy fronts nine providers under one policy and one audit log. If detection errors, the request is blocked rather than forwarded.

• Keep multi-turn context: the same customer gets the same token in every turn.
• Prove it on live traffic: the free plan covers 1M protected tokens a month.
• Hand auditors evidence: every detection is logged per request.

Key Features

• Deterministic tokenization: “Maria Lopez” gets the same token in turn one and turn twelve, so assistants and agents keep reasoning across steps.
• Round-trip detokenization, streaming included: Real values are restored in the response as chunks arrive, for both standard and streaming SSE responses.
• Secret and API key detection: Fourteen pattern families of credentials, from cloud keys to private keys, are caught in the same pass as PII.
• Fail-safe blocking: If tokenization fails, the request is blocked, so raw data never reaches the provider on a best-effort basis.
• One proxy for nine providers: OpenAI, Anthropic, Gemini, xAI, DeepSeek, Mistral, Groq, Together, and Fireworks, plus LangChain, LangGraph, LlamaIndex, LiteLLM, and the Vercel AI SDK.
• Per-request audit log: Each detection records entity type, confidence score, session ID, provider, model, and timestamp.

Pricing tiers

Pros

• Tokens preserve who is who across every turn, so your assistants and agents keep their reasoning while the provider never sees a real identifier.
• The free plan runs on live traffic, so you can prove detection and latency to your risk committee before procurement even starts.
• Nine providers sit behind one policy and one log, so you keep model choice without rebuilding controls each time.

Cons

• It only sees API traffic your apps send through the proxy, so an employee pasting customer data into ChatGPT in a browser is out of scope; pair it with a browser or endpoint control.
• It only tokenizes what it recognizes, so an internal account format it misses reaches the provider as-is; test on your own records and budget for custom phrase rules on a paid plan.
• It runs as a hosted proxy with no published self-hosted option, and ISO 27001 is still in progress, so residency-bound or certification-gated procurement needs extra review with sales.

What security buyers say in reviews: Very positive. Enigma Vault holds 4.4/5 from 6 reviews on G2 and 4.8/5 from 4 on Capterra; these listings cover the company’s vault products rather than NoPII alone. Reviewers like a simple, easy-to-understand API, clear de-scoping, pricing below alternatives, and how quickly the team added requested features. The complaints are a wish for deeper reporting and analytics dashboards and one reviewer who found pricing a bit high.

Want customer data in your prompts but not at the model provider? Try the NoPII live demo on a sample prompt, or bring your own AI use case and we’ll map the data path with you. Get in touch

Protecto detects PII, PHI, and confidential business data and masks it before it reaches LLMs, agents, or MCP servers. It runs as SaaS (99.9% SLA stated), in your VPC, or on-premises including air-gapped setups, and lists SOC 2 Type II, ISO 27001, and a HIPAA BAA.

Key Features

• Consistent tokens within a namespace: The same customer in your CRM and your warehouse maps to one token, so joins and analytics work without raw PII.
• Context-based access control: Unmasking weighs who is asking, for what task, and what they may see, so supervisors and analysts get different views.
• RAG, MCP, and multi-agent coverage: It protects data flowing through RAG pipelines, MCP tools, and multi-agent systems, not just single prompts.
• Three deployment models: Hosted SaaS, your own VPC, or on-premises with zero data egress for air-gapped environments.
• Healthcare-ready paperwork: A HIPAA BAA is available, alongside SOC 2 Type II and ISO 27001.

Pricing tiers

Pros

• Processing can stay in your own VPC or data center, so residency reviews get much shorter.
• Unmasking follows your identity provider, so analysts, agents, and auditors each see only the view they’re entitled to.
• Consistent tokens across systems mean you can still join masked data for analytics.

Cons

• Unmasking rules tied to directory roles and task context take real design work; get them wrong and agents either see too much or fail on masked fields.
• Accuracy and latency claims are vendor-stated with little independent signal, so you have to benchmark both in a proof of concept.
• Running it in your VPC or air-gapped means your team owns upgrades and scaling, and with no published prices, sizing that cost takes a sales cycle.

What security buyers say in reviews: Protecto has no G2 or Capterra reviews yet, so ask for customer references in your industry. Public commentary comes mostly from analyst write-ups that repeat the vendor’s deployment options rather than user experience.

Need deterministic tokens on live prompts this week, before a long self-hosting project? Get in touch

Skyflow’s LLM Privacy Vault detects and redacts sensitive data during collection, training, fine-tuning, RAG, and inference, and lets authorized users re-identify it. It lists PCI DSS Level 1, SOC 2 Type II, and ISO 27001:2022 and is sold on annual contracts, including AWS Marketplace private offers.

Key Features

• Lifecycle-wide redaction: Sensitive data and intellectual property are detected and redacted from collection through training, fine-tuning, RAG, and inference.
• Re-identification for authorized users: De-identified values can be restored after processing for the people allowed to see them.
• Tokenization and polymorphic encryption: Values are tokenized or encrypted in the vault rather than simply removed.
• Agent data protection: It detects, isolates, and protects sensitive data moving into and out of AI agents.
• Data residency: Regional controls support GDPR, EU AI Act, and DPDP requirements.
• Data platform integrations: AWS, Google Cloud BigQuery, Snowflake, Databricks, Glean, ServiceNow, and HubSpot.

Pricing tiers

Pros

• AI data and stored data share one policy model, so you write access rules once.
• Residency controls help you answer cross-border questions from regulators.
• It covers training and RAG, not just prompts, so data science work stays in scope too.

Cons

• Data has to pass through the vault API before AI processing, so existing pipelines may need significant refactoring for tokenize and detokenize calls.
• Every detokenize call adds a round trip, and there are no public latency benchmarks, so real-time apps need careful architecture and your own measurements.
• Production needs an enterprise agreement with no self-serve tier, so even a pilot runs through procurement.

What security buyers say in reviews: Positive but thin. Skyflow’s Payments Data Privacy Vault listing holds 5/5 from 1 review on G2, and its PII and GenAI listings have no reviews; there’s no Capterra listing. One review is too small a sample to show themes, so lean on customer references.

Want prompt-level protection without a vault program first? Talk to us

Private AI now operates as Limina, and its old domain redirects to the new site. It runs as a container in your VPC or on-premises, and it’s a building block, not a proxy: your engineers wire it in before the LLM call and decide how restoration works.

Key Features

• 50+ entity types: Detection covers PII, PHI, and payment data in one engine.
• 52 languages: Coverage includes regional varieties and code-switching within the same text.
• Four transformation methods: Redaction, pseudonymization, synthetic PII generation, and generalization of entities.
• Self-hosted container: CPU or GPU images run in your VPC or data center, so text never leaves your infrastructure.
• Platform integrations: AWS, Azure, Snowflake, and NVIDIA NeMo.
• Independent validation: The vendor cites validation by Armilla AI and offers expert determination reports through partners.

Pricing tiers

Pros

• Text never leaves your infrastructure for detection, so data sovereignty reviews are simpler.
• Broad language coverage suits multinational support and claims data, so one engine covers every region.
• Synthetic replacement keeps training sets realistic, so model quality holds up after de-identification.

Cons

• It’s a detection engine, not a proxy: you build the gateway, token store, and restore logic, and you own consistency across turns.
• The GPU container needs an Nvidia GPU (Volta or newer) with 16GB of VRAM and a 100GB disk; CPU images run slower, so plan infrastructure cost before you scale.
• It ships in several container flavors, and file processing by URI needs extra volume configuration, so operating it is ongoing work for your team.

What security buyers say in reviews: Mostly positive. Limina’s Private AI listing holds 4.6/5 from 22 reviews on G2; there’s no Capterra listing. Reviewers praise PII detection accuracy, an easy setup, and a simple, elegant approach. The complaints are an interface that could be better and enterprise-leaning pricing and setup for smaller teams.

Rather not build the proxy and token store around a detection engine? Get in touch

Nightfall watches where employees actually use AI, from sanctioned assistants to personal chatbots, browser AI apps, and the endpoint itself. Its model is detect and block at the point of action, which protects the data but doesn’t keep a blocked prompt useful.

Key Features

• Shadow AI prevention: It stops corporate data going to personal ChatGPT, unmanaged copilots, and browser AI apps.
• Endpoint egress control: Clipboard, USB, and local egress are monitored on the device, with policy enforced before data leaves.
• SaaS DLP: Slack, Google Drive, Gmail, Jira, Confluence, Salesforce, Microsoft Teams, SharePoint Online, and other apps are scanned for exposed data.
• MCP gateway: Admins approve specific MCP servers, block the rest by default, and enforce policy on each tool call.
• Prompt injection interception: Prompts, files, and tool responses are inspected in live agent sessions.
• Pre-trained detectors: The vendor states 95% detection precision.

Pricing tiers

Pros

• You see and stop shadow AI use in the browser and on the endpoint, so the leak no API proxy can reach is covered.
• High alert fidelity means fewer false positives, so your analysts spend time on real incidents.
• Pre-trained detectors roll out fast, so coverage starts in weeks, not quarters.

Cons

• Blocking stops the leak but the employee’s AI task fails; it doesn’t tokenize and restore values inside the AI apps you build.
• G2 reviewers cite limited reporting dashboards and few ways to customize alerts, so teams often push events to a SIEM.
• Users must sign in through Chrome for the browser extension, so unmanaged browsers and devices without the agent sit outside coverage.

What security buyers say in reviews: Very positive. Nightfall holds 4.6/5 from 101 reviews on G2 and 5.0/5 from 2 on Capterra. Reviewers like the AI and DLP coverage, strong out-of-the-box policies, easy integration, and Slack alerting. The complaints are reporting and filters that need work, false positives that take policy tuning (sometimes with regex), and alert classification that could be clearer.

Nightfall covers your people; for the LLM calls your product makes, add a tokenizing layer. Get in touch

SentinelOne completed its acquisition of Prompt Security on September 5, 2025, according to its SEC filings, and the product now sits within the Singularity Platform. It governs employee AI use across 15,000+ AI services and code assistants, and for homegrown apps it can redact or block sensitive data before the model sees it.

Key Features

• AI usage control: It discovers shadow AI and code assistants such as GitHub Copilot, Cursor, and Claude Code, and redacts sensitive data in real time.
• Guardrails by policy: Rules cover sensitive data, secrets, prompt injection, and custom topics, with redact or block actions.
• Agentic AI security: It maps agents and MCP servers, finds unsanctioned deployments, enforces least privilege, and keeps a searchable log of agent actions.
• AI application security: Homegrown apps are tested for prompt injection, jailbreaks, and data poisoning, then protected by a runtime AI firewall.
• One SentinelOne console: AI policy and risk sit in the same console as endpoint, cloud, and identity security.

Pricing tiers

Pros

• Endpoint security and AI usage control come from one vendor, so you run one fewer console.
• It covers staff AI use and your own AI apps, so one policy set spans both.
• The browser extension deploys through Intune in minutes, so shadow AI visibility arrives on day one.

Cons

• The roadmap and packaging now follow the Singularity Platform, so shops on another EDR take on a second console.
• Redaction removes values rather than keeping consistent tokens, so multi-turn assistants lose track of who is who.
• Browser-extension controls can miss some file upload paths, so confirm coverage for desktop AI apps and CLI coding assistants in your trial.

What security buyers say in reviews: Positive but thin. Prompt Security holds 5.0/5 from 2 reviews on G2; there’s no Capterra listing. Reviewers like how quickly the extension rolls out and that it spots shadow AI and redacts in real time. They want more dashboard customization, and a Gartner Peer Insights reviewer flags limited report export.

Redacted prompts leaving your assistant confused? Let’s talk tokens

Check Point announced its agreement to acquire Lakera on September 16, 2025 and closed the deal on October 22, 2025. Lakera screens prompts and outputs for prompt injection, jailbreaks, and data leakage, and flags or blocks a request rather than replacing values with restorable tokens.

Key Features

• AI agent security: Runtime protection screens inputs and outputs of AI apps and agents for injection and data leakage.
• Workforce AI security: A separate module covers how employees use AI tools.
• AI red teaming: Lakera Red and an expert-led service test your apps with risk-based adversarial attacks.
• Multilingual and multimodal coverage: Detection extends to indirect prompt injection and attacks across languages and media.
• Low added latency: The vendor states sub-50 ms runtime latency, even on large prompts.

Pricing tiers

Pros

• It’s built around prompt attacks that try to pull data out, so your AppSec team gets a dedicated control for that threat.
• The free tier runs on real traffic, so you can measure detection before you budget.
• Check Point ownership means it can join an existing Infinity contract, so procurement is simpler for Check Point shops.

Cons

• A flagged prompt is stopped rather than tokenized, so the user gets no answer and the values aren’t protected for reuse.
• It’s being folded into Check Point’s broader AI security stack, so standalone buyers face renaming, bundling, and roadmap shifts.
• Every call adds a pre-check, and the latency figures are vendor claims, so measure them on your own traffic.

What security buyers say in reviews: Mostly positive, on a small sample. Since the acquisition, G2 lists Lakera under Check Point AI Agent Security, which holds 4.2/5 from 12 reviews on G2; there’s no Capterra listing. Reviewers like that it checks each tool call and MCP connection an agent makes and blocks only the risky ones, that it finds shadow AI across cloud and low-code platforms, and that policies add little noticeable latency. The complaints are that custom and on-premises agent frameworks take more effort to onboard, documentation lags the fast-moving feature set, and licensing inside the Infinity portfolio is hard to size without going back to Check Point.

A firewall stops attacks; tokens stop exposure. See both working together

Key Features Compared Side by Side

Only Enigma Vault (NoPII) and Protecto keep a customer’s identity consistent across turns without you building the token store, and only NoPII publishes a self-serve price. Each cell comes from the vendor’s own pages, checked October 16, 2026.

Packaging changes often, so confirm the cells that matter most to you in the vendor’s quote.

How to Pick the Right AI Data Security Software for Your Enterprise

In my experience, two questions narrow the list fast: is the data flowing through people using AI or products using AI, and must processing stay in your own infrastructure?

Recommended AI Data Security Software for Each Use Case

Which AI Data Security Software Fits Your Scale

Common Mistakes Enterprises Make When Buying AI Data Security Software

• Buying for the wrong path: An API proxy can’t see a browser, and a browser extension can’t see your backend calls, so map flows first.
• Accepting redaction as good enough: Blanking names often breaks summaries and agents, and teams quietly route around the control.
• Skipping a detection test: Run your own records, including internal IDs and multilingual text, through any detector before you trust it.
• Ignoring failure behavior: Ask what happens when detection times out, because a pass-through default leaks raw data.
• Treating a vendor badge as your compliance: A vendor’s SOC 2 or BAA supports your program; it doesn’t replace your own EU AI Act or NIST AI RMF work.

Smart Questions to Ask an AI Data Security Software Vendor Before You Sign

• Exactly which flows do you protect: API calls, agents, browsers, or data stores?
• Do you tokenize, mask, or block, and can my app restore the original values?
• Does the same person get the same token across a session and across agent steps?
• What happens when detection fails or times out?
• What do your audit logs record per request, and can we export them to our SIEM?
• Which reports can you share today: SOC 2 Type II, ISO 27001 status, and a BAA if we handle PHI?

Ready to Try Enigma Vault’s NoPII?

If your next AI feature needs customer data to be useful, you don’t have to pick between the model and the auditor. NoPII tokenizes detected identifiers before they reach the model provider, restores them in the answer, and logs every decision. Try it in the live demo, compare plans on the pricing page, or book a demo to map your AI data path.