Top Runtime Prompt Security Vendors Compared (2026)

In 2026, the way businesses use artificial intelligence (AI) has matured in meaningful ways. AI is no longer an occasional tool for drafting emails or documents. The most critical work in a business now happens in collaboration with AI, which means business data is deeply intertwined with AI systems, whether through employees sending prompts or through agents with access to different parts of the business.

Security teams looking for AI data security vendors need solutions that address today’s risks without getting in the way of productivity. Only a few vendors truly meet that standard.

This article covers key points you must consider when evaluating AI data security vendors in 2026, such as the capability to detect business-sensitive data beyond PII, data preservation techniques that retain critical context, and data security guardrails that enable the work to continue uninterrupted at runtime while keeping the real data secure. It then compares some of the popular solutions in the field.

What is runtime prompt security?

Data security tools have been around for about two decades. Traditional data loss prevention (DLP) tools were built to catch sensitive data leaving a business through known channels, such as email, file uploads and USB drives. They recognise it by patterns, keywords or file labels, and their main response is to allow or block it.

Prompts don’t fit that model. Traditional DLP wasn’t built to read prompts as they move between employees, AI apps and models. Some DLP vendors have since added the ability to read prompts in popular AI apps such as ChatGPT, but their tools still aren’t built to change a prompt to desensitise it, or to read it as part of an ongoing conversation with its own context. That leaves two options: let the sensitive data through to the AI provider, or block the work.

This is where runtime prompt security comes in. It is built to intercept a prompt after it is sent and before the AI model receives it. It looks for sensitive data and for attacks such as prompt injection and applies the guardrails you’ve set, such as replacing the data or rerouting the prompt for safe processing.

What to look for when evaluating runtime prompt security tools

Semantic identification of data

Many tools find sensitive data by its format, such as the 16 digits of a card number, or by tracking where the data is coming from, such as a file marked “Confidential”. This approach misses a large amount of sensitive data, especially more contextual data like intellectual property (IP) or trade secrets. Because this kind of data rarely follows a pattern, the only way for a security tool to catch it is to understand what the sentence means. This is called semantic data identification.

Coverage of business-sensitive data, not only PII

Most products cover personally identifiable information (PII), protected health information (PHI) and payment card data (PCI), because these are regulated categories and they can often be identified through patterns, although with a significant number of false positives. But with AI, where work happens in language, leaks of business-sensitive data like deal codenames, merger and acquisition (M&A) terms, strategy and key accounts are also detrimental to the business. With AI, this data can be leaked or used to train AI models. The prompt security tool must be able to identify and secure this category of data as well for AI adoption to truly be profitable for the business.

Context-preservation instead of masking

Masking, tokenising or redacting might keep sensitive data from reaching the AI model, but it also strips context out of the prompt. The model has less to reason with, and its answers become less accurate. Replacing real data with synthetic stand-ins avoids this. The stand-ins keep the details the task depends on, so the data stays protected while the model is enabled to handle complex work.

Actions beyond block and mask

With two or three actions, every situation gets the same response. A prompt that can be safely rewritten doesn't need to be blocked, data that can't leave the country can go to a model hosted in your environment, and a borderline call can go to the user with the override recorded. Each extra action is one more situation where the work can continue to happen without friction.

Prompt injection detection in live requests

Red teaming an app or agent before deployment can find known weaknesses, but it can't cover new scenarios that might occur at runtime. The Open Worldwide Application Security Project (OWASP) ranks prompt injection first in its Top 10 for LLM Applications. A product needs inline detection on live requests to identify the intent of prompts at runtime, not only testing before release.

The top runtime prompt security vendors in 2026

1. Mavs AI

Mavs AI is a runtime prompt security layer built natively to protect the data that employees, applications and agents send to AI models. It is strongest at identifying sensitive data and at using smart guardrails that let work continue.

Strengths

• Semantic identification of data in each prompt and its attached files, so the same term can be treated as sensitive in one prompt and harmless in another
• Detection that doesn’t depend on keywords, with less overhead for security teams and substantially more accurate results
• Coverage of business-sensitive information, so teams can work deeply with AI on business-critical data
• Realistic synthetic stand-ins that keep the attributes a question depends on, with real values restored in the answer
• Actions beyond blocking and masking through the Mavs Reins AI guardrails, so work continues securely
• Inline prompt injection detection on the same pass as data protection

Limitations

• Focused on business use; it covers employees, applications and agents, where most enterprise data leakage happens
• AI traffic only; it is built natively for prompts and doesn’t inspect non-AI web traffic

Best for: Teams that want to transform how the business runs by integrating AI deeply into real workflows, including work on documents with regulated data, while protecting the data that makes up their competitive moat.

2. Prompt Security (SentinelOne)

Prompt Security is an inline control for employee AI use and AI applications, across browsers, desktop apps and APIs. It is strongest at discovering shadow AI, the AI tools employees use without approval.

Strengths

• Recognition of more than 15,000 AI tools, so security teams can see which ones employees use, including unapproved ones
• Inline prompt injection detection, including jailbreaks, in both prompts and responses
• Protection for AI code assistants such as GitHub Copilot, Cursor and Claude Code, keeping secrets, credentials and proprietary code out of prompts

Limitations

• No automatic detection of business-sensitive data; its built-in categories cover personal data, secrets and code, so information such as deal terms, strategy or key accounts has to be added as custom keywords
• Redaction, not synthetic data; sensitive data is redacted from the prompt, so the model loses the details it was asked about
• Limited actions; it can coach employees on why a prompt was flagged, but what it actually does with the prompt is allow, block or redact it, so a prompt with sensitive data either stops or loses its details

Best for: Organisations that want to discover shadow AI across the business from an established security vendor, where employees mainly use AI for everyday tasks rather than in-depth analysis or complex reasoning.

3. WitnessAI

WitnessAI inspects prompts and responses for both employees using AI tools and the AI applications a company builds. It is strongest at covering employee AI use through the web proxy a company already has, without installing software on every device.

Strengths

• Meaning-based detection through its NER-D model, which catches data with no fixed format, such as trading strategies or code secrets
• Routing of sensitive prompts to secure internal models, so they can still be answered instead of blocked
• Inline prompt injection detection, including many-shot and invisible-character attacks, along with red teaming before release

Limitations

• Network-level only; with no software on devices or in the browser, it sees AI traffic only when that traffic passes through its inspection path
• Some maintenance overhead; beyond its built-in data types, each new kind of company-specific sensitive data has to be described and kept up to date by your team
• Tokens, not synthetic data; sensitive values are swapped for tokens that tell the model nothing about the name, amount or date they replace

Best for: Organisations that want to secure employee AI use through the web proxy they already have, where the AI work doesn’t involve detailed analysis of sensitive values.

4. Protecto

Protecto is a data protection layer that engineering teams build into their AI applications, agents and data pipelines. It is strongest at masking sensitive data consistently across the AI systems a company builds.

Strengths

• Semantic detection across more than 200 data types, including confidential business information
• Actions beyond block and mask, including minimising data or revealing it selectively
• Deployment in the cloud, a private cloud or fully on-premises

Limitations

• Not built specifically for prompts; it applies the same tokenisation it uses for data lakes and pipelines
• API-based; engineers have to integrate every application, agent or pipeline
• Limited employee coverage; only through its own chat gateway, not the AI tools employees already use
• Tokens, not synthetic data; a name becomes a tag such as <PERSON>GHJ6M7</PERSON>, so the model loses the details

Best for: Engineering teams building AI agents and data pipelines on sensitive data who want one masking layer across them.

5. Quilr

QuilrAI is an AI security platform that covers employee AI tools, AI built into business apps, a company’s own AI applications and its agents. It is strongest at breadth, bringing many kinds of AI security into one platform.

Strengths

• Broad coverage, from tools like ChatGPT and Copilot to AI inside Salesforce and ServiceNow
• One platform for discovery, posture management, agent governance and red teaming
• Inline prompt injection detection, including instructions hidden in tool outputs and retrieved documents

Limitations

• Breadth over depth; data protection is one module among many, with basic handling of sensitive data
• Limited business-sensitive coverage; detection partly relies on keywords, so business details written in an ordinary sentence can be missed
• Redacts or blocks only; the model either loses all the details or the work stops in the middle
• No restoring of real values; redacted data stays redacted in the answer

Best for: Security teams that value wide coverage over specific data protection meant for enabling knowledge work or high-level agentic workflows dealing with real sensitive data.

Choosing the right runtime prompt security vendor

Most of the vendors in this list detect prompt injection, and most can find personal data such as names, card numbers and ID numbers. Where they differ is in what happens to everything else in the prompt: whether the tool can recognise business-sensitive data written in an ordinary sentence, and whether the work can continue once that data is found. Blocking or redacting keeps the data safe, but it leaves employees with either no answer or a less accurate one.

The right choice depends on what you want AI to do for your business. If AI is mostly used for everyday tasks, discovering shadow AI and redacting personal data may be enough. But if you want knowledge workers to run their real workflows on AI, and to put it to work on the business-critical tasks that create the most value, you need security that understands what each prompt means, recognises business-sensitive data, and replaces it with synthetic stand-ins so the model keeps the context it needs.

Whichever vendor you shortlist, test it on the work that matters most to your business. Run a few of your teams’ real prompts through each tool and compare the answers. The right one keeps your data secure without making those answers any less useful.