The security puzzle is getting bigger.
What started with employees sending prompts to AI now extends across desktop applications, coding environments, copilots, and autonomous agents. Security teams need to understand each piece before they can secure the whole picture.
The scale of AI usage makes each layer harder to ignore. Check Point Research found that organizations now use an average of 10 different AI applications each month, while Cyberhaven's 2026 research found that 39.7% of interactions with AI tools involve sensitive data, with the average employee entering sensitive data into AI tools roughly once every three days.
For this comparison, we're focused on employee prompt and AI data loss prevention. That means looking at products that can identify and control sensitive information as employees interact with AI, rather than grouping every AI security product into the same category.
Why Security Teams Look Beyond Prompt Security
Prompt Security falls under the Data Loss Prevention (DLP) Software category on G2. Its product provides browser and gateway-level inspection of prompts sent to AI tools, with controls aimed at reducing sensitive data exposure and prompt injection during employee AI usage.
For organizations primarily concerned with employees putting sensitive information into AI tools, that is a meaningful security control.
The question is whether that security boundary matches how the organization actually uses AI.
Search “Prompt Security alternatives” today and you'll find products addressing very different parts of the AI security stack, including employee AI usage, AI discovery, and autonomous agent execution. Those products may solve important security problems, but they aren't necessarily alternatives for an organization looking specifically for employee AI data loss prevention.
That's the distinction this comparison is designed to make.
What Are You Actually Trying to Secure?
The first problem is employee prompt and AI data loss prevention. An employee opens an AI application and enters confidential information. A developer pastes proprietary source code into an AI coding assistant. A lawyer asks an AI model to summarize a sensitive document. A finance employee uses commercially confidential information to generate an analysis.
In each case, the security team needs to control the data entering the AI interaction.
The question is simple: Can employees use AI without sending sensitive company data to the model?
The second problem is AI usage governance and Shadow AI visibility. Here, the concern is understanding which AI applications employees are using, whether those applications are approved, and whether employees are using personal accounts or services outside the organization's governance model.
That is a visibility problem. A platform can tell you that an employee is using an AI application without necessarily inspecting the sensitive information inside the employee's prompts. Both capabilities matter, but they aren't interchangeable.
The third problem is enterprise agent execution security. An AI agent may retrieve information, call a tool, interact with an enterprise application, or pass information between systems without an employee manually entering every step.
The question changes from what the employee typed to what the AI is now capable of doing: What can an AI agent access, what can it do, and where does the data go?
These are different security purchases, even when they sit under the broader AI security category.
For an organization evaluating Prompt Security alternatives, the immediate requirement is usually the first one: preventing sensitive employee data from reaching AI models without breaking legitimate AI workflows.
That brings us to the question that matters most when comparing AI DLP vendors.
Does AI DLP Understand Context or Just Match Patterns?
A feature list can tell you whether a product offers DLP. It doesn't tell you how well it understands the information inside an AI interaction.
Consider this prompt:
"I'm meeting the CEO of a company, we're planning to acquire them for 85 million dollars."
A pattern-based rule could identify the dollar figure. But the dollar figure isn't necessarily the most sensitive part of the prompt. The company being discussed may be an unannounced acquisition target.
Now consider:
"I'm dealing with harassment at work and want to understand how to report it to HR."
There is no obvious credit card number, tax ID, employee ID, or other structured identifier. Yet the interaction contains sensitive personal and potentially legal information.
This is where contextual detection matters. Pattern matching is useful when information has a predictable structure. Contextual classification considers the meaning of the interaction and the relationship between the information in the prompt.
That distinction becomes increasingly important as employees use AI to discuss business decisions, legal matters, customer situations, internal strategy, source code, and other information that doesn't necessarily arrive in neatly structured fields.
It also affects false positives. A number that resembles a sensitive identifier can be harmless in one context and sensitive in another. A system that repeatedly flags legitimate interactions creates friction and alert fatigue, while a system that misses sensitive information creates an entirely different risk.
Wald uses a small language model on the endpoint to classify the context and intent of a prompt before it reaches the destination AI. This matters when sensitive information isn't obvious from the text alone, such as an acquisition discussed in natural language or a workplace harassment report without structured PII.
But contextual detection alone isn't a differentiator. Lasso Security, AccuroAI, and WitnessAI also position their products around intent- or context-aware detection.
The more important question is what happens after something is classified.
Where does the classification happen? What latency does it add? Can sensitive information be blocked, redacted, or allowed without disrupting the employee's workflow? And does that same control extend beyond prompts to desktop AI applications, developer environments, and agent-to-tool interactions?
That is where AI DLP products begin to meaningfully differ.
The point isn't that every competing product literally uses regex. The point is that you should test whether a system can recognize sensitivity when the meaning of the interaction matters more than the structure of the data.
What Actually Separates AI DLP Alternatives?
Once you've established that employee AI data leakage is the problem you're solving, the evaluation needs to move beyond whether a vendor can "detect sensitive data."
The architecture underneath that label determines where sensitive information travels, how quickly policy decisions happen, and what employees experience when a policy is triggered.
Where Does Inspection Happen?
The first architectural question is simple: does sensitive prompt content have to leave the device before the security product can inspect it?
Cloud-proxy architectures route prompts through an external inspection layer before they reach the destination AI. That adds another network step between the employee and the model, while sending prompt content through another environment before it reaches the AI provider. Where inspection happens therefore affects both latency and data residency.
Wald takes an on-device approach. Its small language model runs locally on the endpoint and classifies the interaction before the prompt leaves the device. Sensitive information does not need to be transmitted to a separate security cloud simply for classification.
For regulated organizations, that makes inspection location a data-handling decision, not just a performance consideration.
During a proof of concept, ask where classification occurs, whether original prompt content leaves the endpoint, whether traffic passes through a security proxy, how much latency inspection introduces, and what happens if the inspection service becomes unavailable.
Those answers tell you where the security boundary actually sits.
What Happens When Sensitive Information Is Detected?
Detection is only half of AI DLP. The next question is what happens to the sensitive information once the system identifies it.
A conventional DLP workflow may alert the security team or block the interaction. That can protect the data, but it can also stop an employee from completing a legitimate task.
Imagine an employee needs an AI model to analyze a business document containing a sensitive customer identifier. Blocking the entire prompt protects the identifier, but the employee also loses the ability to complete the task.
Wald approaches this through local redaction. Sensitive information can be replaced with intelligent placeholders before the prompt is sent to the destination AI. The model can still reason over the surrounding context, and the original values can be rehydrated locally when the response returns to the employee.
The intended workflow is:
Original prompt → local classification → sensitive values redacted → sanitized prompt → AI response → original values rehydrated locally
That creates a different employee experience from simply blocking the prompt or stripping the sensitive information in a way that breaks the context.
For a vendor evaluation, this is worth testing with real business workflows. Don't only ask whether a product can prevent data leakage. Ask whether the employee can still do the work.
Can Security Teams Allow, Warn, and Block?
AI usage rarely fits a single binary policy.
Some interactions should proceed normally. Some may warrant a warning. Others should be stopped entirely.
Wald supports three enforcement outcomes: allow, warn, and block.
That gives security teams a graduated enforcement model instead of treating every sensitive interaction as an automatic hard block. It can also make rollout more practical. Teams can introduce warnings, observe legitimate workflows, identify false positives, and tighten controls as they learn how employees actually use AI.
The broader question is how much policy logic the product supports. A useful policy engine should account for more than the presence of a sensitive string. User identity, device state, destination, data type, and other contextual conditions can affect whether an interaction should be allowed.
The exact policy capabilities vary by vendor, so those details should be tested during evaluation.
Does the Product Protect AI Usage Beyond the Browser?
Browser coverage is only one part of the AI environment.
An employee might use ChatGPT in a browser. A developer might use an AI assistant inside Cursor or Windsurf. Another employee might use a standalone AI application or an AI copilot embedded inside desktop software.
Those interactions don't necessarily pass through the same browser layer.
Wald's endpoint agent uses direct OS process interception and is designed to extend protection into standalone desktop applications, AI copilots, and developer environments.
That changes the security question from "Can we inspect AI websites?" to "Can we govern the AI interactions happening across the endpoint?"
For organizations with developers, desktop AI applications, or mixed AI usage, this should be part of the proof of concept. Give employees the applications they actually use and test whether the security policy follows the interaction.
Does AI Security Stop When the Employee Stops Typing?
This is where agentic AI changes the security boundary again.
An employee-to-AI interaction may look like:
Employee → AI model
An agentic workflow can look more like:
AI agent → MCP → tool → enterprise data
The agent may retrieve information, call a tool, or pass information between systems without an employee manually entering every subsequent instruction.
Wald's architecture extends into this layer through a secured MCP Gateway designed to govern agent-to-tool interactions and inspect machine-to-machine data interactions before they leave the environment.
That doesn't mean agent security and prompt security are the same thing. They aren't.
It means the security boundary doesn't necessarily have to end at the employee's prompt.
An organization could prevent sensitive information from entering a chatbot while still needing controls over what an AI agent can retrieve or send through connected tools. For organizations building toward agentic workflows, that becomes an important part of the broader AI security architecture.
Can You Prove What Happened?
For regulated organizations, preventing a data leak is only part of the requirement. Security teams also need to know what policy was triggered, what action was taken, who was involved, and what information is available for investigation.
Auditability therefore belongs in the vendor evaluation from the beginning.
Look at whether the platform provides usable records of policy decisions, what information appears in those records, who can access them, and whether the logs can support internal investigations and compliance requirements. The broader DLP evaluation criteria in the research emphasize audit trails, enforcement records, and reporting by user, data type, and channel.
The useful question isn't simply, "Does it have logs?"
It's "Can our security team reconstruct what happened when a sensitive AI interaction was detected?"
Prompt Security Alternatives Worth Comparing
The shortlist should focus on vendors that address employee AI data leakage rather than every company operating somewhere in the broader AI security market.
Wald AI DLP
Wald AI DLP runs a small language model directly on the endpoint and reads what an employee intends to send before the prompt leaves the device. The model classifies the interaction and applies policy locally.
That architecture creates a different approach to AI DLP. The security decision happens at the endpoint rather than requiring the original sensitive prompt to reach a separate security cloud for inspection.
The detection layer is contextual. An acquisition discussed in plain English can be sensitive because of what the interaction reveals, even if the prompt contains no conventional identifier for the security system to match. The same applies to sensitive situations such as workplace harassment, where the meaning of the interaction matters more than the presence of structured PII.
Wald then acts on that decision through graduated enforcement. Employees can be allowed to proceed, warned about a policy concern, or blocked when the interaction presents a higher risk.
The redaction workflow is another important distinction. Sensitive values can be sanitized before transmission while preserving the surrounding context the AI needs to reason about. When the response returns, the original values can be rehydrated locally.
The endpoint architecture also extends beyond browser traffic. Wald is designed to cover standalone AI applications and developer environments such as Cursor and Windsurf, making it relevant for organizations where AI usage happens across the endpoint rather than only inside browser tabs.
And that security boundary can extend again into agentic workflows through Wald's secured MCP Gateway, which governs agent-to-tool interactions and machine-to-machine data movement.
For a buyer specifically looking for a Prompt Security alternative, the relevant layer is Wald's AI DLP. For an organization planning beyond employee prompts, the endpoint and MCP capabilities become part of the broader architecture.
Best fit: regulated organizations that need sensitive data protected before it reaches external AI models, while still allowing employees to use AI across browser, desktop, and developer environments.
Harmonic Security
Harmonic Security belongs on the shortlist for teams comparing endpoint-based AI security approaches.
Its positioning around employee AI usage makes it relevant when the organization wants control closer to the endpoint rather than relying exclusively on browser or network inspection. The useful comparison is how its current implementation handles prompt inspection, enforcement, latency, and the AI environments employees actually use.
Those capabilities should be validated directly during a vendor evaluation rather than inferred from category-level positioning.
Nightfall AI
Nightfall AI is relevant when AI DLP needs to sit within a broader enterprise DLP strategy.
Its positioning spans SaaS, APIs, and data infrastructure, with AI among the covered channels. That makes it a natural consideration for organizations that want AI protection alongside their existing data protection program rather than as a completely separate AI-specific control.
The trade-off is breadth versus specialization. If the organization's priority is unified DLP across SaaS and AI, Nightfall's broader coverage may be attractive. If the priority is deep control over employee AI interactions at the endpoint, the evaluation should put more weight on endpoint coverage, inspection location, latency, and how the product handles desktop AI workflows.
LayerX
LayerX is relevant when browser-based AI usage represents the organization's primary exposure.
Its browser-centric approach can make sense when employees primarily interact with AI through web applications and the security team wants to control those interactions at the browser layer.
The important test is what happens outside that environment. Developers using AI inside an IDE and employees using standalone desktop AI applications create interaction surfaces that shouldn't be ignored simply because browser coverage is strong.
If browser usage represents most of the organization's AI activity, LayerX may be a sensible fit. If AI usage is spread across browsers, desktop applications, and development environments, endpoint coverage becomes a more important evaluation criterion.
Lasso Security
Lasso Security is worth evaluating when intent-based detection is central to the buying criteria.
Its positioning around intent-based security makes it relevant to the contextual detection discussion, but similar language across AI security vendors means a feature-page comparison isn't enough.
A hands-on bake-off is more useful. Run the same acquisition scenario, the workplace sensitivity example, and other ambiguous prompts through both systems. Compare what each product detects, how it enforces the resulting policy, how much latency it adds, and whether the employee can continue working.
Quilr
Quilr takes a broader approach across browser, LLM gateway, and MCP environments.
That makes it relevant for teams that want AI interaction visibility across several surfaces and need MCP coverage as part of the deployment.
For an organization focused specifically on employee prompt leakage, the question is whether broader centralized coverage provides more value than deeper endpoint control at the point where sensitive information enters the AI interaction.
For organizations already planning around MCP and agentic workflows, that broader coverage may carry more weight in the decision.
So Which Prompt Security Alternative Is Right for You?
The right answer depends on the security boundary you're trying to control.
Wald AI DLP is the strongest fit when sensitive data entering AI interactions is the immediate concern and the organization values on-device classification, contextual detection, local redaction, graduated enforcement, and coverage across desktop and browser AI environments. It becomes particularly relevant when the organization's AI roadmap also includes agentic workflows and MCP governance.
Harmonic Security is worth evaluating when endpoint AI usage control is central to the shortlist and the security team wants a direct comparison of its current capabilities against other endpoint-focused approaches.
Nightfall AI makes sense when AI DLP needs to sit within a broader enterprise DLP strategy spanning SaaS, APIs, and data infrastructure.
LayerX makes sense when browser-based AI usage represents the dominant exposure and browser-level enforcement matches how employees actually work.
Lasso Security makes sense when intent-based detection is central to the evaluation and the team wants to compare competing approaches through a real-world proof of concept rather than positioning language.
Quilr makes sense when broader coverage across browser, LLM gateway, and MCP traffic matters as much as prompt-level protection.
And staying with Prompt Security can still be the right decision. If its existing controls already fit the organization's workflow and no meaningful gap has been identified, switching vendors simply to have a different feature set doesn't create value.
The point of evaluating alternatives isn't to replace Prompt Security for the sake of replacing it. It's to determine whether the current architecture gives the organization enough control over where AI interactions happen, how sensitive information is handled, and what happens next.
What Changes When You Migrate From Prompt Security to Wald AI DLP?
A move from Prompt Security to Wald changes more than the vendor name. The architectural difference is primarily where AI interaction inspection happens.
Wald AI DLP is designed as an additive endpoint layer alongside traditional DLP rather than requiring organizations to abandon their existing controls for email and conventional endpoint data protection. The AI-specific inspection moves toward local processing through a small language model running on the device.
Deployment can use existing MDM or group policy infrastructure to push the Wald agent without requiring network proxies or VPN tunnels.
Policy configuration also changes the enforcement model. Wald supports allow, monitor, warn, and block actions, with policies applied across devices, browsers, data types, and AI applications.
The exact process for migrating existing Prompt Security policies should still be confirmed during the vendor evaluation. Rather than assuming that existing policies map one-to-one, security teams should translate the controls they actually use into equivalent test cases and compare the resulting behavior.
A practical migration evaluation should include structured sensitive data, commercially confidential information that relies on context rather than obvious PII, browser-based AI, desktop AI, developer environments, legitimate workflows containing sensitive data, and agent-to-tool interactions.
For every scenario, record detection, policy decision, latency, data transmitted, and employee experience.
That gives IT and security teams evidence for the migration decision rather than relying on a feature checklist.
FAQ
What is the difference between Prompt Security and AI DLP?
Prompt Security and AI DLP overlap, but they aren't necessarily the same thing.
Prompt security generally refers to controls around AI interactions, including inspection and policy enforcement. AI DLP focuses specifically on preventing sensitive data from reaching AI systems.
The distinction is therefore less important than the implementation. Ask what the product inspects, where inspection happens, what it detects, what data leaves the endpoint, and what happens when sensitive information is found.
How do prompt security tools work?
Prompt security tools can inspect an interaction before it reaches an AI service, evaluate it against security policies, and then allow, warn, or block it.
AI DLP can add another layer by identifying sensitive information within the interaction and preventing that information from reaching the model. Some approaches can sanitize rather than block. With redaction and rehydration, sensitive values are replaced before transmission and restored locally in the response shown to the employee.
The architecture matters because inspection can happen at the browser, gateway, network, or endpoint layer.
What should I look for in an AI DLP alternative to Prompt Security?
Start with the security boundary. Ask where inspection happens and whether sensitive prompt content needs to leave the endpoint for classification.
Then evaluate detection. Does the system understand contextual sensitivity, or does it depend primarily on predefined patterns?
Next, look at enforcement. Can security teams allow, warn, and block based on risk, or does every policy violation result in the same action?
Then test the environments employees actually use, including browsers, desktop applications, and developer environments. Finally, evaluate auditability, access controls, response inspection, data handling, and support for agent workflows where relevant.
What are AI prompt security best practices for enterprises?
Start with the AI workflows employees actually use. Identify which data types are sensitive, which AI applications are approved, and where employees are most likely to encounter sensitive information.
Use real prompts and edge cases during testing rather than relying only on synthetic examples. Context matters because sensitive information isn't always expressed through structured identifiers.
During rollout, graduated enforcement can help security teams understand legitimate AI usage before introducing broader blocking. An audit trail should also be part of the initial architecture decision so security teams can reconstruct what happened when a policy was triggered.
As AI agents become more common, extend the same thinking beyond the employee's prompt to the tools, data, and actions that follow.
What are the best AI prompt security solutions for stopping data leakage, not agent monitoring?
If the immediate requirement is stopping sensitive employee data from reaching an AI model, evaluate AI DLP and prompt security products rather than choosing a vendor primarily designed for autonomous agent execution security.
Wald AI DLP, Harmonic Security, Nightfall AI, LayerX, Lasso Security, and Quilr are relevant options for different deployment and coverage requirements.
The distinction matters because agent security answers a different question. If the requirement is "How do we stop an employee from sending sensitive information to an AI model?", prioritize prompt inspection, contextual detection, enforcement, data correction, and endpoint coverage.
If the requirement is "What can an autonomous AI agent access and do?", evaluate agent and MCP controls.
For organizations that need both, the longer-term goal is to protect the entire AI interaction, from what an employee sends, to where they use AI, to what an agent does on their behalf.