Introduction
Generative AI adoption often moves faster than security governance. Employees may use public AI tools without approval, developers may connect assistants to proprietary repositories, and product teams may launch AI applications or agents before security has a complete inventory.
Prompt Security is designed to close that gap. The platform provides visibility and policy enforcement across employee AI use, homegrown AI applications, code assistants, and agentic systems. It combines shadow AI discovery, sensitive data protection, in-browser coaching, runtime AI guardrails, automated red teaming, and Model Context Protocol controls in one AI security platform.
Prompt Security is also now part of SentinelOne. That matters because the product can be evaluated both as a focused AI security platform and as part of a wider enterprise security ecosystem. This Prompt Security review examines its capabilities, deployment model, pricing approach, security considerations, limitations, and strongest alternatives.
What Is Prompt Security?
Prompt Security is an enterprise AI security platform for governing how employees, developers, applications, and autonomous agents interact with AI. It is not a prompt management tool for writing or versioning prompts. Its purpose is to discover AI usage, prevent sensitive data exposure, detect AI-specific attacks, enforce policy, and create audit evidence.
The platform covers four major AI security surfaces:
- Employee use of public and embedded AI tools
- Homegrown applications built with commercial or private models
- AI coding assistants used in browsers, IDEs, and development workflows
- AI agents, custom GPTs, tools, and MCP servers
This breadth is Prompt Security’s main differentiator. Rather than protecting only employee prompts or production applications, it connects workforce governance, application security, developer controls, and agent oversight through one policy layer.
AI Security Platform
Core Prompt Security Capabilities
Prompt Security is best understood as a set of connected enforcement points rather than one isolated AI firewall. The browser extension covers employee interactions, the AI Gateway protects application traffic, developer controls address code assistants, and the MCP Gateway governs agent-to-tool activity.
1. Shadow AI Discovery and Employee AI Governance
The employee security module discovers AI services used across the organization and shows which applications, users, and departments create the most risk. This includes approved tools and unsanctioned shadow AI that may never appear in procurement, SSO, or expense data.
The browser-based approach gives Prompt Security context at the point where an employee submits a prompt, uploads a file, or interacts with an AI-enabled web application. Administrators can create granular policies by user, department, application, or data type instead of relying on a universal blocklist.
Why this capability matters
- Finds AI use outside the approved application catalog
- Separates low-risk experimentation from high-risk data handling
- Creates searchable activity records for governance and audits
- Supports different policies for legal, engineering, finance, and other teams
This is more useful than simply counting AI domains. Security teams need to understand who used the tool, what policy applied, what sensitive information was involved, and whether the interaction was allowed, redacted, coached, or blocked.
2. Sensitive Data Redaction and Employee Coaching
Prompt Security can detect sensitive information before it reaches a third-party model. Depending on policy, the platform can redact, anonymize, tokenize, warn, or block. This can cover personally identifiable information, credentials, intellectual property, proprietary code, and organization-specific sensitive patterns.
The coaching layer can explain why an action is risky and guide the employee toward a safer option. This provides just-in-time awareness instead of relying only on a traditional DLP rejection message.
Buyers should still test accuracy with realistic internal data. Classification rules, context, language, file type, and policy design all affect redaction quality and user friction.
3. AI Gateway for Homegrown Applications
Prompt Security’s AI Gateway sits between an application and the model or connected service. It inspects inbound prompts and outbound responses to detect prompt injection, jailbreaks, data leakage, denial-of-wallet patterns, unsafe content, prompt leaks, and other AI-specific risks.
The gateway can filter or obfuscate sensitive information before it reaches a commercial model or vector database. It can also moderate harmful, inappropriate, or off-brand responses before they are shown to end users. Full interaction logging supports investigation, testing, and compliance workflows.
Prompt Security is model-agnostic and supports cloud, VPC, self-hosted, and on-premises deployment options. This flexibility is important for organizations using several model providers or running private models for regulated workloads.
Where the AI Gateway adds the most value
- Customer-facing chatbots handling account or support data
- Internal assistants connected to private documents or knowledge bases
- Applications switching between multiple commercial and private models
- Regulated workflows requiring logging, filtering, and policy evidence
The gateway should still be treated as one defense layer. Secure system design also requires identity controls, least-privilege data access, safe output handling, application security testing, model evaluation, and incident response.
4. Security for AI Code Assistants
Developers increasingly use tools such as GitHub Copilot, Cursor, Claude Code, and browser-based coding assistants. These products improve speed, but they can also receive API keys, credentials, customer data, source code, and proprietary business logic.
Prompt Security can sanitize code and prompts before submission, detect secrets and PII, and provide visibility into AI usage across development workflows. The vendor states that its coverage extends across thousands of web AI tools, dozens of coding assistants, and nearly 30 programming languages.
The advantage is consistent policy across different developer tools. Because environments vary, a proof of concept should include IDE plugins, terminal assistants, repository workflows, and the operating systems your developers use.
5. Agentic AI and MCP Gateway Security
Agentic AI changes the risk model because an AI system can do more than generate text. An agent may read files, call APIs, modify records, send messages, run code, or invoke tools through the Model Context Protocol. A successful prompt injection can therefore become an unauthorized action rather than an incorrect answer.
Prompt Security’s MCP Gateway is built to discover MCP usage, identify shadow deployments, assess server risk, inspect requests and responses, and enforce allow, block, filter, or redact decisions. Policies can be applied by user, server, tool, or action, while searchable logs provide evidence of what the agent attempted and what the platform allowed.
This is one of Prompt Security’s most strategically important areas. The platform has moved beyond prompt filtering into machine-level governance for agents and tools. Buyers should still evaluate whether it can enforce least privilege across their specific agent frameworks, identity systems, credentials, and rollback requirements.
6. Automated AI Red Teaming
Prompt Security provides automated red teaming for homegrown AI applications. The service tests for risks such as prompt injection, jailbreaks, data exposure, privilege escalation, harmful content, prompt leakage, unsafe agent behavior, and denial of service or excessive model spending.
Its strongest workflow connects testing with runtime protection. Findings can inform remediation and production guardrails, while continuous evaluations help identify drift as models, prompts, tools, or application logic change.
Automated testing does not replace expert review. Prompt Security works best when red teaming supports a wider secure development process rather than a one-time compliance exercise.
Pros and Cons
Advantages and Disadvantages
Prompt Security offers unusually broad AI security coverage, but its enterprise focus, custom pricing, and reliance on careful policy tuning mean it will not suit every organization.
Positive
✅ Covers employees, developers, apps, and agents
✅ Enforces policy at the point of AI interaction
✅ Supports redaction, blocking, and user coaching
✅ Works across major models and AI tools
✅ Offers cloud, VPC, and on-premises options
✅ Connects red teaming with runtime protection
Negative
❌ No transparent standard pricing
❌ Policy tuning requires cross-functional work
❌ Broad browser telemetry needs privacy review
❌ May be excessive for a single AI application
❌ Public peer review volume remains limited
👍 Pros
✅ Broad coverage across the enterprise AI lifecycle
Prompt Security covers employee usage, coding assistants, custom applications, red teaming, agents, and MCP servers, reducing the need for separate policy systems.
✅ Real-time controls instead of reporting only
The platform can redact, anonymize, coach, block, or allow activity as it happens. This is more valuable than discovering an unsafe interaction after sensitive information has already reached an external model.
✅ Flexible deployment and model independence
SaaS, VPC, self-hosted, and on-premises options support data residency requirements, while model-agnostic controls reduce dependence on one LLM vendor.
✅ Strong connection between testing and production
Automated red teaming findings can inform runtime policies. That creates a more operational security loop than platforms that provide either testing or gateway protection without connecting the two.
✅ Employee coaching can reduce friction
Just-in-time explanations help users understand why a prompt, file, or data element is risky. This supports safer adoption without making an outright ban the default response to every new AI tool.
👎 Cons
❌ Pricing is difficult to estimate without a sales process
Prompt Security does not publish simple per-user or per-request rates. Buyers must define employee, application, developer, deployment, red-teaming, and agent requirements before receiving a meaningful quote.
❌ Effective policies require tuning and ownership
Security, privacy, legal, engineering, and business teams must agree on approved tools, sensitive data, exceptions, response actions, and retention requirements.
❌ Browser and interaction monitoring requires careful communication
Prompt and response visibility is sensitive. Define what is collected, who can access logs, retention periods, and how employees are informed before rollout.
❌ Smaller teams may not need the full platform
A company protecting one chatbot may prefer a focused guardrail API. Prompt Security becomes more compelling as AI spreads across departments, applications, developers, and agents.
❌ Independent review coverage is still developing
Public ratings are positive, but detailed verified review volume remains limited. A real-world pilot matters more than aggregate scores.
Setup and Daily Use
Deployment and Administration
Prompt Security’s deployment experience depends on the surface you are protecting. Employee usage usually begins with browser or endpoint coverage, while homegrown applications use a gateway, API, or reverse-proxy pattern. Agentic environments add MCP discovery and enforcement.
Browser Extension and Workforce Rollout
The employee module supports major browsers and can be distributed through standard enterprise browser or device-management systems. SAML and SSO support helps connect activity to organizational identities, users, and departments.
A staged rollout is preferable to an immediate company-wide block policy. Begin with monitoring, identify real usage patterns, classify tools, and then apply targeted coaching, redaction, or blocking. This approach gives security teams evidence for policy decisions and reduces the risk of disrupting legitimate AI-assisted work.
Application, API, and Gateway Integration
Homegrown applications can route model traffic through Prompt Security’s AI Gateway or call its protection API. The main implementation tasks are mapping applications, defining policies, handling blocked or modified responses, monitoring latency, and deciding what interaction data should be logged.
Test the platform with complete application flows rather than isolated prompts. RAG pipelines, function calls, uploaded documents, streaming responses, tool execution, and multi-turn conversations can create different security and performance behavior.
Administration and Security Operations
The central console is designed to show AI applications, users, prompts, policy events, risk findings, and agent activity. Administrators can use this information to investigate violations, tune controls, identify risky departments or tools, and create audit reports.
Prompt Security is most useful when events enter an established process. Define owners for policy violations, data-leak escalation, red-team remediation, and agent incidents so the console does not become another unmanaged alert queue.
Cost and Procurement
Prompt Security Pricing
Prompt Security uses custom enterprise pricing. The official product site directs buyers to request a demo rather than publishing standard per-user, per-application, or per-request rates.
The AWS Marketplace currently displays 12-month contract dimensions for employee security, code-assistant security, homegrown application security, and self-hosted versions. Each dimension is listed at $10,000, but the listing also instructs buyers to contact SentinelOne for custom pricing and private offers. This should be treated as a marketplace starting reference, not a reliable estimate of total enterprise cost.
| Pricing Factor | What to Confirm | Why It Matters |
| Employee coverage | Licensed users, browsers, and managed devices | Determines workforce governance cost |
| Application protection | Apps, gateways, traffic, or request volume | Affects runtime security pricing |
| Code assistants | Supported tools, developers, and environments | May require separate coverage |
| Agentic AI | MCP servers, agents, tools, and interactions | Can introduce new usage dimensions |
| Deployment model | SaaS, VPC, self-hosted, or on-premises | Changes infrastructure and support needs |
| Red teaming | Applications, test frequency, and reporting | May be packaged separately |
Request an itemized quote that separates platform modules, implementation, support, data retention, professional services, and renewal terms. The most useful proof of concept should also measure false positives, redaction quality, employee friction, application latency, policy coverage, and analyst workload.
Security and Privacy
How Secure Is Prompt Security?
Prompt Security occupies a sensitive control point. Depending on deployment, it may inspect prompts, responses, uploaded content, source code, user identity, application activity, and agent-tool interactions. The product can reduce AI data exposure, but the platform itself must be included in your vendor risk and privacy review.
Data Handling and Deployment Control
Cloud, VPC, self-hosted, and on-premises options give buyers flexibility over where enforcement runs. This is valuable for regulated workloads or organizations that cannot route sensitive AI interactions through a shared SaaS service.
Deployment choice does not answer every data question. Confirm whether the platform stores complete prompts or only event metadata, how redacted and blocked content is handled, which regions process data, how long logs remain available, and whether administrators can disable content capture while retaining policy evidence.
Security Review Questions Before Deployment
- Which prompt, response, file, code, and identity fields are collected?
- Can data inspection and retention differ by department or application?
- How are encryption, key management, tenant isolation, and backups handled?
- Which administrators can read content, change policy, or export logs?
- What audit reports, penetration tests, and subprocessors are available?
- How are browser-extension and gateway updates reviewed and delivered?
- What happens to data after contract termination or a user deletion request?
What the SentinelOne Acquisition Changes
SentinelOne announced its acquisition of Prompt Security to extend visibility and control across employee AI usage, applications, and agents. Prompt Security is now positioned inside the Singularity Platform alongside endpoint, cloud, identity, data, and security operations capabilities.
For SentinelOne customers, this can simplify procurement and correlate AI events with wider security telemetry. Other buyers should confirm which capabilities remain standalone and which advantages require Singularity.
Buyers should still review roadmap changes, packaging, migration requirements, support ownership, and contract transitions.
Who It’s Best For
Where Prompt Security Adds Value
Prompt Security is best suited to organizations that want to enable AI rather than prohibit it, but still require centralized visibility, data protection, and enforceable policy.
| Organization Type | Fit | Why |
| Enterprises with widespread employee AI use | Excellent | Strong shadow AI discovery, policy, redaction, and coaching |
| Companies building several AI applications | Excellent | Gateway protection, logging, and automated red teaming |
| Regulated finance or healthcare teams | Strong | Flexible deployment and granular data controls |
| Developer-heavy organizations | Strong | Code-assistant governance across multiple tools |
| Companies adopting agents and MCP | Strong | Discovery, risk scoring, policy enforcement, and audit logs |
| Small teams protecting one chatbot | Limited | A focused API or open-source tool may be simpler |
The platform is particularly relevant when your AI program spans several business units or technical architectures. It gives security teams a common control plane while allowing different policies for employee experimentation, sensitive departments, production applications, developers, and autonomous agents.
It is less compelling when AI adoption is minimal, when the organization cannot manage browser enrollment, or when the primary requirement is model supply-chain scanning and AI infrastructure posture rather than prompt, usage, and runtime governance.
Compare Other AI Security Platforms
Prompt Security Alternatives
The best Prompt Security alternative depends on whether your priority is unified AI guardrails, deep AI lifecycle security, browser-native identity defense, or SaaS and shadow application governance.
Quilr
Quilr combines AI guardrails with broader data protection, employee coaching, agent controls, and AI security posture management. It may be the stronger choice when you want one platform to connect AI security with email, SaaS, file, and human-risk controls. Prompt Security has a clearer dedicated architecture across employees, code assistants, homegrown applications, red teaming, and MCP gateways. Read our full Quilr review for a detailed comparison.
Cisco AI Defense
Cisco AI Defense provides broad AI lifecycle coverage, including AI asset discovery, model and application validation, supply-chain analysis, runtime guardrails, and agent security. It is a strong alternative for large Cisco customers or organizations prioritizing AI inventories and development-pipeline controls. Prompt Security is more directly centered on employee interactions, browser enforcement, code-assistant governance, and cross-surface prompt policies. See our Cisco AI Defense review.
Push Security
Push Security is a browser-native security platform that combines phishing defense, identity posture, shadow SaaS discovery, AI governance, extension controls, and browser data protection. It is better suited when browser attacks and SaaS identities are the main problem. Prompt Security is the stronger fit when you also need homegrown AI application security, automated red teaming, code-assistant coverage, and MCP governance. Read the complete Push Security review.
Grip Security
Grip Security focuses on shadow SaaS, SaaS identity risk, application discovery, access governance, and posture management. It may be preferable when unmanaged SaaS accounts and identity exposure are the primary concern, with AI as one part of a wider SaaS program. Prompt Security is more specialized around AI interactions, runtime application protection, red teaming, and agents. Read our Grip Security review for more details.
Conclusion
Is Prompt Security Worth It?
Prompt Security is worth evaluating if AI has become a real enterprise workflow rather than a limited experiment. Its strongest advantage is the ability to protect several AI touchpoints through one platform: employee prompts, developer assistants, custom applications, red-team testing, autonomous agents, and MCP tools.
Its combination of visibility and real-time enforcement is more useful than reporting shadow AI after the fact. Redaction, coaching, runtime guardrails, and agent policies support proportionate controls without requiring a blanket ban.
The tradeoffs are custom pricing, policy complexity, sensitive telemetry, and disciplined implementation. The platform will not define your acceptable-use policy or replace secure application architecture, so clear ownership and policy tuning remain essential.
Run a pilot across one employee group, one development team, and one production AI application. Measure shadow AI discovery, prevented exposure, false positives, user friction, latency, red-team findings, and incident-response effort before committing broadly.
Frequently Asked Questions
Have more questions?
What does Prompt Security do?
Prompt Security discovers and governs enterprise AI usage. It protects employee prompts, code assistants, homegrown AI applications, and autonomous agents through data redaction, policy enforcement, runtime guardrails, red teaming, and MCP security controls.
Is Prompt Security owned by SentinelOne?
Yes. SentinelOne announced its acquisition of Prompt Security, and the product is now positioned within the SentinelOne Singularity Platform while retaining its dedicated AI security capabilities.
Can Prompt Security detect shadow AI?
Yes. Prompt Security can discover approved and unsanctioned AI tools used by employees, connect usage to users and departments, assess risk, and apply granular policies such as coaching, redaction, blocking, or allowing.
Does Prompt Security prevent sensitive data leaks?
Prompt Security can detect and redact, anonymize, tokenize, warn, or block sensitive information before it reaches an AI service. Buyers should test classification accuracy against their own data and policy requirements.
Can Prompt Security protect custom AI applications?
Yes. Its AI Gateway and protection API inspect prompts and responses for risks such as prompt injection, jailbreaks, sensitive data exposure, harmful content, prompt leakage, and denial-of-wallet behavior.
Does Prompt Security support AI coding assistants?
Yes. Prompt Security supports AI coding assistants and can prevent secrets, PII, intellectual property, and proprietary code from being exposed while giving security teams visibility into developer AI usage.
What is Prompt Security’s MCP Gateway?
The MCP Gateway monitors and controls interactions between AI agents and Model Context Protocol servers. It supports discovery, risk scoring, allow or block policies, data filtering, action-level enforcement, and searchable audit logs.
How much does Prompt Security cost?
Prompt Security uses custom enterprise pricing. AWS Marketplace shows $10,000 annual contract dimensions for several modules, but it also directs buyers to contact SentinelOne for custom pricing and private offers. Request an itemized quote for your exact scope.
Can Prompt Security be self-hosted?
Yes. Prompt Security advertises cloud, VPC, self-hosted, and on-premises deployment options. Confirm which modules support each model and how data processing, updates, and support differ.
What are the best Prompt Security alternatives?
Strong alternatives include Quilr for unified data and AI guardrails, Cisco AI Defense for broad lifecycle and supply-chain security, Push Security for browser and identity defense, and Grip Security for SaaS discovery and identity governance.
