Best Prompt Security Alternatives ranked by AI · updated Aug 2026

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Prompt Security provides security controls for generative AI use in organizations, including protection against prompt injection, data leakage, and unsafe employee or application usage. It is designed for security teams governing both sanctioned and unsanctioned AI interactions.

Developer: Prompt Security Price: Custom pricing 🎯 prompt.security

Top 6 Prompt Security alternatives

1 fenced.ai logo

fenced.ai

Fenced

Fenced is an AI security platform designed to protect enterprise applications using large language models. It helps detect prompt injection, sensitive-data leakage,...

Pros

  • Focused specifically on securing AI applications rather than general application security
  • Targets prompt injection and data leakage risks in model interactions
  • Designed for enterprise deployment and monitoring

Cons

  • Less established publicly than Lakera or HiddenLayer
  • Pricing and deployment details are not publicly clear
  • May require security engineering expertise to configure effectively
2 Protective.ai logo

Protective.ai

Protective AI

Protective AI is an AI security platform designed to help organizations monitor and protect generative AI applications and models. It targets security,...

Pros

  • Designed specifically for securing generative AI applications
  • Addresses both security monitoring and policy enforcement
  • More focused on AI risks than traditional web application security tools

Cons

  • Public pricing and detailed product information are limited
  • May require enterprise security and engineering resources to deploy
  • Smaller ecosystem than established cybersecurity vendors
3 Robust Intelligence logo

Robust Intelligence

Robust Intelligence

Robust Intelligence provides automated security and reliability testing for machine-learning and generative AI systems. Its platform evaluates models for adversarial behavior, vulnerabilities,...

Pros

  • Strong automated testing for model robustness and adversarial threats
  • Useful for pre-deployment validation as well as production monitoring
  • More mature model assurance focus than general-purpose AI security tools

Cons

  • Less focused on broad AI usage discovery and governance
  • Can complement rather than replace a full AI security platform
  • Primarily aimed at larger organizations with dedicated ML teams
4 zerobloks logo

zerobloks

ZeroBloks

ZeroBloks is an enterprise AI security platform for discovering, monitoring, and protecting generative AI applications and usage. It helps security teams manage...

Pros

  • Combines AI asset discovery, monitoring, and protection in one platform
  • Targets enterprise security and governance teams
  • Addresses risks across prompts, models, data, and AI applications

Cons

  • Less established than vendors such as HiddenLayer and Lakera
  • Enterprise-focused rather than suited to individual developers
  • Public pricing and product details are limited

SEAL Privacy AI is a privacy tool that helps individuals and organizations use generative AI without exposing sensitive information. It detects and...

Pros

  • Adds a privacy layer between users and external AI services
  • Designed for anonymizing sensitive prompt content
  • More focused on AI data protection than general-purpose DLP tools

Cons

  • Less established than major enterprise AI-security vendors
  • Public information about integrations and deployment options is limited
  • May not provide the broad compliance and workflow controls found in enterprise platforms
6 NeuralTrust.ai logo

NeuralTrust.ai

NeuralTrust

NeuralTrust is an AI trust and safety platform for organizations deploying generative AI applications and agents. It provides model and application monitoring,...

Pros

  • Combines AI security, governance, monitoring, and testing in one platform
  • Supports controls for prompt injection, sensitive data, and unsafe model behavior
  • Designed for enterprise teams managing multiple AI applications

Cons

  • Pricing and feature limits are not publicly documented
  • More complex to deploy than a focused open-source guardrails library
  • May offer less ecosystem maturity than larger AI security vendors

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