Keywords AI

HiddenLayer vs Pangea

Compare HiddenLayer and Pangea side by side. Both are tools in the AI Security category.

Quick Comparison

HiddenLayer
HiddenLayer
Pangea
Pangea
CategoryAI SecurityAI Security
Websitehiddenlayer.compangea.cloud

How to Choose a AI Security Tool

Key criteria to evaluate when comparing AI Security solutions:

Threat coverageProtection against prompt injection, jailbreaks, data leakage, and other LLM-specific attacks.
Latency impactProcessing overhead added to each request — critical for real-time applications.
CustomizationAbility to define custom security policies and content rules for your domain.
Compliance supportBuilt-in PII detection, data residency controls, and audit logging for regulatory requirements.

About HiddenLayer

HiddenLayer provides AI security solutions that protect machine learning models from adversarial attacks, model evasion, and tampering. Its platform detects and prevents attacks targeting AI systems in real-time, offering model integrity verification and threat intelligence specifically designed for AI/ML workloads.

About Pangea

Pangea is the "Twilio for Security" — a set of composable security APIs that developers embed directly into AI applications. It provides audit logging, data redaction, embargo compliance, IP reputation, and domain intelligence as simple API calls. For AI apps, Pangea's Redact API strips PII from prompts, its AI Guard detects prompt injections, and its Audit API creates tamper-proof logs of all AI interactions.

What is AI Security?

Platforms focused on securing AI systems—prompt injection defense, content moderation, PII detection, guardrails, and compliance for LLM applications.

Browse all AI Security tools →

Frequently Asked Questions

What are the main security risks with LLM applications?

The primary risks are prompt injection, data leakage, jailbreaking, and hallucination. Each requires different mitigation strategies.

Do I need a dedicated AI security tool?

If your LLM application handles sensitive data or is user-facing, yes. Basic input validation is not enough — LLM attacks are sophisticated and evolving. Dedicated tools stay updated against new attack vectors and provide defense-in-depth.

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