Aiceberg Risk Signals Library vs Impart LLM Security: Side-by-Side Comparison (2026)

Aiceberg Risk Signals Library

Mid-market and enterprise security teams deploying generative AI applications need Aiceberg Risk Signals Library to catch prompt injection and data exfiltration before they happen, which most traditional DLP tools completely miss. The library's dual focus on input validation (prompt injection detection) and output controls (prompt leaking prevention) covers the attack surface unique to LLM applications, addressing gaps in PR.DS and DE.CM that legacy platforms ignore. Skip this if your GenAI use is experimental or limited to public ChatGPT; the pricing and operational overhead make sense only when AI models are handling sensitive data at scale.

Impart LLM Security

Teams deploying internal LLM applications without visibility into what models are running or how they're being used should start with Impart LLM Security; automatic model discovery and prompt injection detection address the asymmetry where developers move faster than security can audit. The platform maps to NIST ID.AM and DE.CM strongly, meaning you get asset visibility and continuous monitoring without needing to retrofit logging into every LLM integration. Skip this if your organization hasn't shipped LLM features to production yet or if you need data loss prevention tools that also handle traditional SaaS applications; Impart's focus is narrow by design.