Enkrypt AI Data Risk Audit vs Immuta Data Security for AI: Side-by-Side Comparison (2026)

Enkrypt AI Data Risk Audit

Security teams building or fine-tuning AI models in-house need Enkrypt AI Data Risk Audit to find what's actually in their training datasets before it becomes a breach or compliance violation. The tool generates a Data Bill of Materials for AI datasets and detects PII, PHI, and PCI exposure across multimodal data, then gates releases until risks are remediated, which maps directly to ID.AM and PR.DS in NIST CSF 2.0. Skip this if your AI workloads are purely inference-based or entirely vendor-managed; the value hinges on owning the training pipeline.

Immuta Data Security for AI

Teams building RAG systems and fine-tuning LLMs need Immuta Data Security for AI because it enforces access control at the row level before training data ever reaches your model, not after. The platform covers four NIST CSF 2.0 functions including continuous monitoring of data queries in real time, which catches when someone tries to extract a customer dataset disguised as a model validation set. Skip this if your data lives entirely in closed-loop, air-gapped infrastructure or if you're not yet ingesting external sources into your AI pipelines; the complexity overhead won't justify itself.