Immuta Data Security for AI vs TrustLogix TrustAI: Side-by-Side Comparison (2026)

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.

TrustLogix TrustAI

Security teams protecting AI agent access to sensitive data will find TrustLogix TrustAI's policy-based entitlement engine worth the deployment effort; it's one of the few tools that actually prevents data exposure at runtime instead of just logging what went wrong. The MPC-based API enforces just-in-time access without materializing full datasets, and compliance alignment with HIPAA, PCI DSS, and GDOR means audit prep doesn't become a separate project. This is less relevant for organizations still in early-stage AI adoption or those needing broad infrastructure access controls; TrustLogix is purpose-built for the narrower problem of securing what AI agents can actually touch.