Enkrypt AI Data Risk Audit vs TrustLogix TrustAI: 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.

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.