Anjuna Seaglass vs Duality Secure AI Collaboration Platform: Side-by-Side Comparison (2026)

Short answer

Enterprise and mid-market teams protecting sensitive workloads from insider threats and memory-based attacks will find Anjuna Seaglass valuable because it encrypts data in-use via hardware enclaves, not just at rest or in transit. The no-code wrapping approach means you can move existing containerized apps into Confidential Containers without rewriting application code, and multi-cloud deployment across AWS, Azure, and GCP reduces vendor lock-in. This is not for teams whose primary concern is preventing initial compromise or lateral movement; Seaglass assumes the container already runs and focuses on what happens inside it, leaving your CNAPP and network detection work largely unchanged. Enterprise security and ML teams that need to train models on sensitive external data without exposing it will find real value in Duality Secure AI Collaboration Platform; the platform's multi-party computation approach lets you collaborate on datasets you'd normally keep behind walls. The hybrid deployment model and support for traditional ML through generative AI workloads means you're not locked into one model type or cloud provider, and NIST coverage across data security, platform security, and asset management confirms the security controls are intentional rather than bolted-on. Skip this if your primary constraint is cost or if you need out-of-the-box integrations with legacy on-premises data warehouses; Duality's value proposition assumes you've already committed to moving compute toward your data rather than moving data toward your models.