PumaMesh helps AI teams move models, weights, training data, retrieval data, and results without splitting delivery, security, lineage, and audit across separate tools.
It brings the same simple PumaMesh story to AI: protect important data, understand what it is, move it where it is allowed to go, and accelerate access when speed matters.
AI work often starts on Windows and runs on Linux. PumaMesh supports both so data stays governed from export through training, delivery, and reuse.
A training pipeline is machine to machine. A RAG response is machine to person. Research collaboration is person to person. PumaMesh runs all three with the same policy and evidence model.
Model delivery, training-set replication, fine-tune pushes, inference-cache sync, agent-to-agent tool-calls — all policy-gated and post-quantum-encrypted on every hop.
RAG responses, model outputs, findings, and reports reach humans with the classification and provenance of the underlying records intact. ABAC decides who can see what before the response renders.
Partner exchange, cross-institution research, and legal/IP sharing move between people and organizations with the object carrying its own rules — the sender's permission doesn't override the recipient's restrictions.
Classical DSPM stops at the bucket. AI broke that assumption. PumaMesh enforces policy at the points that actually matter for AI — training, retrieval, tool-calls, and fine-tunes — and produces the evidence regulators now require.
Training Boundary
Restricted records can't enter a fine-tune that leaves the sovereignty zoneABAC on classification, jurisdiction, and customer ontology gates which records can enter which training artifact — before bytes leave the source.
Retrieval Boundary
PHI doesn't cross into non-US-hosted model contextRAG retrievals evaluate ABAC per-record before embedding or inference. Fresh within 300 seconds, fail-closed on revocation.
Tool-Call Boundary
Agents can only invoke tools their attributes allowThe same ABAC surface that gates file movement gates what an AI agent can read, write, or act on — one policy language, one enforcement point.
Fine-Tune Provenance
See which sensitive records entered which modelTraining set posture, fine-tune lineage, and model-to-source mapping — the governance layer MLOps platforms still don't own.
Every transfer produces audit evidence aligned to the standards governing AI today — not just the ones that governed file transfer a decade ago.
Automatic activity logs with operator, classification, policy, and model-lineage context — exportable for the record-keeping obligations governing high-risk AI systems.
Measure and Manage functions (MS-1 to MS-4, MG-1 to MG-4) supported with training-set posture, fine-tune provenance, and retrieval lineage reporting.
AI management system evidence — data governance, lifecycle controls, and model-to-source traceability in the same audit chain that produces CMMC and FedRAMP evidence.
All 110 CMMC controls met for data sharing. NIST SP 800-171 Rev 2 and Rev 3 anchor requirements satisfied by the product. AI training data inherits the same CUI control surface as any other regulated record.
80+ NIST SP 800-53 Rev 5 controls mapped with direct code evidence. AI pipeline boundaries inherit the same enforcement and audit.
AI data surface inventory, sensitive-data flow map, and training provenance — the artifacts underwriters and internal MRM teams now ask for on every renewal.
