OpenSharing

Expanded Definition

OpenSharing refers to a protocol-driven way of exchanging AI assets across organisations, platforms, and clouds without requiring a single vendor stack. For NHI Management Group, the defining issue is not the transport alone, but whether the receiving environment can verify what is being shared, who is allowed to consume it, and under what policy. In practice, the term may cover models, prompts, datasets, tools, agents, and other artefacts that move between governance boundaries. The security question is therefore closer to trust establishment than file transfer.

Definitions vary across vendors and ecosystem groups, because no single standard fully governs OpenSharing yet. Some implementations emphasise interoperability first, while others treat it as a controlled exchange pattern with mandatory provenance metadata and access enforcement. That makes it adjacent to data sharing, API federation, and workload portability, but not identical to any of them. The most useful way to read the term is as a security-relevant interoperability layer that must be wrapped in explicit identity, authorization, and integrity checks, consistent with the risk-based direction of the NIST Cybersecurity Framework 2.0.

The most common misapplication is treating OpenSharing as inherently safe simply because the protocol is open, which occurs when organisations assume interoperability also guarantees provenance, policy inheritance, or least-privilege access.

Examples and Use Cases

Implementing OpenSharing rigorously often introduces policy and provenance overhead, requiring organisations to weigh faster cross-platform collaboration against tighter controls at the receiving boundary.

• A research team shares a fine-tuned model with a partner organisation, but only after embedding source metadata, versioning, and usage restrictions so the receiver can validate lineage.
• A cloud platform ingests AI agents from an external marketplace, with each agent constrained by scoped credentials, sandboxed execution, and approval gates before tool access is granted.
• A data science group exchanges prompt libraries and retrieval bundles across business units, while the destination environment checks ownership, classification, and expiry before activation.
• A regulated enterprise republishes internal AI assets to a subsidiary through a common interface, using policy enforcement at the boundary instead of relying on identical infrastructure.
• An engineering team uses OpenSharing to move artefacts between clouds, but maps every shared asset to an internal identity record so access can be revoked when the source relationship changes.

These patterns align with the broader governance approach in the NIST Cybersecurity Framework 2.0, where asset management, protective controls, and monitoring must travel with the asset rather than be assumed by the transport method.

Why It Matters for Security Teams

OpenSharing changes the security perimeter. Once AI assets can move freely across organisations, the main risk is no longer only external compromise, but uncontrolled reuse, hidden dependencies, and broken trust at the point of import. Security teams need to understand that shared AI assets can carry inherited permissions, embedded secrets, unsafe tool calls, or opaque provenance trails that are invisible until something fails. That is especially important for agentic AI, where an imported agent may arrive with execution authority and tool access that exceeds what the receiving organisation intended.

For identity and access teams, OpenSharing also creates an NHI governance problem. Shared models, agents, pipelines, and service integrations often authenticate non-human workloads rather than people, so access control, rotation, and revocation must be precise and auditable. Without that discipline, interoperability becomes a route for privilege drift. Organisations typically encounter the consequences only after a shared asset behaves unexpectedly, at which point OpenSharing becomes operationally unavoidable to secure.