An open, community-edited body of guidance covering security and privacy concerns across all types of AI systems. It serves as a shared reference for threat models, controls, and best practices, and is intended to support collaboration across industry, academia, and standards bodies rather than a single vendor approach.
Expanded Definition
AI Exchange refers to an open, community-edited reference point for security and privacy guidance across AI systems. In practice, that means it is used as a shared vocabulary for identifying risks, describing controls, and comparing defensive approaches across model development, deployment, and operation. Unlike a vendor framework tied to a single product stack, AI Exchange is meant to support cross-organisational discussion and evolving consensus.
Its value is partly in translation. Security teams use it to connect AI-specific concerns such as prompt injection, data leakage, model misuse, and agentic tool abuse with more familiar governance patterns from broader cybersecurity. That makes it useful when organisations need a lightweight but credible way to discuss controls before formal policy or regulation catches up. For governance context, teams often map the same issue back to NIST Cybersecurity Framework 2.0 to anchor it in established risk management language.
Definitions vary across contributors because AI Exchange is community-maintained rather than codified by a single standards body, so terms and emphasis can shift as new threat patterns emerge. The most common misapplication is treating AI Exchange as a compliance standard, which occurs when teams assume community guidance alone is sufficient to prove control effectiveness or regulatory alignment.
Examples and Use Cases
Implementing AI Exchange guidance rigorously often introduces interpretation overhead, requiring organisations to balance flexibility for emerging AI risks against the consistency needed for repeatable governance.
- A security architect uses AI Exchange guidance to assess whether an LLM application has adequate safeguards against prompt injection and unintended data disclosure.
- A platform team adopts its terminology to document controls around training data access, model versioning, and review workflows for NIST Cybersecurity Framework 2.0-style governance mapping.
- An organisation building AI agents uses the reference to discuss where tool permissions should be restricted, logged, and reviewed before production rollout.
- A privacy team references it when comparing how different AI use cases handle personal data, retention, and disclosure risks across jurisdictions.
- A risk committee uses it to normalise language between engineering, legal, and security teams when no single internal AI policy is mature enough to cover every use case.
Because the body is community-edited, its practical use is strongest when paired with internal control ownership and clear decision records. It works best as a bridge between emerging AI practice and established security governance rather than as a stand-alone rulebook.
Why It Matters for Security Teams
AI Exchange matters because AI security is still a moving target, and security teams need a defensible way to talk about risks before controls are standardised. For organisations adopting AI systems, especially those exposing models through APIs or agentic workflows, a shared reference reduces ambiguity around what should be reviewed, logged, restricted, or monitored. That is especially important when teams are trying to align AI governance with broader frameworks such as the NIST Cybersecurity Framework 2.0.
The identity connection becomes relevant when AI systems consume secrets, call internal services, or act on behalf of users and services. In those cases, poor terminology can lead to weak assumptions about trust boundaries, access delegation, and approval workflows. AI Exchange is useful precisely because it helps teams express those concerns early, before they become incident response questions.
Organisations typically encounter the consequences only after an AI feature exposes data, performs an unauthorised action, or bypasses an expected approval step, at which point AI Exchange-style guidance becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF, NIST AI 600-1, NIST CSF 2.0 and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | AI RMF provides the governance context for managing AI risks that AI Exchange helps describe. | |
| NIST AI 600-1 | NIST AI 600-1 profiles GenAI risks that overlap with the guidance themes in AI Exchange. | |
| NIST CSF 2.0 | GV.RM-03 | CSF governance and risk management align with using shared AI guidance for accountability. |
| NIST SP 800-63 | Digital identity assurance is relevant when AI systems act for users or access protected services. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance complements AI Exchange where autonomous tool use and action risks are involved. |
Apply identity assurance checks before allowing AI-driven actions that depend on user or service identity.
Related resources from NHI Mgmt Group
- What is the difference between OAuth and token exchange for AI agent access?
- How do AI agent delegation flows differ from standard token exchange?
- How do token exchange and federation help with least privilege in AI toolchains?
- How should security teams handle authentication and authorization for AI and application integrations without adding unnecessary token exchange complexity?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 28, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
