The gap between code that appears valid in review and code whose origin, prompts, model inputs, and generation path are not fully understood. It matters because modern delivery pipelines may accept artefacts without enough provenance to explain how sensitive logic was created.
Expanded Definition
The AI Delivery Trust Gap describes a trust breakdown in software and AI delivery where an artefact looks acceptable during review, but the evidence needed to explain its origin is incomplete. That can include missing prompt history, unclear model inputs, weak provenance for generated code, or an opaque path from draft to deployment. In security terms, the issue is less about whether the output is syntactically correct and more about whether the delivery chain can prove how and why it was produced.
Definitions in the industry are still evolving, especially where AI-assisted coding blends with DevOps, MLOps, and secure software supply chain governance. NHI Management Group treats the term as a provenance and assurance problem: teams need to know which human, model, tool, or agent touched the artefact, and what controls validated each step. That aligns with the governance emphasis reflected in the NIST Cybersecurity Framework 2.0, even though the framework does not name the term directly.
The most common misapplication is treating a successful code review as proof of trustworthy origin, which occurs when reviewers assess output quality but not the generation path or underlying context.
Examples and Use Cases
Implementing AI-assisted delivery rigorously often introduces traceability overhead, requiring organisations to weigh faster development against the cost of stronger provenance, approval, and logging controls.
- A developer accepts AI-generated infrastructure code after a visual review, but later cannot show which prompt or model version produced the changes.
- An agentic workflow opens a merge request with valid syntax, yet the repository lacks signed provenance for the generated files or the toolchain steps that created them.
- A security team approves a policy update because it matches the intended control, but cannot verify whether sensitive configuration was influenced by an unreviewed prompt or retrieval source.
- During incident response, investigators find that build artefacts were produced by multiple AI tools, but no single record ties those outputs to the final release candidate.
- Teams using AI coding assistants align their controls with the supply chain guidance in NIST Cybersecurity Framework 2.0, then add internal attestations for prompts, approvals, and source lineage.
These use cases show that the trust gap is not limited to software quality. It appears wherever generated material enters a release process without enough context to justify confidence in its provenance, especially when human reviewers assume the artefact is fully understood because it passed testing or linting.
Why It Matters for Security Teams
The AI Delivery Trust Gap matters because it weakens accountability across the delivery chain. If teams cannot explain how an artefact was generated, they also cannot reliably determine whether it contains unsafe logic, leaked secrets, policy bypasses, or unapproved dependencies. That creates blind spots in secure development, change management, and incident analysis. It also makes it harder to enforce separation of duties when AI systems or agents participate in code creation, refactoring, or deployment support.
For security teams, the practical concern is not only trust in the output, but trust in the evidence. Logs, provenance records, model IDs, prompt capture, and approval trails become part of the control set. Where agentic AI is involved, the same discipline helps distinguish an accountable workflow from an autonomous action path that no one can reconstruct after the fact. The NIST Cybersecurity Framework 2.0 is useful here because it reinforces governance, risk management, and traceability expectations around delivery integrity.
Organisations typically encounter the operational cost of this gap only after a questionable release, at which point AI delivery trust 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 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST AI 600-1 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-01 | Governance and risk management address delivery trust and accountability for artefacts. |
| NIST AI RMF | AI RMF focuses on trustworthy AI processes, including transparency and accountability. | |
| NIST AI 600-1 | The GenAI profile highlights governance and traceability concerns for generative AI use. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance covers unsafe autonomy and weak visibility into tool-using workflows. | |
| OWASP Non-Human Identity Top 10 | NHI guidance is relevant where AI tooling, tokens, and service identities create provenance gaps. |
Define provenance controls and risk ownership for AI-generated delivery outputs before release.
