Google Vertex AI identity governance: why invocation rights matter

At a glance

What this is: This analysis frames Google Vertex AI as an identity governance problem, highlighting how invocation rights, tuning permissions, and shared service accounts can bypass enterprise controls.

Why it matters: It matters because IAM, PAM, and AI governance teams need to treat model access as privileged access with traceable ownership, not as ordinary cloud operations.

👉 Read P0 Security's analysis of Google Vertex AI identity governance and invocation rights

Context

Google Vertex AI exposes a governance gap that many programmes still miss: the ability to invoke, tune, deploy, and share models changes the identity surface, not just the cloud footprint. In practice, the security question is who is allowed to trigger model behaviour, with what permissions, and under which attribution model.

That becomes harder in multi-team, multi-project estates where default roles, shared service accounts, and federated workloads can bypass the intended separation between development, production, and shared model registries. For organisations scaling generative AI, the control problem is identity governance across the model lifecycle, not platform administration alone.

Key questions

Q: How should security teams govern Vertex AI invocation rights?

A: Treat invocation rights as privileged access, not routine application usage. Grant only the endpoint, project, and workload permissions needed for a specific business function, and review them separately from broader cloud entitlements. If a principal can call production models by default, the governance model is already too loose.

Q: Why do shared service accounts create risk for AI agents?

A: Shared service accounts hide which agent made a request, so they collapse accountability and make incident response slower. They also turn credential exposure into a single compromise path for multiple workloads. For agentic systems, every identity should be attributable to a specific workload or delegated task.

Q: What breaks when model training and production invocation use the same identity?

A: Separation of duties breaks down, and a single principal can move from experimentation into operational control without a meaningful governance checkpoint. That creates hidden model drift risk, weakens traceability, and makes it harder to prove that production behaviour came from approved training activity.

Q: How can teams tell whether Vertex AI identity governance is working?

A: Look for clear attribution, distinct lifecycle identities, and narrow rights on model actions such as prediction, training, and deployment. If audit logs still rely on shared principals or if sandbox access can reach production models, the control design is not working as intended.

Technical breakdown

Invocation rights as privileged access

Vertex AI endpoint permissions such as prediction and explain actions turn model usage into a privileged operation. When broad roles grant those permissions by default, any user or workload with the role can run inference on production endpoints, potentially exposing sensitive inputs, generating uncontrolled spend, or abusing internal ML services. The underlying issue is that model invocation is not a low-risk read operation; it is an action that can trigger business, cost, and data consequences in real time.

Practical implication: Treat model invocation permissions as privileged access and scope them to the minimum endpoint, project, and workload context required.

Model tuning and deployment create governance drift

Fine-tuning pipelines, model uploads, and registry operations move governance from consumption into lifecycle control. Once a team can create training pipelines or register new model versions, it can change model behaviour, introduce unvetted training data, and push artefacts into shared registries that other teams may trust. That is where identity and supply chain governance intersect: the identity that trains a model is not always the identity that should publish or invoke it.

Practical implication: Separate training, registration, and production invocation into distinct identities with different approval paths and audit expectations.

Cross-project access makes Vertex a shared identity boundary

Vertex is tightly coupled to Google Cloud Resource Manager, so permissions can propagate across projects and regions unless boundaries are enforced deliberately. Shared service accounts and inconsistent workload identity mappings can make a sandbox endpoint callable from production or allow regional use that conflicts with residency policy. This is less a platform quirk than an identity boundary problem: shared AI services need explicit trust zoning or they behave like an ungoverned federation layer.

Practical implication: Constrain cross-project access, region use, and shared-service trust with organisation policy and project-level boundaries.

Threat narrative

Attacker objective: The attacker or insider wants to use legitimate Vertex permissions to access data, alter model behaviour, or extend AI access beyond the intended governance boundary.

1. Entry occurs when a broadly assigned Vertex role, shared service account, or inconsistent federation mapping gives a workload or user access to invoke or tune models.
2. Credential or permission abuse follows when that identity uses endpoint predict rights, training pipeline creation rights, or model upload rights beyond the intended scope.
3. Escalation happens as the same identity expands from simple inference into model modification, shared registry publication, or cross-project access paths.
4. Impact is unauthorised model use, data exposure through inference inputs, untrusted model behaviour, and governance bypass across production AI workloads.

Breaches seen in the wild

• Nx s1ngularity attack 2025: Attackers stole Nx's npm token via a GitHub Actions flaw and shipped malware that stole 2,349 secrets and abused developers' AI CLIs.
• LiteLLM PyPI package breach: LiteLLM PyPI supply chain attack, credentials stolen from users.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Invocation rights are a privileged access problem, not a cloud convenience feature. Vertex AI endpoint calls can expose sensitive inputs, drive material cost, and trigger downstream business actions, so they belong in the same governance class as other privileged operations. Broad roles that permit model invocation collapse the distinction between operational access and decision authority. The practitioner conclusion is simple: model calls need explicit privilege design, not inherited convenience.

Model lifecycle control is where identity governance and AI governance converge. The same environment that lets teams tune, register, and deploy models also creates an opening for unvetted data, shared trust, and unclear ownership. This is a lifecycle problem, not just an access problem, because the identity that creates the model artefact can shape the trust of every downstream consumer. Practitioners should govern training, registration, and consumption as separate control points.

Multi-project Vertex estates turn shared service accounts into hidden trust channels. A sandbox project that can be invoked from production is not isolated in any meaningful governance sense, even if the technical boundary remains intact. When service accounts are reused across projects, the identity layer becomes the real federation boundary and the weakest policy wins. The implication is that cross-project trust must be designed explicitly, not inferred from cloud structure.

Identity provenance is now a core AI control objective. Cloud Audit Logs are only useful when the request can be tied back to a verified human or workload identity with stable attribution. Long-lived service accounts, shared API keys, and inconsistent federation mappings turn auditability into guesswork and weaken accountability across the model lifecycle. The named concept here is vertex identity provenance: the requirement to preserve a clear, verifiable chain from enterprise identity to each model action. Practitioners should treat provenance as a control requirement, not a reporting nicety.

Least privilege for AI must be evaluated at the action level, not the platform level. A role that looks acceptable for a development notebook can be excessive when it also permits production inference or model publication. That mismatch creates hidden privilege creep across AI programmes because the same principal can cross from experimentation into operational control. The practitioner conclusion is to review the exact model actions each identity can perform, not just the platform it can reach.

From our research library:

• 92% of organisations expose NHIs to third parties, raising concerns about supply chain security, according to the Ultimate Guide to NHIs.
• Read next: AI Agent Identity Security Buyer's Guide

What this signals

Vertex identity provenance: AI governance programmes need a verifiable chain from enterprise identity to each model action, because Cloud Audit Logs are only useful when the actor is distinguishable and accountable. Shared credentials and inconsistent federation turn model oversight into forensic reconstruction instead of live governance.

Vertex AI adoption will push IAM teams to review not only who can access a platform, but who can trigger model behaviour, change model artefacts, and cross trust boundaries between projects. That means identity review, approval design, and workload federation need to move into AI operating models, not sit beside them as separate controls.

According to the Ultimate Guide to NHIs, 92% of organisations expose NHIs to third parties, raising concerns about supply chain security. The same exposure pattern becomes more consequential when third-party models, pipelines, and service accounts can influence production AI behaviour.

For practitioners

• Define model invocation as privileged access Catalogue every Vertex permission that can invoke, explain, tune, or publish models, then map it to an owner and an approval path. Remove those rights from default editor-style roles.
• Split lifecycle identities by duty Use distinct identities for training, registration, deployment, and production inference so no single principal can move a model from experiment to shared trust without review.
• Replace shared service accounts with traced workload identity Normalise workloads through federation so each Vertex action is tied to a verified enterprise identity rather than a reusable shared credential.
• Constrain cross-project and cross-region trust Apply organisation policies and project boundaries to stop sandbox endpoints, shared registries, or foreign regions from becoming unplanned access paths.

Key takeaways

• Vertex AI exposes an identity governance problem because model invocation, tuning, and deployment can all be exercised through privileged permissions.
• Auditability depends on preserving clear provenance for every model action, especially when service accounts and federated workloads are involved.
• The control gap is not just excess access, but the absence of separated lifecycle identities and explicit trust boundaries across AI projects.

Key terms

• Vertex identity provenance: The verifiable chain that ties a model action back to a specific enterprise identity, workload, or approval context. In AI platforms, provenance is what makes logs actionable and governance defensible instead of merely descriptive.
• Invocation rights: Permissions that allow an identity to call a deployed model, run inference, or trigger related AI actions. These rights are privileged because they can expose sensitive inputs, generate cost, or influence downstream business decisions.
• Model lifecycle governance: Model lifecycle governance is the control of ownership, versioning, data lineage, approval, and retirement for AI systems. It gives security and compliance teams the evidence needed to explain what changed, who is responsible, and whether a model is operating within its intended boundary.
• Generic Service Account: A generic service account is a shared non-human identity used by applications, scripts, or automated jobs in cloud environments. It is created for convenience, but it often lacks clear human ownership and individual accountability. If poorly governed, it can retain broad access and become difficult to audit or contain.

What's in the full article

P0 Security's full article covers the operational detail this post intentionally leaves for the source:

• Permission-by-permission mapping of Vertex AI actions to governance risk
• Practical examples of separating training, registration, and production inference identities
• Recommended GCP boundary controls for cross-project and cross-region model use
• How to map federated and workload identities back to a clear audit trail

👉 The full P0 Security article expands on Vertex AI privilege boundaries, attribution challenges, and lifecycle controls.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.