Google Vertex AI identity governance: why invocation rights matter

At a glance

What this is: This is an analysis of identity governance risks in Google Vertex AI, with the central finding that model invocation and lifecycle actions behave like privileged access.

Why it matters: IAM and NHI teams need to treat Vertex AI permissions as high-impact entitlements because mis-scoped access can expose data, alter models, and weaken auditability.

👉 Read P0 Security's analysis of Google Vertex AI identity governance risks

Context

Google Vertex AI expands the NHI attack surface because model access is not just an application permission, it is a privileged action that can move data, change behaviour, and trigger downstream automation. In multi-team environments, the governance gap appears when broad IAM roles, reusable service accounts, or inconsistent federation let model access escape normal identity controls.

For organisations scaling generative AI, the control problem is familiar even when the platform is new: who can invoke, tune, deploy, and share models must be governed with the same discipline used for other non-human identities. That means tying Vertex activity back to a verified identity, constraining where it can execute, and preventing shared access paths from becoming hidden trust shortcuts.

Key questions

Q: How should security teams govern AI model invocation in enterprise environments?

A: Treat model invocation as privileged access, not routine application use. Scope permissions to specific endpoints, short time windows, and approved workloads, then verify that each call maps to a known identity. This reduces the chance that shared service accounts or broad roles can be used to expose data or drive uncontrolled inference at scale.

Q: Why do AI platforms create NHI governance problems for IAM teams?

A: AI platforms concentrate multiple high-risk actions into the same identity plane. A single workload may invoke models, launch training, publish versions, and move data across projects, which means entitlement mistakes can affect confidentiality, integrity, and compliance at once. IAM teams must therefore manage AI permissions as NHI privileges with lifecycle controls.

Q: What is the difference between least privilege and separation of duties for AI workloads?

A: Least privilege limits how much access one identity has, while separation of duties limits which identities can perform different steps in the model lifecycle. In practice, a team can still have least privilege and remain risky if the same account can train, register, and deploy a model. Both controls are needed for Vertex AI governance.

Q: Should organisations use just-in-time access for AI model operations?

A: Yes, when access is task-based and the operational workflow can tolerate short-lived credentials. Just-in-time access reduces standing privilege and narrows the impact of compromised credentials, but it does not replace role design. Teams still need clear ownership, strong identity provenance, and separate permissions for training, deployment, and invocation.

Technical breakdown

Runtime invocation rights as privileged NHI access

In Vertex AI, the ability to call an endpoint is not a low-risk application permission. Permissions such as aiplatform.endpoints.predict and aiplatform.endpoints.explain can be attached to human users, service accounts, or federated workloads, and broad predefined roles can spread that privilege widely. The technical issue is that inference requests may carry sensitive data while also consuming expensive or regulated model capacity. When invocation rights are not tightly scoped, the platform turns ordinary API access into an NHI privilege domain that needs explicit governance, not just network controls.

Practical implication: Treat model invocation permissions as privileged access and scope them to specific endpoints, workloads, and time windows.

Model lifecycle controls across tuning, registry, and deployment

Vertex AI separates model development, training pipelines, registry operations, and deployment, but those stages can still be controlled by the same identity if governance is weak. Permissions such as aiplatform.trainingPipelines.create and aiplatform.models.upload allow new training runs, model updates, and version publication into shared registries. That creates a supply-chain style risk inside the AI lifecycle: unvetted data, manipulated training inputs, or a compromised pipeline can change model behaviour before it reaches production. The key architectural point is that model integrity depends on identity separation at each stage of the lifecycle.

Practical implication: Split training, registration, and production invocation into distinct roles and review who can move models between them.

Identity provenance and auditability in federated AI workflows

Vertex deployments often rely on Cloud Run, GKE, CI/CD pipelines, or other federated workloads, so the identity seen in logs is not always the identity that made the decision. If service accounts are long-lived, API keys are shared, or Workload Identity Federation is inconsistent, attribution becomes weak and Cloud Audit Logs lose investigative value. The core problem is provenance: security teams need a trustworthy chain from enterprise identity to workload identity to model action. Without that chain, response teams cannot tell whether a request came from an approved workload, a misused credential, or a compromised automation path.

Practical implication: Normalize workload identity, enforce federation mappings, and require every model action to resolve to an accountable identity.

Threat narrative

Attacker objective: The attacker aims to use AI platform privileges to access sensitive data, alter model behaviour, or operate production inference at scale without detection.

1. Entry through over-privileged Vertex AI roles or reused service accounts that can invoke production endpoints without narrow scoping.
2. Escalation through training pipeline or model registry permissions that let an attacker modify model behaviour or register altered versions.
3. Impact through unauthorized inference, sensitive data exposure, or cost-amplifying batch prediction jobs that are hard to attribute.

Breaches seen in the wild

• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.
• DeepSeek breach — DeepSeek breach exposed 1M+ log lines and sensitive secret keys.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Vertex AI should be treated as an identity plane, not an ML convenience layer. The platform’s risk is not limited to model misuse, because invocation, tuning, deployment, and cross-project sharing all create governance decisions with security consequences. When those permissions sit inside broad cloud roles, IAM teams inherit an NHI problem disguised as platform administration. The practical conclusion is that AI governance must sit inside identity governance, not beside it.

The largest control failure in AI platforms is often entitlement sprawl, not model weakness. A user or workload that can invoke a model, create a training pipeline, and register a new version has enough privilege to affect outputs, data exposure, and compliance posture. That pattern is structurally similar to over-privileged service accounts in other systems. Practitioners should map AI lifecycle permissions to explicit business roles and remove any default access that is not tied to a task.

Identity provenance is the difference between usable audit logs and forensic noise. If an inference request cannot be tied back to a verified enterprise identity, audit data only describes activity, not accountability. That creates blind spots for incident response, legal review, and access certification. Security teams need a clear trust chain from IdP to workload to model action, or they will not be able to prove who did what.

Cross-project model sharing creates an identity blast radius that many organisations underestimate. A model endpoint in one project can become reachable from another if hierarchy, policy, and service account reuse are not tightly governed. That means sandbox decisions can leak into production and regional policy can be bypassed by careless access design. The practical conclusion is that AI platform boundaries must be enforced with the same discipline as other high-value identity boundaries.

Ephemeral access is the right direction for AI governance, but only if it is paired with lifecycle separation. Just-in-time credentials reduce standing exposure, yet they do not solve the problem of who is allowed to train, publish, and consume models. The field needs to stop treating AI access as a single entitlement category. Practitioners should design for task-scoped access across the full model lifecycle, not just at the point of invocation.

From our research:

• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
• That governance gap is why practitioners should also review Ultimate Guide to NHIs , Standards alongside model access policy design.

What this signals

Identity governance for AI platforms is becoming a core control domain, not a specialist add-on. As organisations expand model access beyond experimentation, they need entitlement models that can survive cross-project sharing, federated workloads, and production inference. The operational question is no longer whether AI should be governed, but whether existing IAM processes can express task-scoped control over model actions.

With 80% of organisations reporting AI agents acting beyond intended scope in our research, the control problem is already operational rather than hypothetical. Vertex-style environments expose the same weakness when broad roles and reused service accounts are allowed to carry model privilege across environments. Teams should expect audit, access review, and privilege design to become materially harder as AI adoption grows.

Identity blast radius: the total amount of damage a compromised AI-related identity can cause across models, data, and projects. That blast radius shrinks only when invocation, training, deployment, and federation are separated and tied to accountable owners. Practitioners should measure AI access by blast radius, not by convenience.

For practitioners

• Scope model invocation to specific endpoints Restrict aiplatform.endpoints.predict and related permissions to named workloads, approved projects, and short-lived access paths. Remove broad predefined roles from users who do not need routine inference access.
• Separate training, registry, and production roles Create distinct identities for model development, pipeline execution, model registration, and production consumption so one account cannot move a model end to end. Review role bindings for shared service accounts and inherited permissions.
• Normalize federated workload identity Use Workload Identity Federation and consistent IdP mapping so Cloud Run, GKE, and CI/CD jobs resolve to accountable identities. Verify that audit logs can trace every inference and lifecycle action to a known owner.
• Apply regional and project boundaries to model usage Constrain cross-project invocation and region selection with organizational policies and resource hierarchy design. Align these restrictions with data residency rules so model access does not bypass governance through shared infrastructure.
• Review standing access for dormant AI privileges Audit long-lived service accounts and cached permissions that can still invoke models or alter pipelines. Replace persistent access with just-in-time credentialing where the task duration is short and the blast radius needs to stay small.

Key takeaways

• Vertex AI governance is an identity problem because model access can expose data, change behaviour, and trigger privileged automation.
• Broad roles, shared service accounts, and weak provenance turn AI platforms into hard-to-audit NHI environments.
• Practitioners should separate lifecycle duties, use task-scoped access, and require every model action to map to a verified identity.

Key terms

• Non-Human Identity: A non-human identity is any credentialed machine actor that can authenticate and perform actions in a system. In AI platforms, this includes service accounts, API keys, tokens, certificates, and autonomous agents that can invoke models or move data on behalf of a workload.
• Identity Provenance: Identity provenance is the ability to trace an action back to the specific identity that initiated it, including the chain from enterprise identity to workload identity to platform event. In AI environments, strong provenance is essential for auditability, incident response, and policy enforcement.
• Identity Blast Radius: Identity blast radius is the scope of damage that one compromised identity can create across systems, data, and workflows. For AI platforms, it expands when the same account can invoke models, alter training jobs, and publish new versions without separation of duties.
• Just-in-Time Access: Just-in-time access is a credential pattern that grants permissions only when a task requires them and removes them quickly after use. For NHI governance, it reduces standing privilege and limits the window in which a stolen credential or misused role can be exploited.

What's in the full article

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

• Permission-by-permission breakdown of Vertex AI actions such as endpoint prediction, training pipeline creation, and model upload
• Guidance on separating model training, registration, and consumption duties across teams and identities
• Examples of how Cloud Audit Logs, Workload Identity Federation, and GCP policy boundaries support attribution
• Practical recommendations for governing cross-project and cross-region model usage

👉 P0 Security's full post covers model permissions, auditability, and cross-project control details.

Deepen your knowledge

AI model invocation, workload identity, and least-privilege lifecycle control are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building AI governance into existing IAM practice, it is worth exploring.