Runtime identity security for AI agents in Gemini Enterprise

At a glance

What this is: This is a product announcement about runtime identity security for AI agents in Google Cloud’s Gemini Enterprise Agent Platform, with the key finding that agent activity needs continuous identity-based enforcement rather than one-time authentication.

Why it matters: It matters because AI agents behave like privileged non-human identities, so IAM teams need visibility, ownership mapping, and policy enforcement at runtime to prevent overreach and escalation.

👉 Read Silverfort's analysis of runtime identity security for AI agents in Gemini Enterprise

Context

AI agent governance is becoming an identity problem before it becomes a tooling problem. These systems authenticate into cloud and SaaS services, chain tool calls, and act with enough autonomy that traditional session-based controls do not fully describe what they are doing. For IAM and NHI practitioners, the gap is not whether an agent can log in, but whether the organisation can inventory, constrain, and audit what it is allowed to do once it does.

Silverfort frames Agent Gateway as the place where those controls can be applied in real time, because every agent request passes through that layer before reaching downstream systems. That is a practical model for AI agent governance, but the broader issue is industry-wide: most organisations still lack a complete operational picture of their agent population, ownership, and privilege boundaries. That starting point is increasingly typical, not exceptional.

Key questions

Q: How should security teams implement runtime controls for AI agents in enterprise environments?

A: Start by enforcing policy at the point where the agent requests access, not only where the data lives. Bind each agent to a human owner, a defined task scope, and a limited set of downstream resources. Then log every decision so teams can trace when the agent exceeded scope or was blocked.

Q: Why do AI agents complicate non-human identity governance?

A: AI agents complicate governance because they combine autonomy, tool use, and delegated identity into one workflow. They may act through service accounts, inherit access from humans, and chain steps across systems. That makes ownership, privilege boundaries, and auditability harder to manage than with static machine identities.

Q: What breaks when AI agent access is reviewed only after the fact?

A: After-the-fact review leaves a gap between action and containment. If an agent can already reach a dataset, API, or SaaS system, the damage may be done before a human sees the alert. Runtime checks reduce that gap by stopping unauthorized actions before they execute.

Q: How do organisations know if AI agent governance is actually working?

A: Look for three signals: every production agent has a named owner, access decisions are enforced during runtime, and audit trails show when requests were allowed, denied, or escalated. If teams can only describe agent behaviour in hindsight, governance is still incomplete.

How it works in practice

Why gateway-layer enforcement matters for AI agent access

AI agents differ from human users because they do not follow a single login-to-logout pattern. They can authenticate, call multiple tools, and continue operating across systems in a sequence of actions that changes context as they go. A gateway layer is useful because it sits in the request path and can evaluate each access attempt before the action reaches the target system. That enables contextual policy checks against identity, privilege, and risk signals. The technical shift is from static permission assignment to continuous authorization. For NHI governance, this is closer to runtime control than classic access review.

Practical implication: Treat the gateway as an enforcement point, not just a telemetry source, when designing AI agent controls.

How AI agents create chained identity and privilege paths

An AI agent rarely acts alone. It may be linked to a human owner, backed by service accounts, and allowed to use MCP-connected tools or SaaS APIs. Each hop creates another identity assertion and another chance for privilege to widen. If the agent can trigger downstream workflows, a single compromised or overprivileged agent can become a path to lateral movement or data exposure. This is why agent identity cannot be managed as a standalone object. The security picture spans the agent, its human operator, the non-human identities it relies on, and the resources it can reach. That chain is the control surface.

Practical implication: Map agent-to-service-account-to-tool relationships before granting production access.

Why visibility and audit trails are central to agent governance

Visibility is more than discovery. For AI agents, it means knowing which agents exist, who owns them, what data and systems they can reach, and how their access changes over time. Audit trails matter because agent behaviour is dynamic and often multi-step, which makes post-incident reconstruction difficult without granular event records. When access decisions are tied to identity governance frameworks, teams can prove who authorised the agent, which policies were enforced, and where the scope was exceeded. That is the difference between having a log and having an accountability model.

Practical implication: Require auditable ownership and policy history for every production AI agent.

NHI Mgmt Group analysis

Runtime enforcement is becoming the deciding control for AI agent governance. Discovery alone is not enough when agents can act in real time and change behaviour mid-workflow. The practical question is whether access can be evaluated at the point of action, not after the fact. Organisations that keep relying on inventory-first models will keep finding blind spots after privilege has already been exercised.

AI agents create a new identity blast radius that conventional IAM programmes do not model well. A single agent can sit at the intersection of human ownership, service accounts, tool access, and cloud permissions. That makes overprivilege harder to spot and harder to contain. The governance lesson is straightforward: map the full chain of identity dependencies or assume the blast radius is larger than expected.

Continuous authorisation is now a baseline expectation for agentic workflows. One-time authentication does not answer what an agent is doing across multiple steps or whether its scope still matches the task. Policy must adapt to context, behaviour, and access path in motion. For practitioners, that means shifting from periodic review to runtime control wherever agents can reach production systems.

Identity ownership becomes an operational control, not a paperwork exercise. If teams cannot tie each agent to a human owner and a clear access purpose, accountability breaks down the moment something goes wrong. That is especially true when agents interact with MCP-connected tools and distributed SaaS services. Practitioners should treat ownership metadata as enforceable governance data, not documentation.

From our research:

• 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate, according to AI Agents: The New Attack Surface report.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
• The governance gap is structural, so practitioners should compare this risk model with OWASP Agentic AI Top 10 and align controls to agent misuse paths.

What this signals

Identity blast radius: AI agents do not just expand the number of identities in play, they expand the number of ways identity can be misused. For IAM and NHI programmes, that means the control objective is no longer simply authenticating the agent. It is constraining how far the agent can move, which resources it can reach, and how quickly an unsafe action can be interrupted.

With 80% of organisations reporting agent behaviour beyond intended scope in our referenced research, the problem is no longer theoretical. Security leaders should treat agent governance as a runtime discipline aligned to Zero Trust Architecture principles and continuous verification, not as a policy exercise that can wait for the next access review.

The practical signal for enterprise teams is whether they can prove ownership, enforce task-scoped access, and retain an audit trail for every agent decision. If those three elements are missing, the organisation is not governing agentic AI, it is observing it after the fact. That is the point where escalation paths become harder to unwind.

For practitioners

• Inventory every production AI agent and its owner Build a current register of agents, the human accountable for each one, the systems it can reach, and the service accounts or tokens it uses. Prioritise the agents that can touch sensitive data or administrative workflows.
• Enforce runtime policy at the request path Place access decisions where the agent request is made, not only in post-event review. Evaluate context, privilege, and destination resource before the action reaches cloud, SaaS, or MCP-connected tools.
• Bound agent privilege to task scope Remove standing access where possible and constrain agents to narrowly defined actions, datasets, and destinations. Revisit any agent that can chain multiple tool calls without a fresh policy check.
• Correlate agent activity with human accountability Tie audit trails to both the agent identity and the person who authorised its use. That makes it easier to investigate misuse, prove control enforcement, and contain scope creep across shared environments.

Key takeaways

• AI agents should be treated as governable identities with runtime access boundaries, not as ordinary application components.
• The main risk is not authentication failure alone, but identity chains that allow overprivilege, escalation, and unauthorised tool use.
• Practitioners need ownership, runtime enforcement, and auditable scope controls before agentic workflows move deeper into production.

Key terms

• AI Agent: An AI agent is autonomous software that can take actions, call tools, and execute tasks with some degree of delegated authority. In security terms, it behaves like a non-human identity that must be owned, constrained, monitored, and audited across its full action chain.
• Runtime Authorization: Runtime authorization is the practice of evaluating access decisions at the moment a request is made rather than relying only on pre-granted permissions. For AI agents, this matters because context changes during execution, and policy must keep pace with the action being attempted.
• Identity Blast Radius: Identity blast radius is the amount of damage an identity can cause if it is misused, overprivileged, or compromised. For AI agents, the blast radius can extend across cloud services, SaaS tools, and linked non-human identities, which makes scope control a primary security concern.

Deepen your knowledge

AI agent identity governance is a core topic in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building runtime controls for agentic workflows, it is worth exploring.

This post draws on content published by Silverfort: runtime identity security for AI agents in Google Cloud's Gemini Enterprise Agent Platform. Read the original.