TL;DR: Agentic AI is moving from experimentation into production, according to AvePoint, with Google's integration of Agentspace into Gemini Enterprise reflecting the shift, McKinsey reporting 71% of respondents now use GenAI in at least one business function and Gartner projecting 80% of routine customer service tasks will be handled by agentic AI by 2029. The control problem shifts from model adoption to permission sprawl, traceability, and policy enforcement across every agent action.
At a glance
What this is: NHI Mgmt Group's analysis, drawing on AvePoint's blog post: Google folded Agentspace into Gemini Enterprise, and the governing issue is no longer whether agents can work but whether permissions, auditability and compliance can keep pace.
Why it matters: This matters for IAM, PAM, cloud security and compliance teams because agentic AI inherits existing access models and can amplify permission gaps, data exposure and audit failures across connected systems.
By the numbers:
- 71% of respondents said their organizations regularly use generative AI in at least one business function, according to McKinsey's The State of AI: Global survey cited by AvePoint.
- Gartner anticipates that by 2029, agentic AI will handle 80% of routine customer service tasks without human intervention, according to AvePoint.
Context
Agentic AI is software that can decide and act across systems, which changes the security problem from static access control to governed delegation. In practice, that means the identity model has to cover how agents are authorised, monitored and constrained across business applications, data stores and collaboration tools.
AvePoint's framing of Gemini Enterprise shows why the governance question now sits beside productivity. When agents inherit existing permissions, any weakness in classification, least privilege or auditability becomes a live control gap rather than a theoretical one.
Key questions
Q: What breaks when AI agents are given broad inherited permissions?
A: Broad inherited permissions break the assumption that access is tied to a narrow business need. The result is larger blast radius, weaker accountability, and faster propagation of mistakes or abuse across connected systems. A single compromised or misconfigured agent can then touch far more data and workflows than the original task required.
Q: Why do agentic AI systems increase compliance risk in multicloud environments?
A: They move data and actions across systems that often enforce different rules for classification, retention, approval and logging. If policy is inconsistent, the agent can cross from tightly governed data into loosely governed data without any new decision point. That creates audit gaps and makes it harder to prove that access stayed within policy.
Q: How do teams know if AI observability is actually working?
A: It is working when teams can show which change caused a quality shift, which dataset surfaced the issue, and whether the regression was contained before users were affected. If the team cannot trace behaviour across versions, observability is producing logs, not governance evidence.
Q: When should organisations treat an AI agent as a privileged system?
A: Organisations should treat an AI agent as privileged whenever it can reach production data, administrative tools, or sensitive workflows without direct human approval for each step. At that point, the agent is no longer a passive automation helper. It becomes a governed identity whose permissions, logs, and exceptions need the same scrutiny as other high-risk access.
Technical breakdown
How agentic systems inherit and amplify existing permissions
Gemini Enterprise sits on top of existing enterprise accounts, data sources and application permissions. That means the agent does not create a new access model from scratch, it inherits the one already in place and then executes actions faster and more broadly than a human user could. If role design is broad, the agent can traverse more systems than intended. If entitlements are inconsistent, the agent can surface data from places the organisation never meant to connect. The technical issue is delegation without a fresh trust boundary. See the Agentic AI Identity Guide.
Practical implication: Map every agent to an explicit identity and entitlement boundary before production use.
Why observability and audit trails become mandatory control surfaces
Agentic systems are not safe because they are smart, they are safe only when their actions are traceable. Live observability matters because the control question is no longer just who signed in, but what the agent read, changed, escalated or handed off during a workflow. Audit logs need to capture the request, the source data, the tools invoked and the resulting system changes. Without that evidence, investigation and compliance teams cannot reconstruct intent or prove containment after an abnormal action. See the AI Agent Observability, Audit and Incident Response Guide.
Practical implication: Instrument agent actions with immutable logs that preserve decision context and downstream system effects.
How multicloud data access turns into a policy problem
When agents operate across Google Workspace, CRM, analytics and cloud storage, the security boundary becomes policy consistency, not just authentication. Multicloud access introduces mismatched labels, uneven approval logic and varied control enforcement, so a single agent can move from well-governed data to poorly governed data in one workflow. The real failure mode is not only over-access, but inconsistent interpretation of what the agent is allowed to do with information once it has retrieved it. See the AI Agent Identity Security Buyer's Guide.
Practical implication: Align data classification, access rules and usage policy across the systems an agent can traverse.
Threat narrative
Attacker objective: Exploit over-scoped agent access to reach sensitive data and execute actions that bypass human review and governance.
- Entry occurs when an agent is granted access to enterprise data and tools under existing user or service permissions.
- Credential and privilege exposure follows if those permissions are broader than the task requires, allowing the agent to reach systems and records beyond its intended scope.
- Impact emerges when the agent acts across connected systems at machine speed, exposing sensitive data, misapplying permissions or creating compliance failures that are harder to detect and unwind.
Breaches seen in the wild
- Gemini AI Breach — Google Calendar Prompt Injection: Gemini AI assistant prompt injection attack leaks sensitive Google Calendar data.
- Google API Keys Exposure — Gemini AI: Google API keys exposed in client-side code via Gemini AI integrations, creating data leak risk.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Agentic AI turns identity governance into a runtime control problem: the issue is no longer only whether an account exists, but whether delegated action is continuously justified at the moment of use. Existing permissions models were built for people who pause, review and escalate; agents can move straight from request to execution. That makes entitlement design, task scoping and revocation logic the real control surface.
Permission sprawl becomes a force multiplier for AI risk: when an agent inherits a messy access model, it does not behave like a cleaner user. It simply exposes every inconsistency faster and across more systems, which means poor RBAC design becomes visible as operational failure. Practitioners should treat broad inheritance as a governance defect, not a productivity feature.
Observability is the only way to make agent autonomy governable: if an organisation cannot explain what an agent accessed, why it did it and which systems it changed, then the control environment is incomplete. This is where agent observability becomes an identity governance requirement rather than a monitoring extra. The practitioner conclusion is simple: without action-level traceability, agent deployment outpaces accountability.
Gemini Enterprise is a signal that the market is converging on integrated orchestration and governance: vendor packaging is moving toward combined creation, orchestration and oversight because buyers are no longer shopping for isolated experimentation tools. That increases pressure on teams to evaluate whether their governance can follow agents across search, collaboration and business applications. The field is shifting from model access to managed delegation.
Read next: Agentic AI Identity Guide
What this signals
Agentic AI governance will increasingly be measured by delegation quality, not model quality: as more work shifts into autonomous workflows, the decisive question is whether every agent has a narrow purpose, a defined authority set and an auditable trail. Organisations that cannot express those three things in policy will struggle to operationalise agentic AI safely.
Runtime access review will replace one-time onboarding as the meaningful control check: static approval is too weak when an agent can act, fetch data and finish a task in a single session. The programme implication is that access decisions need to happen at issuance time, with continuous visibility into what the agent is doing across environments.
For practitioners
- Define agent identities before rollout Assign each agent a named identity, scope its authority to a specific task and separate it from human accounts and shared service credentials.
- Tighten permission inheritance across connected systems Review the permissions an agent inherits from Google Workspace, CRM, analytics and cloud storage, then remove any access that is not required for the workflow.
- Make agent actions fully auditable Log inputs, tool calls, data touched, approvals and resulting changes so investigators can reconstruct every material action the agent takes.
- Standardise data policy across multicloud sources Apply one classification and usage model to the data stores agents can reach, so policy does not change as the workflow crosses platforms.
- Pilot agents in low-risk workflows first Start with contained use cases where failure is recoverable, then validate monitoring, escalation and human override paths before expanding scope.
Key takeaways
- Agentic AI shifts the governance problem from who can sign in to what delegated action is allowed in the moment.
- The main risk is that existing permission sprawl and inconsistent multicloud policy become visible and exploitable at machine speed.
- Teams need named agent identities, task-scoped permissions and audit trails that can reconstruct every material action.
Related NHI Mgmt Group resources: Agentic AI Identity Guide · AI Agent Observability, Audit and Incident Response Guide · AI Agent Authorisation Guide · AI Agent Identity Security Buyer's Guide
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 addresses the attack surface, NIST AI RMF and NIST CSF 2.0 set the technical controls, and GDPR defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article centers on agent authority, inherited permissions and over-scoped access. |
| ASI09 — Human-Agent Trust Exploitation | The post warns that trust in agent actions can exceed the controls needed to verify them. | |
| Recommendation — Scope agent authority narrowly and prevent privilege inheritance from becoming uncontrolled delegation. Require explicit approval and traceability for high-risk agent actions instead of assuming trustworthiness. | ||
| NIST AI RMF | GOVERN — AI Governance and Accountability | The core issue is organisational governance over autonomous AI use and accountability. |
| Recommendation — Define accountable ownership, policies and escalation paths for every agentic workflow. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article repeatedly focuses on permission sprawl and entitlement control across systems. |
| DE.CM-01 — Monitoring for Anomalies and Events | Observability and auditability are central to safely operating agents in production. | |
| Recommendation — Review and constrain entitlements so agent access matches approved business purpose. Instrument agent workflows so anomalous actions and policy drift are detected in real time. | ||
| GDPR | Art.32 — Security of Processing | The article explicitly raises GDPR concerns when agents touch personal or sensitive data. |
| Recommendation — Apply security of processing controls when agents can access personal data across business systems. | ||
Key terms
- Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions, including calling APIs, writing code, and orchestrating other agents, with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
- Agent Identity: An agent identity is the set of attributes, credentials and permissions assigned to an autonomous software entity. It is treated as a non-human identity because it can authenticate, act on systems and accumulate access over time, which creates governance, audit and lifecycle obligations similar to other production identities.
- Permission Sprawl: Permission sprawl is the accumulation of unnecessary or outdated access across identities over time. In cloud and NHI environments, it grows through automation, rapid deployment, and weak offboarding, leaving more standing privilege than the business actually needs.
- Delegated authority: Delegated authority is permission given to one identity to act on behalf of another within defined limits. In identity systems, it enables an agent, user, or service account to perform approved actions without owning the underlying rights, using scoped tokens, roles, or approvals to constrain access and accountability.
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Published by the NHIMG editorial team on October 27, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org