TL;DR: AI adoption is creating a visibility and governance gap as employees use external services, Copilot reaches into M365 data, and agents act on behalf of users, according to Vectra AI. The core problem is that existing access can now be discovered, moved and abused faster, so security teams need observability before AI use becomes unmanaged risk.
At a glance
What this is: Vectra AI argues that AI adoption is outpacing governance, with shadow AI, Copilot usage and agentic workflows creating new visibility and identity risk.
Why it matters: This matters because IAM, PAM and NHI programmes now have to govern not just access, but where AI services move data, which identities administer agents, and how quickly compromised access can be amplified.
Context
AI adoption is creating a governance problem because security teams cannot protect what they cannot see. External AI services, Copilot integrations and agent-driven workflows all introduce new paths for data movement and control, especially when identity permissions already span email, files and business applications. Agentic AI security becomes relevant when those workflows start acting on behalf of users or teams.
The central issue is not whether organisations use AI, but whether they can distinguish approved use from shadow AI, understand which identities control agents, and identify when sensitive data leaves governed boundaries. That is now a security and identity oversight problem, not just a productivity issue.
Key questions
Q: How should security teams govern AI data access without slowing the business down?
A: Security teams should define policy around data context, not around static folders or file names. The practical model is continuous discovery, high-confidence classification, and automated enforcement that limits risky access while preserving approved AI use cases. If controls require manual review for every exception, they will fail at AI scale.
Q: Why do AI agents make excessive access more dangerous than human access?
A: AI agents can use inherited permissions continuously, across multiple systems, and at machine speed. That means a single overbroad entitlement can create rapid exposure to data, workflows, or administrative actions before humans notice. The risk is driven by both breadth of access and the pace at which the agent can exercise it.
Q: What are the signs that AI tool usage is outside governance?
A: Look for repeated calls to AI endpoints from unmanaged devices, local processes tied to MCP servers, newly active OAuth tokens, and browser or desktop extensions that have not been reviewed. Those signals show the tool is not just installed, but actively moving data through a path security teams may not know exists.
Q: What is the difference between AI observability and AI governance?
A: AI observability tells you what the system did. AI governance decides whether it should have been allowed to do it, who approved it, and what happens when it crosses a policy boundary. Observability is a data problem. Governance is an operating model that combines policy, ownership, evidence, and enforcement.
Technical breakdown
How AI observability connects service usage to identity risk
AI observability is the act of correlating network, cloud and identity telemetry so teams can see which AI services are used, who accessed them, and where data flowed. In this case, the security value is not the dashboard itself but the ability to connect service adoption to the identities that enabled it. That matters because many AI risks emerge only after a legitimate account, SSO session or workload has already been trusted. Once that trust is in place, the visibility problem becomes an access problem: what was used, by whom, and against which data sources. See the AI Security Maturity Model.
Practical implication: Map AI service usage back to the identities and permissions that enabled it, not just to the service list.
Why agentic AI creates a governance layer above traditional access control
Agentic AI changes the control problem because the actor is no longer just a human user. An agent can be created, modified or administered by one identity and then operate with delegated permissions that outlive a single human decision. That makes the lifecycle of the agent, and the identity behind it, central to governance. If administration, ownership and logging are weak, the organisation loses track of who can change the agent, who can invoke it, and what actions it can trigger across business systems. See the Identity Governance for AI Agents Guide.
Practical implication: Treat agent administration and ownership as governance objects that need explicit inventory, review and offboarding.
How Copilot accelerates discovery when permissions are already too broad
Copilot does not create new authorisation, but it can make existing access easier to find, summarise and exploit. That means over-permissioned accounts, shared libraries and broadly exposed M365 content become more dangerous because search and summarisation compress the time needed to locate useful material. The technical issue is privilege amplification through faster discovery, not a new authentication flaw. Once an attacker or insider lands on a valid account, AI-assisted workflows can speed up collection across files, email and collaboration data. See the AI Agent Authorisation Guide.
Practical implication: Review broad file and mailbox access as a Copilot risk multiplier, not as a separate AI-only concern.
Threat narrative
Attacker objective: The attacker wants to turn existing identity access into faster discovery, broader data exposure and higher-impact misuse across AI-enabled workflows.
- Entry begins with a valid user account or non-human identity that already has access to business data and collaboration systems.
- Escalation occurs when AI services, Copilot or agent administration make discovery and control of that access faster and broader than manual workflows allow.
- Impact follows when sensitive content is summarised, moved or acted upon at speed, expanding the blast radius of the compromised identity.
Breaches seen in the wild
- AI LLM hijack breach: attackers used stolen AWS access keys to hijack Anthropic LLM models on Bedrock.
- Amazon AWS Hacked Accounts Crypto-Mining: Compromised IAM credentials across multiple AWS accounts fuel large-scale crypto-mining campaign.
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
AI readiness is now an identity governance problem, not a tool discovery problem. The article shows that organisations need to know where AI is used, who controls it, and how data moves through it before they can claim governance. That is a direct extension of identity visibility into AI-enabled workflows, where access can be legitimate but still unsafe. The programme implication is clear: inventory the identities, services and agents first, then govern the use cases.
Shadow AI creates a trust boundary problem that conventional approval lists cannot solve. Approved tools are only part of the picture when employees can route data into unmanaged external services. The governance failure is not simply lack of policy, but lack of ground truth about where data actually goes. That makes discovery and telemetry more important than policy statements alone. Security teams should treat unmanaged AI use as an identity and data flow issue, not only as a procurement issue.
Agentic AI introduces a new form of privilege drift. Agents can be created, modified and administered by one identity and then operate under another layer of delegated authority. That means the control gap is not just over-permissioned access, but unclear ownership of the thing exercising that access. The challenge aligns closely with agent lifecycle governance because organisations must know who controls an agent, who can change it, and when it should be retired.
Copilot expands the blast radius of existing over-permissioning. The article makes clear that AI does not invent authorisation, but it can accelerate discovery and summarisation across already exposed data. That means the real weakness is not the interface, but the underlying permission model and data sprawl it exposes. Practitioners should read Copilot exposure as evidence of broader IAM and information governance debt.
AI observability becomes the control plane for governed adoption. The market is moving from debating whether AI is allowed to deciding how much of it is visible, attributable and auditable. That shift favours programmes that can tie AI services back to identities, admins and data paths. The practitioner conclusion is to make observability the prerequisite for safe scale, not the afterthought.
From our research library:
- 96% of security operations teams report critical blind spots, most commonly in cloud infrastructure (74%) and identity and access behaviour (67%).
- Read next: Identity Governance for AI Agents Guide
What this signals
Shadow AI creates a control gap that policy alone cannot close. The practical issue is that employees will keep adopting external services faster than approval processes can catalogue them, so teams need telemetry that proves where data is flowing and which identities are involved. Observability is the prerequisite for any meaningful AI governance programme.
Identity visibility becomes the deciding control as AI spreads across the enterprise. Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey. That gap tells practitioners to prioritise ownership, administration and access review for agents before adoption becomes irreversible.
AI-assisted discovery changes the shape of blast radius. When a valid account or non-human identity already has broad access, AI can make it easier to locate and misuse valuable data at speed. The programme implication is to treat Copilot exposure, agent administration and external AI destinations as one connected governance surface, not separate risks.
For practitioners
- Inventory AI services by identity and data path Identify which AI services are in use, which identities access them, and which data sets they can reach. Prioritise unmanaged services and external destinations first because those are the hardest to govern after adoption spreads.
- Review agent ownership and administration Establish who created each agent, who can modify it, and who is responsible for its ongoing activity. Require lifecycle review for any agent that can trigger workflow actions or access sensitive systems.
- Tighten permissions exposed through Copilot Find accounts with broad access to files, mailboxes and collaboration content, then reduce unnecessary exposure before AI-assisted discovery makes that access easier to exploit. Focus on high-value repositories and shared content first.
- Separate sanctioned AI use from shadow AI Use existing controls to block or restrict risky AI destinations, then validate whether approved tools match actual usage. The goal is to stop unmanaged AI from becoming the default path for sensitive data movement.
Key takeaways
- AI adoption is colliding with identity governance because visibility into services, admins and data paths is lagging behind real-world use.
- The article’s central warning is that Copilot and agentic workflows can amplify the impact of already over-broad access rather than creating a new category of permission risk.
- Practitioners should treat AI observability, agent ownership and permission review as linked controls, not separate workstreams.
Related NHI Mgmt Group resources: Identity Governance for AI Agents Guide · AI Agent Authorisation Guide · Agentic AI Security Guide · AI Governance Platform 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 and risk surface, while NIST AI RMF, NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent administration and delegated authority are central to the article’s risk model. |
| ASI10 — Rogue Agents | The article focuses on unmanaged and poorly governed agents that emerge outside formal control. | |
| Recommendation — Apply ASI03 controls to restrict agent privilege, ownership and administration to explicit, reviewable authorities. Inventory and contain agent instances that appear without approved ownership or policy alignment. | ||
| NIST AI RMF | GOVERN — AI Governance and Accountability | The article is fundamentally about governance, visibility and accountability for AI use. |
| Recommendation — Establish governance roles, inventory processes and accountability for AI use before scaling deployment. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | Over-permissioned accounts and agent administration are the article’s main access-control concerns. |
| Recommendation — Review and reduce AI-adjacent permissions that let identities reach sensitive data or administer agents. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Non-Organizational Users) | The article includes service and agent identities that operate beyond human user boundaries. |
| Recommendation — Use IA-9 to authenticate and govern non-human actors that interact with enterprise AI services. | ||
Key terms
- AI observability: AI observability is the ability to see how AI systems are being used, what information they process, and what actions they trigger. In security programmes, it extends beyond uptime or model quality to runtime visibility, policy enforcement, and audit evidence across human and agent-driven use cases.
- Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
- 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.
- Privilege Amplification: A condition where a low-trust input path can influence a high-trust action path. In agentic systems, that happens when model-facing content can shape commands, commits, or credential use, allowing an attacker to get more impact than their direct access should permit.
Deepen your knowledge
NHI Mgmt Group covers identity security, NHI governance, and agentic AI through independent research, practitioner guides, and the NHI Foundation Level course, the industry's only accredited NHI security programme. It is designed for practitioners who need to connect identity governance to real operational risk across modern environments.
Published by the NHIMG editorial team on October 22, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org