AI adoption is no longer a future planning exercise. It's happening inside the AI Enterprise. Employees are using external AI services to move faster. Microsoft Copilot is becoming part of everyday work. New agents are being created to support workflows. Platforms like AWS Bedrock and Azure AI Foundry are giving builders new ways to ship AI-powered capabilities.
That is exciting but is also unchartered territory for security teams. The question for them is no longer whether to use AI. Most organizations already are. It is whether they can see how AI is used, understand the emerging risk and protect the business as adoption grows.
AI creates a visibility challenge
Some AI use is approved. Some of it is shadow AI, where employees use unmanaged services outside formal governance. Some of it involves data moving to external AI destinations. Some of it involves agents that can act on behalf of people, teams or workflows.
Each situation carries a different risk. A user pasting sensitive data into an external AI service is one kind of exposure. A Copilot user with broad access to sensitive files creates another. An agent being modified by the wrong identity creates another. And if a human or non-human identity is compromised, AI can increase the blast radius of what that identity can access, summarize, move or act upon.
Security leaders need practical answers: where AI is being used, who is using it, how much data is moving, what agents exist, who administers them and where the team should focus first. Vectra AI's AI Readiness features help turn those answers into actionable intelligence for security teams.
Evolving Threats: AI changes the speed and scale of attacks
The risk is not simply around employees using new AI tools. It is what happens when AI connects identities to data already spread across the business. For example, an attacker may compromise a valid user account that already has access to sensitive files, email, Teams conversations, SharePoint sites, or business application. With Copilot, discovery is performed faster. Instead of digging manually, an attacker may use AI-assisted workflows to find and understand valuable information quickly.
Agents raise the stakes. If the compromised user can administer agents, the attacker may influence workflows or abuse the identity behind an agent. If a non-human identity is compromised, the activity can be harder to spot because automation is often expected. Data movement adds another layer of risk. If sensitive content is flowing to unmanaged AI services or cloud AI platforms without guardrails, security teams need to know.
AI should not be slowed down. It needs to be visible. Security teams need to see which services are in use, where data is moving, which agents exist, who controls them, and when identities behave unusually. That is when AI readiness becomes security readiness.
AI Observability: find AI usage before it becomes unmanaged risk
AI governance cannot start with a spreadsheet of approved tools. It must start with understanding and validating ground reality.
Vectra AI’s AI Observability capability brings together network, cloud and identity telemetry to show which AI services are being accessed, how broadly they are used and where data is flowing externally. Teams can see high-traffic AI services, AI service reach by connection count, data movement to external AI services, OpenClaw instances, AWS Bedrock activity and AI services accessed through Entra SSO.
That visibility gives security and governance teams actionable insight. They can remove or restrict unsanctioned AI tools, block risky AI destinations through existing controls, investigate large data movement, audit which teams are building with AI platforms and validate whether usage aligns with approved policy. It also helps move unmanaged AI adoption into a governed rollout instead of treating it as a surprise after the fact.
Agentic AI Estate: understand agent sprawl and who controls it
Agentic AI introduces a new layer of complexity and risk. Agents can support workflows, access systems, trigger actions and operate with delegated or assigned permissions.
Vectra AI’s Agentic AI Estate capability helps security teams understand which agentic applications exist, who is using them and who is administering them. It shows active agentic applications and their administrators, M365 productivity agents that are deployed, modified or deleted, top admins and operations on those agents, Azure Foundry agents created and Security Copilot agents and their administration. Vectra AI focuses this view on agentic AI that matters most for security teams: Microsoft Copilot agents, Security Copilot agents, Azure Foundry agents and agentic applications that users log into.
The outcome is not just inventory. Customers can build an accounting of agentic identities, rein in unmanaged rollout, review privileged agent activity and audit the identities behind agent administration. They can ask who created an agent, who modified it, which users interact with it and whether the activity aligns with existing policies.
This becomes critical if an agentic identity, or the identity behind an agent, is compromised. Security teams need to know when that identity performs unusual or high-risk actions: privilege changes, abnormal administration, unexpected access to sensitive resources, unusual file or mailbox activity, high-volume downloads or behavior that deviates from its normal role. Vectra AI helps customers audit agent identity activity and detect suspicious behavior across human and non-human identities so teams can investigate and contain misuse before it expands.
Copilot for M365: understand adoption and exposure
Microsoft Copilot helps people find, summarize and act on information across M365 applications such as OneDrive and SharePoint. That usefulness is also why security teams need visibility. Copilot does not create permissions out of thin air, but it can make existing access easier to use (and abuse). If an account is over-permissioned or compromised, Copilot can increase the speed and reach of discovery behaviors enabling attackers to move much faster.
The Copilot for M365 dashboard capability shows real usage across the organization. It highlights top Copilot users, files accessed most through Copilot and suspicious Copilot usage patterns tied directly to Vectra AI detections.
From there, teams can review whether sensitive files are being accessed in expected ways, investigate unusual Copilot usage, validate permissions and connect Copilot activity to broader identity risk. Instead of debating Copilot risk in the abstract, they can look at actual users, actual files and actual behaviors.
Visibility becomes useful when teams can investigate
With AI Readiness, teams can move from a high-level view of their AI usage directly into the underlying telemetry, then continue investigating in natural language with the help of Vectra AI’s Agentic AI Assistant. If an external AI service that is non-compliant or a risk to their business, an analyst can review the exact identities, devices, apps and context behind it. When an AI Agent is administered or modified, the analyst can see who performed the operation, what changed and whether it fits policy.
That closes the gap between what security teams think is happening and ground reality.
Helping organizations adopt AI with confidence
AI adoption is moving quickly, and security teams are being asked to support the business without slowing it down. That requires more than policy. It requires visibility, context, investigation and detection. For security teams using Vectra AI's Pro plan, AI Readiness capabilities help discover AI usage, identify shadow AI exposure, monitor data movement, understand agentic AI risk and detect suspicious behavior across human and non-human identities.
The AI Enterprise is here. Vectra AI helps security teams see it clearly, understand where risk is growing and protect the business as adoption expands.