Basic endpoint posture integrations and network controls (SASE/proxy) are foundational, but they are not enough for AI governance. If you want granular, real-time control over how people use AI tools and agents, you need local, purpose-built sensors: a browser extension and a dedicated AI endpoint agent.
Enterprise security has always shifted with application architecture. Perimeter firewalls worked when apps lived in the data center. SASE and proxies became standard as SaaS moved to the cloud. EDR and MDM grew critical as endpoints became the new perimeter.
AI changes the shape of the problem again. AI tools, AI copilots, autonomous agents, and local LLMs are not just applications sending traffic over the network. They behave more like execution platforms embedded in user workflows, often making decisions locally, invoking skills, connecting to data sources, and taking actions in real time. That is exactly where traditional controls start to lose fidelity. The endpoint is undergoing a structural transformation, and AI is accelerating it.
At Prompt Security, we are tracking a clear direction toward an AI OS, where AI capabilities are embedded into the operating system and everyday workflows. Windows 11 is an example of this direction, with AI becoming more integrated into the endpoint environment. Some of these actions can happen locally without cloud round-trips.
The illusion of coverage: why network controls fall short
SASE and proxy solutions were built to inspect and control network traffic. That approach works when the risk is visible on the wire, and when inspection at the network edge tells you what matters.
Modern AI usage does not always fit that model. AI agents increasingly:
- Run locally on the endpoint
- Use local LLMs
- Access local files
- Interact with applications directly
- Execute actions without generating meaningful inspectable traffic
In many environments, these agents behave like persistent, privileged actors with access to the filesystem, the shell, credentials, browsers, and enterprise systems.
When prompts are processed and reasoning happens on-device, there is simply nothing useful for a proxy to inspect.
Local MCP servers can sit on the endpoint coordinating browser-AI interactions. Monitoring and controlling MCP communications requires endpoint-level visibility and enforcement.
MDM and EDR are foundational, but AI needs more
Many organizations try to extend AI governance through MDM or EDR integrations (mostly by periodically running scripts). These approaches are not designed to govern AI interaction layers. Software on the endpoint is no longer just traditional binaries managed by IT. Packages, extensions, MCP servers, models, and containers can be installed without oversight and governed by no one.
They provide device posture, process visibility, software inventory, and basic policy enforcement. What they do not provide is prompt inspection, skill-level enforcement, connector governance, runtime interaction analysis, context-aware redaction, or autonomous action control.
EDR can tell you a process is running. It cannot tell you what an AI assistant is reasoning about. MDM can confirm a device is compliant. It cannot enforce which AI plugins are allowed to access sensitive SaaS systems.
There is also a practical constraint that gets overlooked. These integrations depend on whatever telemetry and enforcement APIs a vendor exposes. If a new AI assistant launches tomorrow, an EDR tool does not automatically understand its internal configuration, its skills, or its data flows. You end up with posture signals and process metadata, not governance.
Why capability-level visibility matters
A common failure mode in AI security is treating assistants like static applications you can block or allow as a single unit. In practice, these systems behave as programmable platforms that can be assembled differently across users and teams.
They are composed of skills, hooks, plugins, connectors, extensions, automation chains, tool invocations, and model switching between local and cloud processing. Two users can run the same AI assistant with completely different risk profiles depending on how it is configured.
For example, one configuration might allow an assistant to find the right file based on folder contents, while another configuration might allow it to generate code based on folder contents.
That is why blocking an executable or domain rarely solves the real problem. The risk lives in the capabilities that are turned on and the access paths those capabilities create:
- Which connectors are enabled
- What data sources are accessible
- Which skills are active
- What autonomous actions are permitted
- What model processes the data
Governing AI safely requires inspection at the capability level, not just the application level.
Speed is now part of the security requirement
The AI ecosystem is moving faster than prior technology waves. New agents and assistants show up daily. New local model wrappers appear weekly. Connectors and plugins expand functionality constantly.
If your protection model depends on waiting for network signatures, waiting for vendor telemetry updates, blocking domains reactively, or relying on posture integrations, you end up behind by design. AI governance has to be adaptive, real-time, and context-aware, because the functionality you are trying to govern changes continuously.
What is actually required: local sensors
To secure AI usage properly, you need controls that operate where AI runs and where interactions happen. That means using local sensors designed specifically for AI, with two complementary components.
A browser extension for web-based AI
For browser-based AI tools, a local extension provides:
- DOM-level interaction awareness
- Real-time prompt inspection
- Context-aware data redaction
- In-moment user guidance
- Discovery of shadow AI tools
This enables enforcement at the interaction layer, where prompts are written and data is shared, rather than relying only on network controls at the edge.
A dedicated AI endpoint agent for agentic and local AI
For AI assistants, autonomous agents, and local LLMs, you need a dedicated endpoint AI security agent that can:
- Detect new AI agents immediately
- Understand active skills and connectors
- Inspect prompt construction and responses
- Analyze autonomous behavior
- Enforce runtime policies
- Restrict risky capabilities
- Prevent sensitive data exposure before execution
This agent needs full context of the endpoint environment, not just process metadata. Only with that level of inspection can you move beyond allow/block controls and toward granular enforcement.
Granular enforcement is the goal
AI cannot be governed safely through binary decisions, and most organizations do not want governance to become a productivity tax. The practical goal is to allow AI with guardrails while controlling the specific risk drivers: restrict certain connectors, disable risky skills, enforce least-privilege access, redact sensitive information dynamically, and monitor autonomous actions.
The new AI security model
To enable AI adoption safely at scale, organizations need network visibility and posture awareness, plus browser-level inspection and a dedicated AI endpoint agent. The endpoint is where AI operates, and the interaction layer is where risk emerges, so without local sensors that understand AI context and behavior in real time, security teams are making decisions without the visibility they need.
Prompt Security is built for this next-gen reality, providing guardrails across AI tools and agents. To see it in action, book a demo with our team.
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