> ## Documentation Index > Fetch the complete documentation index at: https://docs.blindsight.io/llms.txt > Use this file to discover all available pages before exploring further. # Overview > Endpoint DLP for AI traffic. A signed desktop agent intercepts LLM calls at the OS network layer and routes them through Blindsight so nothing reaches a public model unscanned. Blindsight DLP is the **endpoint** half of the runtime story. A signed desktop agent runs on every employee laptop (macOS + Windows), intercepts outbound LLM traffic at the OS network layer, and routes every prompt through Blindsight for verdict and policy enforcement before the bytes leave the device. It's the right answer when you cannot rely on every internal team to wire SDK-level Runtime Security on their own. Once the agent is pushed by your MDM, every prompt to every public model on every managed device is covered, automatically, without changing a line of application code. ## DLP vs Runtime Security The two surfaces share the same scanners, the same Apps, the same event store. They differ in **where the traffic is intercepted**. | Dimension | Runtime Security (SDK) | DLP (endpoint) | | ----------------------- | ------------------------------------------------------------------ | ------------------------------------------------------------------- | | **Where it intercepts** | In your application code (proxy `base_url`, scan API, tool calls). | At the OS network stack on every managed laptop. | | **Who deploys it** | The application team that owns the LLM call. | IT, once, via MDM (Jamf, Kandji, Intune, GPO, SCCM). | | **What it covers** | Your own apps that you instrumented. | Every browser tab, every IDE plugin, every third-party desktop app. | | **Identity** | API key + App ID per traffic source. | SSO-bound device + directory user, per machine. | | **Code changes** | Yes (one config value for proxy, two HTTP calls for scan API). | None. | | **Best for** | Apps you own and want fine-grained policies on. | Shadow-IT AI traffic from tools you don't control. | You usually want **both**. Runtime Security gives the application team tight control over its own LLM surface; DLP catches everything else. ## Why endpoint, not just network A network-perimeter DLP (a SWG or CASB) can route HTTPS through an inspection point, but it cannot: * **See identity per session.** Every prompt from your fleet looks like the same egress IP. With the agent, every prompt is bound to the directory user whose laptop sent it. * **Survive on Wi-Fi off-corporate-network.** Endpoint coverage follows the user, the home office, the coffee shop, the airport. * **Scan inside applications that pin TLS.** The agent installs your organisation's root CA on the device, so it can MITM TLS without client co-operation. The endpoint also gives you a tamper-evident posture: the agent reports its own integrity events to the cloud (`/agent/tamper-events`). ## How it fits together 1. **IT pushes the agent** via MDM. One enrollment token covers the whole fleet. 2. **The agent self-enrolls** on first boot and runs the SSO device-code flow to bind the device to a directory user. 3. **The OS network layer routes** every outbound LLM call (OpenAI, Anthropic, Gemini, Vertex, Bedrock, and the OpenAI-compatible ecosystem) through the agent's local proxy. 4. **The agent fetches centrally-managed policy** from `/agent/policy`, runs local TLS MITM with the org root CA, and POSTs each prompt to `/agent/scan` for a verdict. 5. **Allow / redact / block** is enforced on the device, exactly the same verdict logic as the SDK paths. The backend integration uses dedicated `agent/*` endpoints that share the same scanners, the same App config, and the same event log as in-app Runtime Security. Dashboards show DLP traffic alongside SDK traffic, attributed to the device's directory user. ## What it covers, what it doesn't DLP catches LLM traffic from any managed device, including: * Browser tabs (ChatGPT, Claude, Gemini, Perplexity, ...). * IDE plugins (Copilot Chat, Cursor, Continue, ...). * Native desktop apps (Claude Desktop, ChatGPT desktop, ...). * CLI tools that hit a public LLM API. It does not cover: * Unmanaged devices (personal phones, contractor laptops without the agent). Use SSO + conditional access for those surfaces. * LLM traffic that never leaves your network (a private model on your own VPC). Use the SDK reverse proxy in those services. ## Where to next MDM push for macOS and Windows. One enrollment token, the agent self-enrolls on every device. SCIM directory sync, OIDC SSO, and the device-code flow that binds each laptop to a directory user. The fleet view: device status, identity binding, tamper events. Centrally-managed AI host rules and the backend endpoints the agent calls.