Make AI governance enforceable.
Govern AI where AI actually happens: in browsers, IDEs, CLIs, desktop apps, personal accounts, and the locally-running agents your network and browser controls can't see. Discover shadow AI, block sensitive prompts in real time, and produce the audit-grade evidence NIST AI RMF, ISO 42001, and EU AI Act readiness ask for.
Demo + optional assessment
See AI usage control in 30 minutes.
A solutions engineer will tailor the walkthrough to the AI tools your team uses, the data you're protecting, and the compliance frameworks you're preparing for. If it's a fit, ask about layering on a 7-day Shadow AI Risk Assessment — a read-only deployment that quantifies your exposure and produces a board-ready snapshot.
Trusted by 10,000+ organizations governing AI use across financial services, healthcare, BPO, technology, and professional services
Ranked across both UAM and DLP categories - and recognized in 125+ countries.
Discover · Control · Evidence
One endpoint agent across the AI usage workflow.
A single lightweight endpoint agent captures AI tool use, LLM conversations, and agentic activity across Windows, macOS, and Linux - then enforces policy in real time. Shadow AI, browser agents, and locally-running frameworks like openclaw all surface in the same dashboards as ChatGPT, Claude, Copilot, and Gemini.
See AI usage end-to-end
Dashboards by user, department, and app - prompt frequency, sensitive-content rate, sanctioned vs personal account use. Spot the half of your team using AI you didn't know about, and the apps that are doing the heavy lifting.
Capture LLM conversations
Tamper-evident, OCR-searchable playback of prompts and responses across ChatGPT, Claude, Copilot, Gemini, and any browser-based LLM. Investigate a suspected leak in 30 seconds, not 30 days - the evidence is in the recording.
Detect agentic activity
Endpoint-resident detection of locally-running agent-of-agents frameworks (openclaw and similar), browser-agent tools, and autonomous task agents. Catches the AI workloads your firewall can't see because they execute on the device.
Block in real time
Hard-block uploads to AI tools, warn users, redirect to sanctioned apps, or lock the session. Behavioral DLP recognizes sensitive content in the prompt itself - not just the URL - so blocking is policy-driven, not allow-list whack-a-mole.
Where Teramind fits
Governance defines. Usage control enforces.
Your AI governance platform documents what should happen. Teramind proves and enforces what actually does — and feeds runtime evidence back into the workflows your risk, compliance, and audit teams already use.
AI governance — the policy layer
AI governance platforms inventory AI systems, run intake and approval workflows, map use to NIST AI RMF, ISO 42001, and EU AI Act, and produce executive evidence. They define what should happen with AI across the enterprise.
AI usage control — the enforcement layer
Teramind discovers shadow AI on endpoints, captures LLM conversations, scores risk by data sensitivity and intent, blocks sensitive prompts before they leave, and produces audit-grade evidence — then plugs that telemetry into your governance, GRC, and SIEM stack.
AI is already in production. Governance is not.
Shadow AI is the new shadow IT - except this time the data leaves the building.
“We needed to govern AI use without banning it - the tools were already in production for half our staff. Teramind let us see what was leaving in prompts, document who used which apps for the audit, and block on policy without becoming the team that says 'no' to AI.”
The endpoint blind spot
Why other AI controls miss the endpoint.
Most AI control planes were built for traffic, not for how AI actually gets used. Here's what each lens can't see — and why endpoint-first changes the picture.
Network · SSE · SASE
Misses local apps, encrypted flows, desktop AI tools, CLI commands, and locally-running models. Edge inspection can't see prompts typed into native apps or terminals, and tunneled traffic blinds the gateway.
Browser security
Misses IDEs, terminals, desktop apps, autonomous agents, and any non-browser workflow. The fastest-growing AI tools — IDE assistants, coding agents, desktop LLM clients — run outside the browser entirely.
LLM guardrails
Sees the model call, not the user. Can't capture the prompt before it's sent, the response after it's returned, or the file movement, credential pastes, and account switches that surround the conversation.
Governance platforms
Document policy and run lifecycle workflows. They don't enforce in real time, capture runtime evidence, or block sensitive prompts at the moment they leave — they assume someone else does.
Compliance
Evidence the new AI frameworks - and the old ones - both ask for.
NIST AI RMF (Govern · Map · Measure · Manage)
Inventory the AI tools in use, classify their risk, measure prompt-level sensitive-data exposure, and manage with role-based access and audit logs. The evidence auditors ask for is already captured by the agent.
ISO/IEC 42001 readiness
Document an AI management system: which models are sanctioned, how use is logged, how incidents are reviewed, who approved what. Session playback and policy logs give you the artifacts the standard expects.
EU AI Act preparation
For high-risk system documentation and human-oversight evidence, capture per-user interaction logs with timestamps, prompts, and policy outcomes - exportable in the formats compliance teams already use for GDPR.
SOC 2, HIPAA, PCI DSS 4.0, GDPR
Existing frameworks extend cleanly: AI use becomes a logged channel like any other. Sensitive-content blocking in prompts, masking, retention controls, and immutable audit trails are the same controls auditors already accept.
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