How complex is deployment, and how fast do we see value?
You can land in an afternoon. Start agentless: connect the systems you already run — identity (Entra/Okta), your egress proxy, GitHub — through read-only APIs, and within an hour Ospiri inventories the AI agents, skills, and MCP servers across your fleet, mapped to people and sensitive data. The kernel-grade endpoint agent is an optional next step you roll out ring-by-ring when you're ready for runtime sandboxing. No rip-and-replace of anything you run today.
Can we start without deploying an endpoint agent?
Yes. The zero-install tier connects to your existing stack and returns a ranked inventory and risk report with no endpoint footprint and nothing for change control to approve. Most teams land here, prove value to their stakeholders, then graduate to the endpoint agent only on the machines that warrant deep, inline control.
Will the agent slow machines down or break agent workflows?
No. The agent is built around copy-on-write, not block-on-deny: when an AI process writes to a sensitive file, Ospiri clones it into a sandbox instead of killing the process — the agent keeps working and your real files stay untouched. It runs at kernel scope alongside your EDR with negligible overhead.
What business problem does Ospiri actually solve?
Your workforce adopted Copilot, ChatGPT, Cursor and a sprawl of "vibe apps" faster than anyone can govern them. Ospiri gives security and IT one place to see every AI agent, understand the business and GRC risk it carries, and stop the risky actions inline — so you can adopt AI faster without the shadow-AI blast radius. IBM's 2025 report puts the shadow-AI premium at +$670K per breach.
What is an "agent firewall," and how is it different from prompt guardrails?
Prompt guardrails inspect the text going into and out of a model. An agent firewall governs what an agent does once it's running — the files it touches, the registry keys it writes, the network it reaches, the MCP servers it calls. Guardrails stop at the prompt; Ospiri reaches the OS, where the real risk lives.
How does Ospiri help with compliance and audit?
Ospiri exports board- and auditor-grade evidence mapped to the EU AI Act, NIST AI RMF, and SOC 2 — a live inventory of your AI estate, who is using what, and proof of the controls enforcing it. Gartner's 2026 Guardian Agent Market Guide frames this shift as "governance moving into runtime," which is exactly the evidence auditors are beginning to request.
We already run CrowdStrike / Defender — why do we still need Ospiri?
EDR secures the OS against malware and known-bad processes. It sees Claude or Cursor as a trusted, signed application — it has no concept that an autonomous agent just decided to move a payroll file. Ospiri adds the AI layer on top of your EDR, not instead of it: same endpoint, complementary jobs. EDR protects the machine; Ospiri governs what AI does on it.
Doesn't my EDR already see what agents do on the endpoint?
It sees the process, not the intent. EDR watches for malicious binaries and behaviors; an AI agent taking a legitimate-looking action no human approved slips right through. Ospiri classifies the agent, correlates its actions to sensitive data and business context, and can sandbox or block inline — none of which EDR is built to do.
How is Ospiri different from AWS Bedrock Guardrails?
Bedrock Guardrails protect the generative-AI apps you build on AWS — content filtering, PII redaction, and prompt-attack detection at the model layer. They don't see the AI your employees run on their laptops (Copilot, Cursor, shadow tools), the endpoint actions those agents take, or anything outside AWS. Ospiri governs agent behavior at the endpoint, across every vendor and surface — a complement to Bedrock, not a substitute.
How is Ospiri different from Google Cloud Model Armor?
Model Armor screens prompts and responses and adds inline guardrails for Google's Agent Gateway and MCP servers — strong for agents running inside Google Cloud. But it's a cloud- and model-layer control for what you build on GCP; it doesn't reach employee endpoints, cross-vendor tools, or the file, registry, and network actions of agents on the device. That endpoint runtime is Ospiri's ground.
How is Ospiri different from Databricks (Unity Catalog / AI Gateway)?
Databricks' Unity AI Gateway centralizes guardrails, usage tracking, and governance for models served through Databricks. It governs your data-and-AI platform; Ospiri governs the agents your workforce actually runs — on endpoints, in browsers, across clouds — and enforces at kernel scope. They're complementary layers, not the same job.
Where does Ospiri fit in Gartner's framework?
Ospiri sits squarely in what Gartner's February 2026 Market Guide calls "guardian agents" — governance and runtime controls that supervise AI agents, monitor their actions, and intervene when behavior deviates. Gartner expects guardian agents to capture 10–15% of the agentic-AI market by 2030, and predicts 25% of enterprise breaches will trace back to AI agent abuse by 2028. Ospiri's differentiator within that category is inline, kernel-grade enforcement — most guardian-agent tooling only observes.