Use your own AI provider and your own API key. We do not resell tokens or mark up inference. You stay in control of cost, model choice, and data residency.
No agents, no cloud pipeline. Findings live in a local database on your machine. AI calls go from your machine straight to the model you chose. Senserva never receives your tenant data and never trains on it.
Answers come from your real scan graph through the Senserva MCP, with a deterministic ranking underneath. The AI explains and plans on top of facts, so you get evidence, not hallucination.
Every AI remediation is checked before it reaches your team. Findings become validated fixes you can ship with confidence, not raw model output pasted into a console.
The AI proposes, you decide. Nothing is applied to your environment silently. You review and approve every change, and you can always see why it was recommended.
Apply a fix, scan again, and the result confirms it worked. The loop closes on evidence, so improvement is measured, not assumed.
Loop engineering, with guardrails
The industry is starting to call the deliberate design of an AI's observe, decide, act, and verify cycle loop engineering: treating the feedback loop, not a single clever prompt, as the thing you actually engineer. Senserva has built on this idea for years, under our own name and our own patent. The difference is that a security loop has to be trustworthy, so ours runs on deterministic facts with a human at the controls.
A loose, one-shot AI loop is fine for a brainstorm. It is not fine for changing identity, access, or device policy in a live Microsoft 365 tenant. Senserva Trustworthy AI is the guardrail.
A read-only scan builds the graph of your real configuration, logs, identities, devices, and CVEs. This is ground truth, not a prompt.
A deterministic ranking puts real-world risk first. The AI reasons on top of that ranking to explain and to plan, grounded in the graph.
Proposed fixes are validated, then presented for your review. You approve what gets applied. Nothing happens silently.
The next scan confirms the gap is closed. The loop is closed on evidence, and the result feeds the next cycle.
Deterministic where it counts, AI where it helps
Trust comes from knowing which part of the system is doing what. The checks, the risk ranking, and the compliance mappings are deterministic and repeatable: run them twice and you get the same answer, every time. The AI sits on top to explain findings in plain language, draft reports, and build remediation plans. It never replaces the facts, it works from them.
That separation is what lets you defend a decision. You can show the deterministic basis for a ranking, and you can show the AI plan that acted on it, with a human approval in between. The grounding comes from Siemserva's unified security model, configuration, patching, and logs in one place.
Where Senserva Trustworthy AI shows up
The same principles apply across every AI capability in the product.
Plain-language reports drafted from your real findings, with the evidence attached.
AI security reportsValidated, ready-to-run fixes you review and approve, proven by the next scan.
AI remediationFindings mapped to frameworks, with AI explaining the gap and the path to close it.
AI complianceAsk plain-language questions and get answers grounded in your local graph, no per-query lookups.
Claude and MCPA defensible, deterministic ranking of CVEs and patches the AI can plan against.
CVE managementSee the AI reason about real Microsoft 365 security, and test your own judgment against it.
Beat ClaudeFrequently asked questions
It is our approach to using AI in security work so the output can stand up to an auditor. You bring your own AI model, your data stays local, every answer is grounded in your real scan findings rather than guessed, and every AI remediation is validated and reviewed by you before anything is applied.
No. Siemserva runs on your machine with no agents and no cloud pipeline. Findings are stored in a local database. AI calls go from your machine directly to the model provider you configure with your own key. Senserva does not receive your tenant data and does not train on it.
No. You bring your own model and your own API key, so there is no AI markup. Because the enriched data lives locally in the Senserva graph, answers are grounded and token cost stays low.
No. The AI proposes validated remediation, but a human reviews and approves every change. Nothing is applied silently. After you apply a fix, the next scan proves it worked, closing the loop.
See Trustworthy AI on your own data
Run the demo free, no registration, no access to your tenant, and watch the AI reason from real findings. Then register free to point it at your own environment.
Download and go, free