Go further
Secure the code, the cloud and the cognition.
From prompt to runtime, see what your agents use, stop dangerous actions and keep the evidence of every incident.
An AI agent does far more than produce an answer. It reads data, picks tools, calls APIs, uses MCP servers and can execute real actions inside your information system.
Ariovis helps you discover that surface, protect every layer and contain dangerous behaviour before it turns into a business incident.
One chain, protected end to end
The same component plays several roles at once inside your information system. Each role carries its own risk surface.
Code, dependencies, images and configuration that ship like any other service.
Secrets, tokens and entitlements that live outside any human session.
A model, its versions, artefacts and prompt templates — with their provenance.
Outbound calls to internal and external endpoints, some of them undeclared.
MCP servers and tools whose descriptions and schemas shape its behaviour.
Retrieval, RAG indexes and documents whose content becomes instruction.
Persistent state that stores, propagates and reuses whatever it has encountered.
Very real processes, files, syscalls and network flows.
The ability to read, write, transmit, modify or execute — that is, to produce an effect.
Protecting prompts alone leaves the code, dependencies, data, tools, APIs and the agent's real-world effect uncontrolled.
Four verbs structure the offer, in that order: discovery comes before understanding, understanding drives enforcement, and evidence makes the whole chain verifiable.
Discover the real surface, including everything that was never declared.
You cannot protect what you do not know about.
Connect every object to its business context to prioritise what matters.
Risk is not only about the vulnerability — it is about what the agent can actually reach and execute.
Act on the input, the tool call, the flow or the workload itself.
Useful protection acts before the effect, not only after the incident.
Reconstruct intent, path and effect — for the SOC as much as for audit.
Every incident must be explainable, fixable and verifiable.
Seven distinct surfaces stack around a single agent. A real incident almost always crosses several of them.
AI agent
Each surface has its own controls, but all of them are analysed inside a single graph.
Controls do not start in production: they are designed in and verified at every execution.
Cognition becomes a runtime layer. It must be observable, constrained and stoppable with the same rigour as a process, a file or a network flow.
Four capability families, activated according to the maturity of your use cases and the criticality of the actions involved.
An isolated alert explains nothing. What matters is the path: where the instruction came from, what it triggered and what it reached.
The path is reconstructed from signals collected at every layer, not inferred from a single application log.
Three common situations, handled from discovery through to evidence.
The Prompt Firewall protects language. The MCP Guard protects the action. Runtime protection governs the real effect.
The level of control follows the criticality of the action, not a uniform posture applied to every agent.
AI projects keep moving while protections are being put in place.
The architecture is qualified against your sovereignty, latency and operational constraints.
Fast start, hosted in a region of your choice.
Centralised analysis, controls enforced close to your environments.
Deployed inside your infrastructure, operated by your teams.
Environments without outbound connectivity and with controlled updates.
Discovery can be agentless where possible. Controls that must stop an action stay close to the runtime, the gateways, the APIs or the workloads.
Capability availability per deployment model is confirmed during technical scoping.
A deliberately narrow scope, chosen to be representative and demonstrable.
The six-week reference format is indicative: it depends on environment availability, integrations, use-case maturity and security prerequisites.
Ariovis designs and integrates an agentic protection layer suited to your environment, then connects it to your IAM, cloud, API, DevSecOps and SOC capabilities.
You remain the owner of your environments, your data and your security decisions.
It does not replace your IAM building blocks: it relies on them and adds control over the real effect.
Decide whether a specific action is legitimate given the identity, the resource and the context.
Explore the serviceProtect the secrets, technical accounts and privileged access the agent relies on.
Explore the serviceSurface the groups, delegations and attack paths reachable by the agent.
Explore the serviceHandle alerts, maintain controls and improve protections.
Explore the serviceA filter acts on language, not on effect. It sees neither the agent's code and dependencies, nor the tools it calls, the APIs it reaches, or the processes and network flows it generates. A malicious instruction can arrive through an indexed document, a tool result or contaminated memory, then produce a real action that only a control close to the runtime can interrupt.
By combining several sources: traffic to model providers, API calls, running workloads, code repositories, MCP manifests and configuration. These signals are correlated to build an inventory, identify owners and surface Shadow AI, including usage started outside the usual project framework.
By inventorying servers and their tools, verifying owner, version and provenance, then restricting which tools each agent can see. Schemas and parameters are controlled, destinations and amounts constrained, sensitive actions simulated or sent for human approval, and results filtered before they return to the agent.
By controlling what enters indexes and memory, detecting instructions hidden inside documents, partitioning retrieval per tenant or per record, masking sensitive data, and making it possible to freeze or restore a contaminated memory.
Because that is where the real effect happens: a process started, a file read or written, an outbound flow. It is the last place a dangerous action can still be stopped, and the only one that proves a fix actually changed the observed behaviour.
Yes, and that is the recommended starting point. Discovery and observation build a baseline, policies are tested in simulation, and blocking is enabled only where the action is sensitive or irreversible.
Through cross-layer correlation: an unusual instruction, an unexpected tool chain, abnormal volume or cost, access to out-of-scope data, a never-seen process or egress. Detection targets the sequence, not an isolated event.
By cutting whatever produces the effect: revoking secrets and tokens, removing tools, blocking network destinations, switching to read-only, isolating or stopping the workload, freezing memory. These actions are prepared in runbooks so they can be executed without improvisation.
Detections, timelines and evidence are exported to your SIEM and SOAR tooling, with tickets and runbooks attached. The goal is to use your existing handling processes rather than adding one more console.
Fine-grained authorization answers the permission question: is this action legitimate in this context. This offer answers the effect question: what the agent actually does, with which tools and data, whether its environment is sound, and how to interrupt then explain an incident. The two complement each other and both build on your IAM, PAM and Access Management foundations.
Use cases
This capability often contributes to a broader response. Explore how it works with other Ariovis services to address concrete challenges.
AI and automation
Give AI agents autonomy without losing control over their identities, their tools, their data and what they actually do.
Start from one agent, its tools and a real workflow. Ariovis helps you discover its action surface, test its risk scenarios and roll out the necessary protections step by step.
