AI agent protection
See what the agent actually does, understand its blast radius and contain its effects at runtime.
AI and automation
Secure AI agents
Give AI agents autonomy without losing control over their identities, their tools, their data and what they actually do.
An agent is neither one more user nor just another API. It decides, calls tools and produces real effects in your systems. The question is no longer only “who signs in”, but “which action may run, under which mandate, on which data”.
Does this sound familiar?
- Teams are already connecting agents to internal APIs and data.
- Agents rely on tokens, secrets or service accounts that nobody really governs.
- No one has a precise picture of the tools or MCP servers in use.
- Authorization rules are hard-coded inside the agents or the applications.
- Teams can filter prompts, but cannot contain an action at runtime.
- The SOC lacks the context to tell drift, prompt injection and compromise apart.
What you are trying to achieve
- Give every agent an owner, an identity and an explicit mandate.
- Limit the secrets, privileges and tools it can reach.
- Decide dynamically which actions are allowed.
- Protect prompts, MCP servers, APIs, RAG sources, memory and workloads.
- Detect an agent exploring or acting outside its scope.
- Contain, revoke and explain an incident.
How Ariovis secures this use case
An AI agent is not only an identity to govern or a prompt to filter. It is at the same time an application, a machine identity, an API client, an orchestrator of tools and MCP servers, a data consumer, a workload and a semi-autonomous actor able to produce a real effect.
The AI Agent Protection offer carries this use case end to end. IAM, privileged access and fine-grained authorization remain essential: they decide who acts and under which mandate. Agent protection extends that control to the code, the tools, the data and the agent's actual behaviour.
See how Ariovis protects AI agents from code to runtimeAI agent protection
See, understand, prevent, prove — end to end
- Code
- Model
- Data
- Prompt
- Tool / MCP
- API
- Workload
- Action
- Evidence
Complementary capabilities directly involved
The Ariovis capabilities involved
Each offer keeps its own role. Here is exactly what it brings to this situation.
Fine-grained authorization
Decide whether a specific action may run, based on identity, resource and context, outside the agent's own code.
Privileged access and secrets
Take secrets out of prompts and configuration files, and cut the privileges the agent holds permanently.
IAM strategy and Zero Trust
Set the entry rules: who may deploy an agent, under which mandate and which operating conditions.
Trapster — Deceptive Security
Place credible decoys that reveal an agent exploring beyond what it was entrusted with.
AI agent protection does not replace IAM. It extends it to the agent's tools, decisions and real-world effects.
The risk you meet, the capability that answers it
Securing an agent means being able to see, understand, prevent and prove. Here are the situations we meet most often, and what actually addresses them.
Discover and prioritise
The risk : “I don't know which agents and MCP servers are actually in use.”
What answers it : Inventory of agents and assets, Shadow AI discovery, owners and purposes, AI-BOM and lineage, dependencies, exposure, blast radius, then contextual scoring to decide where to start.
Understand blast radiusSecure before production
The risk : “The agent ships with hard-coded secrets and unverified dependencies.”
What answers it : Code and dependencies, secrets, images and containers, IaC, provenance of models and artefacts, prompts-as-code, MCP manifests, automated tests, red teaming and evaluations as CI/CD quality gates.
Take secrets out of prompts and configurationProtect execution
The risk : “My agent holds a tool far too powerful for what it is supposed to do.”
What answers it : Tool and MCP Guard: filtering the exposed tools, limits on parameters, destinations, volumes and costs, and an authorization decision taken outside the agent's own code.
Why an administrator role is not a security policyProtect execution
The risk : “An injected instruction triggers a perfectly valid, yet dangerous call.”
What answers it : Prompt and response protection, agentic API security, protection of data, RAG sources and memory, runtime sandboxing on processes, files and network, blocking, isolation, revocation and human validation when the action demands it.
See how an agent becomes a mandated identity (Hermes)Detect, respond and prove
The risk : “The agent starts reading thousands of documents, and the SOC cannot see the path that produced the action.”
What answers it : Detection of takeover or drift, exfiltration, memory poisoning, model tampering, agent-to-agent propagation, correlation with the SOC and SIEM, end-to-end timeline, containment, remediation and audit evidence.
Detect abnormal access from context and volume
What does this look like in practice?
“AI agent security” does not describe a single architecture. Depending on the agent, the priority questions change.
Securing an MCP server
An MCP server exposes real tools to an agent. The question becomes: which tools are published, with which parameters, towards which destinations and under which identity.
Manifests, tool scope, call ceilings, per-action authorization.
Protecting a coding agent
A coding agent reads code, installs dependencies, handles secrets and reaches the internet. The risk plays out before production as much as at runtime.
Code, dependencies, secrets, workload isolation, network egress.
Protecting a customer-service agent
A customer-service agent reads personal data and triggers commercial gestures. The risk moves to the data being read and the effect being produced.
RAG and sensitive data, business APIs, action ceilings, human validation.
Where to start
Pick one agent, one business workflow, its tools and its data, then map its real blast radius.
- 01Choose one agent already in use or about to ship, on an identifiable business workflow.
- 02List its tools, MCP servers, APIs, data sources and memory stores.
- 03Identify the identity, secrets and privileges it uses today.
- 04Describe the actions it can trigger, and those that should be refused.
This mapping is the entry point to the topic, not the whole path.See how the AI Agent Protection offer maps this surface and its real blast radius
Related use cases
To keep reading on situations that touch the same surface: technical identities, standing privileges, APIs and application access.
Access and privileges
Govern service accounts and secrets
Take back control of legacy technical identities: their owners, their secrets and their privileges.
Access and privileges
Reduce standing privileges
Replace permanent administrative rights with proportionate, temporary and traceable access.
Access and privileges
Modernise SSO and federation
Simplify access journeys while regaining control over protocols, sessions, applications and responsibilities.
A Practical Starting Point
A short format, already online, to get an objective view of your situation before committing to a project.
- Runtime
Runtime
A useful starting point to situate your maturity on dynamic authorization for AI usage (MCP, RAG, ABAC/PBAC). It opens the topic; it does not replace full agent protection.
- Fine-grained Authorization
Fine-grained Authorization
Helps qualify the applications and APIs your agents will call before opening any access.
Explore the Topic
Our published content that speaks directly to this situation.
Field note
An administrator role is not a security policy for an AI agent
Governing an over-powered role cleanly does not make that role less powerful: read this before handing privileges, service accounts or OAuth scopes to an agent.
Field note
Deploying Hermes in an Enterprise Environment: Best Practices
Turns the principles on this page into a concrete architecture: delegated identity, mandate, PEP/PDP, APIs rather than a generic connector, volume and context in the decision.
Field note
How do you spot an attacker reading too much data?
Shows how to spot an actor — human or agent — reading far too much data, using context and volume.
Field note
OAuth issues a token. AuthZEN asks whether the action is allowed.
Gives useful perspective on the difference between issuing a token and deciding whether an action is allowed.
Field note
An authorization engine does not know your business
Helps explore how business rules should move out of the application or agent code.
Understand the Concepts
The notions worth sharing with your teams on this topic.
Does this use case look like yours?
Tell us about your context. Together we identify the priority capabilities and the first useful step.