vaulty ai

AI agent governance that puts policy before action

The agent proposes. Your policy decides.

AI agents can reason, plan and interact with business tools. But when an agent requests an action, how do you know it follows your organization’s policies?

Vaultly AI introduces an AI agent governance layer that evaluates requested actions against defined policies before they proceed. Apply access controls, route sensitive actions for approval and restrict actions that don’t meet your requirements.

Bring policy enforcement, oversight and accountability to your AI-powered workflows without relying on the agent to govern itself.

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Role-based access control

Security guardrails

Policy management

Try a request

Signed in as: PAM operator

  1. 1

    Agent reads and proposes

  2. 2

    Policy engine checks your role

  3. 3

    Approval gate

Vaulty AI, in plain terms: AI agent governance for every tool your agents can call

Vaulty AI sits between your AI agents and the systems they act on. You ask for something in everyday language, and the agent works out which tool call answers it. What happens next is decided by code. A policy engine checks your role, and an approval gate holds anything destructive until a change ticket matches.

Every MCP server you connect gives an agent another way to act: read a record, rotate a secret, change a cloud setting, close a ticket. A language model is good at choosing the call. It is a poor place to keep the rule about who may make it. Vaulty AI keeps those jobs apart, and that separation is AI agent governance in practice. The same policy engine, approval gate and audit log cover CyberArk, BeyondTrust, Azure, AWS, ServiceNow and any MCP server you run yourself.

Built by
Hassium Cyber Solutions
Category
PAM agent with a policy engine and approval gate
Connects through
MCP integration, plus 500+ CyberArk operations as ready tools
Runs
Self-hosted or SaaS, with your own LLM key
Sign-in
AD/LDAP, OIDC or SAML
How a request moves through Vaulty AI: the user asks, the agent proposes, the policy engine checks the role, and the result is run, held for a ticket or denied. All outcomes go to the audit log. You ask in plain language Agent proposes one call Policy engine checks role Read Runs now, result logged Destructive Waits for a matching ticket Outside the role Denied, attempt recorded Every outcome lands in the hash-chained audit log

Three parts and only one of them is AI.

Anyone can wrap a language model around an API. The hard part is proving afterwards that it could not have done anything it wasn’t allowed to. Vaulty AI™ separates the one that thinks from the ones that decide.

The agent

Reads and proposes

Ask in plain language. The agent finds the right operation across hundreds of tools, fills in the details and explains what it wants to do.

Can’t: execute anything on its own. It has no direct path to your systems.

The policy engine

Enforces who may do what

Every proposed call is checked against role permissions, guardrails and a risk classification in code, not by asking the model nicely.

Fails closed: unknown actions are treated as destructive; unknown users get least privilege.

The policy engine

Holds destructive work

Deletes, rotations and other high-impact changes wait for an approved, scope-matched change ticket for every role, admins included.

Then: the approved action runs automatically and the record is sealed in the audit chain.

AI agent governance at every layer

Same copy as before, with one change. In “Hard-stop guardrails,” replace “Vault data” with “Tool output”: Tool output is treated as untrusted and cannot steer the agent.

Identity

Sign in with AD, OIDC or SAML. Each session is isolated.

Role-based access

AD groups map to roles. The agent only sees tools the role allows.

Policy management

Custom roles and permissions per domain and server. Unknown means denied.

Hard-stop guardrails

Vault data is treated as untrusted and can’t steer the agent.

Injection fencing

High-risk operations are blocked outright, whatever the prompt says.

Secret hygiene

Passwords are masked before the model or the logs ever see them.

Eight orbits around the sun.

Nothing orbiting is trusted. Each orbit is a boundary that must be crossed to reach your privileged targets, and the agent is a body in transit at orbit 05 inside four defences, outside three more.

Eight orbits of defence around privileged targets Concentric rings from governance on the outside to session mediation next to the privileged targets at the centre. The AI agent sits at orbit five, inside four defences and outside three more. Each ring lists its mapped framework controls. The asset Privileged targets everything else exists to keep this safe 01 Governance & accountability who is answerable NIST GOVERN 1.1–1.7 · 2.1–2.3 | ISO A.2.2 · A.3.2 · A.5.2 | EU Art. 4 · 9 · 17 | AGT 17 02 Observability, attribution & response spans every orbit inside it AGT 11 · 12 | NIST MEASURE 3.1 · MANAGE 4.1 · 4.3 | ISO A.6.2.8 · A.8.4 | EU Art. 12 · 19 · 73 | ATLAS Exfiltration · Impact 03 Input integrity & trust boundary stops indirect prompt injection OWASP LLM01 · LLM08 | AGT 13 · 14 | ATLAS Initial Access | NIST MEASURE 2.7 04 Model & supply-chain integrity stops poisoning and backdoors OWASP LLM03 · LLM04 | ISO A.7.2 · A.7.5 | NIST GOVERN 6.1 | EU Art. 10 05 Agent runtime containment prompt, tools and model pinned under change control AGT 15 · 16 · 18 | OWASP LLM07 | NIST MEASURE 2.4 | EU Art. 15 06 Agency & authorisation limits bounds the blast radius OWASP LLM06 | AGT 05 · 06 · 07 | ATLAS Priv. Escalation | EU Art. 14 07 Identity & credential control no standing access AGT 01 → 04 | ATLAS Credential Access | NIST GOVERN 1.6 08 Session mediation & command control agent never holds the credential AGT 08 · 09 · 10 | ATLAS Execution | EU Art. 12 The agent a body in orbit, not the sun and not one of the rings End-user prompts ITSM tickets & comments Retrieved vector chunks Documents, email & tool output Output & downstream Protects what consumes the agent, not the sun. OWASP LLM05 · LLM02 · LLM09
  • NIST AI RMF 1.0
  • ISO IEC 42001
  • OWASP LLM Top 10
  • ATLAS MITRE, for ML
  • EU AI Act
  • AGT Agentic + Privileged targets

Orbits 01–02 are cross-cutting: they govern and observe every orbit inside them. Orbit radius is defence depth, not distance the innermost orbits are the last thing standing between the agent and the vault. AGT controls fill the gap the five public frameworks leave around agents that hold credentials.

Connect your stack. Govern every action.

Plug Vaulty AI into the systems you already run, through MCP. One policy engine, one approval gate and one audit trail across every server your agents touch. Add a server today and it is under the same governance the same day.
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MCP integration, without rebuilding governance for every tool.

The Model Context Protocol (MCP) is the open standard that lets an AI application discover and call tools on other systems. Vaulty AI works as your MCP integrator. It connects to as many servers as you need and applies one policy layer over everything they expose.

  1. Mount the server. Add the address and credentials. Vaulty AI reads the list of tools it offers.
  2. Classify each tool. Mark it as read, change or destructive. Anything left unclassified is treated as destructive until someone decides otherwise.
  3. Map tools to roles. Choose which roles see which tools, per domain and per server. The agent never sees a tool the signed-in role cannot use.

The tenth server gets the same controls as the first, which is how an agentic AI platform stays governable as it grows.

 

Vaulty AI at the centre, connected by MCP to CyberArk, BeyondTrust, ServiceNow, a custom server, AWS and Azure. Vaulty AI policy · gate · audit CyberArk BeyondTrust ServiceNow Your own server AWS Azure

Answers for the questions your security team will ask.

An agent gets approved only when it can be proven safe. Here is what we tell the people who sign off.

What can it do?Exactly what the user’s role allows. Permissions are enforced at execution time, per role, per domain and per connected server.
What stops it deleting production?Destructive actions need an approved change ticket that matches the request. No ticket, no execution.
How do I know what it did?Every privileged call is written to a hash-chained audit log. Editing or removing history is detectable.
Can it be talked into things?Tool results are treated as untrusted data, and authorization never consults the model — prompt injection has nothing to persuade.
Where do my secrets go?Retrieved credentials are withheld from the model’s context and masked in logs. Keys are encrypted at rest.

An audit trail that notices when it’s touched.

Each record carries the fingerprint of the one before it. Change a row, delete a row or strip the hashes, and verification points to exactly where the chain broke.

Safe listed operator · allowed a91f…3c07
Account onboarded operator · allowed prev a91f… · 5e2b…d418
Mismatch detected row edited after signing prev 5e2b… · expected 0c6a…
Safe deleted ticket approved · executed prev 0c6a… · 77d1…e9a0

Evidence for SOX, ISO 27001, DORA and NIS2 reviews comes from the same log your operators already create by working.

Questions people ask about Vaulty AI.
Frequently Asked Questions (FAQs)​

What is Vaulty AI?

Vaulty AI is an agentic AI platform from Hassium Cyber Solutions that governs AI agents across the systems they reach through MCP. Users ask in plain language, the agent proposes a tool call, and a policy engine and approval gate decide whether it runs. It works with CyberArk, BeyondTrust, Azure, AWS, ServiceNow and any MCP server.

No. PAM is one of its strongest uses, but Vaulty AI governs any AI agent working through MCP servers. The same role checks, approval gate and audit log apply to every connected system, whether it holds privileged credentials or not.

MCP integration means Vaulty AI can connect to any server that speaks the Model Context Protocol and offer its tools to the agent. Each mounted server inherits the same policy engine, approval gate and audit trail, so you classify the tools and assign roles instead of building governance again for every system.

No. The agent only proposes. Deletes, rotations and other high-impact changes wait for an approved change ticket that matches the request, for every role including admins. Once the ticket is approved the action runs automatically and the record goes into the audit chain.

No. It ships with 500+ CyberArk operations ready to use and connects to BeyondTrust, Azure, AWS, ServiceNow and any other MCP server, all under one policy engine.

You can self-host it inside your own network next to your vault, or start on the managed SaaS deployment. Either way you bring your own LLM key. Everyday reads use zero tokens, so cost stays predictable, and in a self-hosted setup your data stays inside your boundary unless you choose otherwise.

It treats anything returned by tools, tickets and documents as untrusted data, and authorization never consults the model. A hidden instruction in a ticket comment has no route to a permission, because the signed-in role decides what can run.

Every privileged call is written to a hash-chained audit log, so editing or removing a record is detectable. Teams can use the same log as evidence for SOX, ISO 27001, DORA and NIS2 reviews. The eight-orbit map on this page also lines the controls up against NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, MITRE ATLAS and the EU AI Act.

Hassium Cyber Solutions, a Hyderabad-based cybersecurity firm that specialises in identity and access management and privileged access management, with deep CyberArk and BeyondTrust experience. Hassium is MSME registered and part of the Claude Partner Network.