For MSPs and MSSPs
Managed AI governance for MSPs and MSSPs.
Help customers put AI agents to work under clear authority. Govern your own AI technicians, protect customer workflows, and build recurring services around policy, evidence, and autonomy reviews.
Two opportunities. One control plane.
The first is about how you deliver service. The second is a service you can sell. Both run on the same policy model and the same evidence format. Labor savings and new revenue are outcomes to measure in a pilot, not figures we promise in advance.
Govern your own AI technicians
Behavry governs provider-owned agents acting inside customer systems.
Your AI technicians, your ticket queue, your partner access into many customer tenants. Behavry enforces each customer's boundary and the authority it delegated on every governed action, so one agent can serve many customers without one customer's authority leaking into the next. When a customer asks what your AI did in their tenant, you hand them a signed record instead of a log export.
- Per-customer scope on shared agents and shared credentials
- Delegated authority mapped from the contract into policy, not inferred from the admin role
- Escalation to your service desk lead, not to the customer
- Evidence for the customer, the cyber insurer, and your own counsel
Sell managed AI governance to customers
Behavry discovers and governs the customer's own AI tools, agents, MCP servers, browser AI usage, and privileged integrations.
Your customers are already running copilots, agents, MCP servers, and browser AI with access to their mail, files, and admin consoles. Often nobody in their organization is watching it. Behavry finds what is visible through the agreed coverage, puts the high-risk parts behind enforcement, and produces the evidence. You package that as a recurring service.
- Discovery across agreed sources: IdP, SaaS admin APIs, endpoints, browsers
- Prioritized findings the customer's leadership can read
- Runtime enforcement on write-capable and privileged AI
- Evidence reports you deliver on a regular cadence
Four offerings. One ladder from assessment to recurring service.
Each step stands on its own and sets up the next. Discover and Assess are engagements. Protect is the recurring service. Command is how you run it across your customers. Autonomy Review sits on top, for customers who want it.
Proposed packagingAvailability, commercial terms, and delivery responsibilities are confirmed with each partner before anything is offered as a purchasable service.
Find AI in customer environments, through the coverage you agree.
- AI apps and copilots
- Agents, hosted and local
- MCP servers
- Browser AI tools
- Privileged integrations
- Write-capable agents
Customer getsAn inventory of AI assets visible through the agreed coverage, with its limits stated
Your roleFixed-scope discovery and onboarding
Turn discovery into findings and recommended authority boundaries.
- Risk findings, prioritized
- Delegated authority gaps
- Recommended authority boundaries
- Recommended controls
- A summary leadership can read
Customer getsPrioritized findings and recommended authority boundaries
Your rolePaid assessment and remediation planning
Put high-risk AI activity behind runtime enforcement.
- Warden desktop proxy
- MCP and API enforcement
- Browser coverage
- Policy packs, per customer and per action
- Allow, escalate, deny
- Decision Trace on each governed action
Customer getsManaged policy enforcement and exception handling
Your roleRecurring governance operations
Run the service across your customers from one operating view.
- Parent and child tenants
- Risk across your customers
- Violations and blocked actions
- Reports, per customer
Customer getsA provider who can see and act across their environment, within agreed access
Your roleThe operating console behind the service. Cross-tenant capabilities confirmed per partner
Proposed partner serviceBegins in pilot and shadow mode
A review of whether a customer's agent is ready for a broader, scoped grant: the evidence from its Decision Traces, the criteria the customer set, and a recommendation the customer approves or declines. Delivered through the operating model above, not as a fifth platform product.
- Customer gets
- An evidence assessment and a recommendation for a scoped grant
- Your role
- A recurring review, or a separately scoped readiness engagement
- What it does not do
- Change permissions. A recommendation is not an approval, and the first milestone runs in shadow mode
Buy what you need/Governance and protection stand alone/Autonomy Review is optional/No customer has to adopt autonomy changes
Summarizing tickets is safe. Acting on them is where the risk starts.
AI that reads a ticket and drafts a reply saves a technician minutes. AI that resolves the ticket can save far more, and every step of it is a privileged write inside an environment you do not own.
| Action the AI needs to take | Where it runs | What it needs to have |
|---|---|---|
| Reset a password | Entra ID, Active Directory | Credential write on a customer identity |
| Disable a stale account | Entra ID, Google Workspace | Account lifecycle rights, and the offboarding context to use them |
| Update Microsoft 365 settings | M365 admin center, Exchange Online | Tenant-level admin scope, delegated through partner access |
| Quarantine an endpoint | RMM, EDR | Isolation rights on a production machine |
| Modify groups and licenses | Entra ID, M365 | Group membership and license assignment across the tenant |
| Triage a phishing ticket | Mailbox, EDR, PSA | Mailbox read, message purge, and ticket write |
| Update an RMM policy | RMM | Fleet-wide policy write across an entire customer |
| Recommend or execute remediation | EDR, firewall, RMM | Change rights with no ticket-bound limit |
| Close the ticket | PSA | The authority to say the work is done and billable |
Every row is privileged. Every row is a different customer. The model does not know the contract.
The agent that resets a password at Acme has the same credentials, the same prompt, and the same blind spots when it moves to Smith Financial ten seconds later. Nothing in the model knows where one customer's authority ends and the next one's begins. That is not an AI safety problem. It is a service delivery problem with a liability attached.
Behavry sits between the AI technician and the customer system.
Not the prompt. The action: the Graph API call, the PowerShell, the MCP tool invocation, the admin-center write. Each governed call is evaluated before it runs against the boundaries you configured, and gets one of three answers.
From the PSA, a chat, an alert, or a scheduled runbook. Carries the customer, the requester, and the ask.
Your agent, on whatever framework. Reads the ticket, plans the fix, and attempts the privileged call.
Each governed action is checked against what you have configured, and recorded, before it is allowed to proceed.
- Customer boundaryWhich tenant this action may touch, and no other.
- Ticket contextThe request that justifies the action, where your ticket source is mapped in.
- Delegated authorityWhat this customer authorized, mapped from the contract into policy.
- PolicyYour runbooks as enforceable rules, per customer, per action.
- Credential scopeThe least identity that can do the job, scoped to the session.
- Data sensitivityWhat the action reads, moves, or exposes.
- Risk levelBlast radius, reversibility, and drift from the agent's baseline.
- Human escalationThe named approver when the answer is not a clean yes.
- Decision TraceA signed record of what was asked, decided, and why. Produced for each governed action.
M365, Entra, the RMM, the EDR, the firewall. Only receives the actions that passed.
In scope, in authority, in policy. The action runs, and the record shows it did.
Risk above the delegated line. Held for a human with the ticket, the diff, and the reason attached.
Outside the customer boundary, the contract, or the credential scope. Does not run. Ever.
Ticket, contract, and customer-scope checks work from explicit mappings you configure. Behavry does not read prose contracts. Which PSA, RMM, and identity systems can be connected is confirmed for each pilot.
Works with/MCP tool calls/API proxies/Browser AI/Desktop agents/integrations →
What the console looks like on a Tuesday.
An illustration of the operating view, drawn with made-up data for a provider with 147 customers. It is not a product screenshot, and cross-tenant console capabilities are confirmed per partner.
BEHAVRY COMMAND · MSSP CONSOLEFour customers you can call today. With evidence.
Each row is a conversation an account manager can open, with the evidence behind it.
Managed AI governance. A service line, not a feature.
A recurring engagement with a discovery phase, a regular evidence report, and a control the customer cannot get from their own tooling. The scope below is a proposed starting point, agreed with each partner and each customer.
The customer-facing promise“We found where AI can reach sensitive data, act with privilege, and operate outside the authority you defined. We now govern that activity on the paths we cover, and we bring you the evidence.”
What your account manager says. What the evidence report shows.
- Review changes in the covered inventory
- Tune the policies you agreed
- Handle exceptions under the contract
- Deliver an evidence summary, with signed traces for escalations and denials
- Identify workflows suitable for further assessment
Recommendations are reported separately from approved changes.
- Review permitted actions with the customer
- Assess agent readiness within specified workflows
- Run the relevant validation scenarios
- Agree any grant changes, in writing
An agent's history in one customer's environment never authorizes work in another.
Owns service delivery and the customer relationship.
Defines the authority it delegates, and keeps approval rights and evidence access, including through a change of provider.
Act within that delegation, and approve or decline what falls outside it.
Enforces the configured boundaries and supplies evidence for the supported paths. Signed traces can be checked at verify.behavry.ai with no Behavry account.
Proposed packaging/Discover and Assess as scoped engagements/Protect as a recurring service/Autonomy Review, recurring or scoped/Terms confirmed per partner
30-Day AI Technician Safety Pilot.
One customer environment and up to three explicitly supported workflows, so you can measure labor impact, policy outcomes, and evidence before you commit to a contract. Agent and tool compatibility is scoped before anything is signed.
- Scope the environmentOne customer tenant via your existing partner access. Agent, ticket-source, RMM, and EDR compatibility confirmed in scoping. PSA connections such as ConnectWise, Autotask, or HaloPSA are proposed per pilot, not confirmed integrations.
- Inventory, policy, observationGoverned actions are recorded and decided in observation first. Nothing is blocked yet.
- Enforcement, by agreementAllow, escalate, and deny rules built with your service desk lead, switched on through a change process you agree.
- Up to 3 explicitly supported workflowsChosen from the list, or your own runbooks once their paths are confirmed
- Decision Trace evidenceSigned and hash-chained for each governed action, verifiable by the customer
- A1 to A2 eligibility, in shadow modeOnly after the published demonstration gate passes. Permissions do not change.
| Workflow | Systems | Example starting policy |
|---|---|---|
| Password reset | Ticket → Entra ID | ALLOW within ticket scope, requester identity verified against the directory |
| Stale account disablement | Entra ID, ticket | ALLOW after inactivity check. ESCALATE if the account holds a privileged role |
| M365 group or license change | M365 admin, Entra ID | ALLOW for the ticket's named user. ESCALATE on admin groups or license removal |
| Phishing ticket triage | Mailbox, EDR, ticket | ALLOW read and classify. ESCALATE purge and sender block |
| Endpoint quarantine recommendation | RMM, EDR | ALLOW the recommendation. ESCALATE isolation to a human, every time |
| Low-risk remediation approval flow | RMM | ALLOW from the approved runbook list. DENY anything not on it |
Starting points, built with you in week one. Ticket and contract scope are enforced through mappings you configure; Behavry does not read prose contracts.
Bring one customer and the workflows you want to govern. We will scope coverage, authority, evidence, and success criteria together.
Your customer stays your customer.
These are the program requirements we are putting forward for commercial approval. They are proposals, not terms. We will confirm each one with you in writing before a pilot is contracted.
- Registration and attributionPartner registration and attribution on pilot requests, with your consent before any customer contact.
- Ownership rulesDefined rules for account ownership, renewals, expansion, and overlap with direct sales.
- Separate chargesBehavry platform charges kept separate from your assessment and managed service fees.
- EnablementDeployment guidance, a sample scope of work, and example evidence reports.
- Clear support linesEscalation and support responsibilities set out among the customer, you, and Behavry.
- Customer continuityCustomer-controlled approval and evidence access that survives a change of provider.
Co-branding, white-label exports, deal protection, console inclusion, partner discounts, and specific integrations are proposed until product and commercial owners confirm them. Nothing on this page sets a price or a term.
If removing Behavry means turning off your AI technicians, it is doing its job.
The control layer is what makes labor savings safe to keep. Take it out and the authority problem comes back.
Bring one customer and the workflows you want to govern.
We will scope coverage, authority, evidence, and success criteria together. You do not need to name the customer yet.
✓ Request received
Thank you. The Behavry team will reply to you within 2 business days with times to scope the pilot. We will not contact your customer unless you asked us to.
Would rather not wait? Pick a time now →
Nothing arrived after two business days? Email hello@behavry.ai and we will pick it up from there.
- COVERAGE
Eight ways an agent gets in, and where each one is intercepted.
- EARNED AUTONOMY
How an agent's authority is reviewed against evidence, and why a recommendation is not an approval.
- COMPLIANCE
Control mappings and evidence support, produced as a by-product of enforcement.
- DECISION TRACE
The evidence format, with a sample package you can verify yourself.