For MSPs and MSSPs
Let AI technicians do real customer work without giving them uncontrolled authority.
Behavry helps MSSPs move from AI-assisted service desks to AI-executed service desks by enforcing customer boundaries, delegated authority, and policy on every privileged action.
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 saves the technician. The margin lives in the second one, 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. None of them read 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 one is evaluated before it runs against everything the agent should have known, and gets one of four 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.
Every attempted action is checked against nine things before it is allowed to proceed.
- Customer boundaryWhich tenant this action may touch, and no other.
- Ticket contextThe request that justifies the action, and its scope.
- Delegated authorityWhat this customer authorized your agents to do, in writing.
- 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 on every 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.
Right intent, wrong parameters. Narrowed to the ticket's named user or device, then run.
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.
Works with/MCP tool calls/API proxies/Browser AI/Desktop agents/integrations →
Two ways MSSPs use it. One control plane.
The first protects your margin. The second creates a new one. Both run on the same console, the same policy model, and the same evidence format.
Protect your own AI operations
Behavry governs MSP-owned agents acting inside customer systems.
Your AI technicians, your PSA, your RMM, your partner access into a hundred M365 tenants. Behavry enforces each customer's boundary and delegated authority on every action your agents take, so one agent can serve every customer 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 modeled from the contract, not 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. Nobody in their org is watching it. Behavry finds it, scores it, puts the high-risk parts behind enforcement, and produces the monthly evidence. You sell that as a service line with margin, from the console you already run.
- Discovery across IdP, SaaS admin APIs, endpoints, and browsers
- An exposure score the customer's board can read
- Runtime enforcement on write-capable and privileged AI
- A monthly evidence report with your name on it
Four offerings. One ladder from assessment to recurring revenue.
Each step is sellable 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 the book.
Find unmanaged AI across customer environments.
- AI apps and copilots
- Agents, hosted and local
- MCP servers
- Browser AI tools
- Privileged integrations
- Data exposure paths
- Write-capable agents
Sells asA fixed-scope discovery engagement
Turn discovery into customer-ready exposure reports.
- Autonomous Exposure Score
- Risk findings, ranked
- Delegated authority gaps
- Recommended controls
- Board-readable summary
- Your logo on the cover
Sells asAn assessment deliverable, or the QBR
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, modify, escalate, deny
- Decision Trace on every action
Sells asThe monthly managed service
Manage AI risk across every customer from one MSSP console.
- Parent and child tenants
- Fleet risk across the book
- Customer risk rankings
- Violations and blocked actions
- Report generation, per customer
- Pilot-to-managed conversion tracking
Sells asYour operating console. Not resold.
What the console looks like on a Tuesday.
Example data for an MSSP tenant with 147 customers under management. The figures are illustrative. The shape of the day is not.
BEHAVRY COMMAND · MSSP CONSOLEFour customers you can call today. With evidence.
Each row is a conversation an account manager can open. Each conversation ends in a Protect line item.
Managed Autonomous Security. A service line, not a feature.
Call it Managed AI Governance and Protection if your customers prefer it. Either way it is a recurring engagement with a discovery phase, a monthly report, and a control the customer cannot get from their own tooling.
The customer-facing promise“We discovered where AI can access sensitive data, act with privilege, and operate outside defined authority. We now continuously govern that activity and provide monthly evidence reports.”
What your account manager says. What the evidence report proves.
New agents, MCP servers, browser AI tools, and privileged integrations since last month. What appeared, what gained write access, what the customer did not know about.
Actions allowed, modified, escalated, and denied, by category and by system. The customer sees what their AI tried to do and what stopped it.
Signed Decision Traces for every escalation and denial. The customer, their auditor, or their insurer can verify them at verify.behavry.ai with no Behavry account and no help from you.
Pricing motion/Discover and Assess as a paid engagement/Protect per customer per month/Command included for the MSSP
30-Day AI Technician Safety Pilot.
Scoped to one customer environment and three workflows, so you can measure labor saved, risk held, and evidence produced before you commit anything to a contract.
- 1 PSA integrationConnectWise, Autotask, HaloPSA, or your ticket source
- 1 Microsoft 365 / identity environmentOne customer tenant, via your existing partner access
- 1 RMM or security toolYour RMM, EDR, or both, on the same customer
- 3 controlled workflowsChosen from the list, or your own runbooks
- Weekly labor, risk, and evidence reportTickets resolved by AI, hours displaced, actions held, traces produced
- Allow, escalate, deny policy modelBuilt with your service desk lead in week one
- Decision Trace evidence for every actionSigned, hash-chained, verifiable by the customer
| Workflow | Systems | Default policy |
|---|---|---|
| Password reset | PSA → Entra ID | ALLOW within ticket scope, requester identity verified against the directory |
| Stale account disablement | Entra ID, PSA | ALLOW after inactivity check. ESCALATE if the account holds a privileged role |
| M365 group or license change | M365 admin, Entra ID | MODIFY to the ticket's named user. ESCALATE on admin groups or license removal |
| Phishing ticket triage | Mailbox, EDR, PSA | 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 |
Bring the customer you would least like to explain an AI incident to. That is the right pilot.
Related: decision trace · MCP governance · integrations · trust center
If removing Behavry means turning off your AI technicians, it is doing its job.
The control layer is what makes the labor savings safe to keep. Take it out and the authority problem comes back.