MahaGuardian builds the information wall around the agent itself — not around the prompt, not around the server. Built for M&A advisors, PE firms, family offices, and law firms whose engagements demand absolute matter isolation.
Every firm using AI agents in client work knows there is a moment when the agent gets it wrong — and there is no good answer for the call that follows.
A senior associate asks the AI agent to draft a comparable-deal memo. The agent has access to the firm's prior M&A files, including a deal for a competitor of the current client. The memo includes a phrase that could only have come from that other file. The associate doesn't notice. The partner doesn't notice. The other client does.
A wealth advisor's AI summarises a family's investment thesis ahead of a meeting. The summary cites recent positions the advisor took for a different family — positions that were never disclosed to this family, and never should have been. The other family is in the room.
A PE associate runs diligence using the firm's AI. The agent reads the data room and, helpfully, cross-references findings against deals the firm has done before. One of those prior deals is under NDA with a counterparty. The reference appears in the IC memo. The firm finds out when the counterparty's GC calls.
Every advisor in these scenarios is using a model whose vendor promises it will not leak across matters. The vendor's promise is a policy. MahaGuardian replaces policy with software that — by construction, when correctly deployed — cannot leak across matters through Guardian-controlled credentials.
MahaGuardian is built around three properties that map directly onto how your firm already thinks about confidentiality.
Every agent in your practice carries a credential that opens exactly one matter's information. There is no key on that credential for any other matter. An agent that should not be able to read another client's file is not asked to be careful. It is given a key that does not unlock that file — through the enforced retrieval boundary MahaGuardian controls.
Every retrieval the agent performs through MahaGuardian is recorded in a tamper-evident, hash-chained log. Because each agent operates under its own credential — distinct from the staff member's — its actions appear in a dedicated agent audit trail, not interleaved with the staff member's own activity. If a client's GC ever asks "show me everywhere the AI touched our data," the firm has a complete answer covering all Guardian-mediated retrievals and approvals. The log lives on your firm's infrastructure, not the vendor's.
For actions the firm has flagged as sensitive — moving money, sending external communications, sharing privileged material — the agent must request human approval before proceeding. The request and the approving partner are both recorded. The agent cannot route around the gate through the Guardian-controlled boundary.
Electric Mobility Co. X is raising a Series B. The founder hires one advisor to coordinate the round — fundraising plus legal, with an external counsel brought in for the legal structure. Both the coordinating advisor and the external counsel use AI agents in their work. The coordinating advisor also has other clients.
| Unit Economics | Legal Structure | Partner Pipeline | |
|---|---|---|---|
| Coordinating advisor's agent (Electric Mobility) | Allow | Allow | Allow |
| External legal counsel's agent | Deny | Allow | Elevate |
| Coordinating advisor's agent (other client) | Deny | Deny | Deny |
Three states. Allow means the agent's credential opens that information automatically. Deny means the credential does not open it — there is no path through the Guardian-controlled boundary. Elevate means the agent's request falls outside its normal scope but may be legitimately needed: it must request approval from a named human before proceeding; both the request and the approval are recorded.
The third row is the test. Rows one and three share the same human — same advisor, same desk, same login. What changes is the engagement. The agent assigned to Electric Mobility Co. X has full access to its data. The agent assigned to the advisor's other client has none. Without enforcement, an agent active in one engagement could quietly summarise data from the other. With MahaGuardian, the other-client agent is given a credential that does not unlock any of Electric Mobility Co. X's data. Same human, two engagements, two physically separate credentials.
AI agents are becoming operational actors — they access systems, invoke tools, coordinate with other agents, and increasingly operate autonomously across institutional boundaries. Current architectures lack enforceable trust boundaries for machine authority. The governance problem is not that agents are dangerous; it is that agents inherit broad access, and no layer exists to constrain what they can reach within a specific task.
MahaGuardian separates authority from execution. Agents do not possess authority — they receive it, scoped and time-bounded, from an external governance layer that they cannot modify or self-escalate. This is the same architectural principle that makes multi-agent coordination safe across institutions: bounded autonomy, externally verifiable, cryptographically enforced. The enterprise deployment is the first implementation context. The architecture generalises wherever autonomous systems require governable delegation.
The shape of the problem differs by practice. The shape of the solution does not.
Multiple live mandates at any moment. One careless cross-reference between engagements — even an indirect one — can cost a relationship, a fee, or worse. AI agents need the same walls your senior advisors operate under.
Diligence on Deal A while another team is mid-process on Deal B. The AI cannot be the path by which confidential information from one deal informs another, however subtly.
Multiple principals, sometimes related, sometimes in conflict. The agent that helps one family member cannot reveal — even by inference — what the same office is doing for another.
Bar ethics rules are categorical: no cross-matter disclosure, ever. AI agents have to honour the same wall the partners do, and the firm has to be able to prove it.
You run the security program and you know the gap. AI agents touching JV data rooms, partner integrations, and supply-chain counterparty information cannot operate on shared credentials. You want an architectural primitive that enforces those walls — and that your auditors can verify cryptographically.
MahaGuardian is the governance layer for AI agents in your practice. We are not the AI model, not the email server, not the document-management system. We sit between the agent and your data, and we ensure the agent gets exactly the keys you have authorised — and never more.
We don't rely on prompt defences alone. Prompt injection is an unsolved flaw in all large language models. MahaGuardian limits what a compromised agent can reach: if an injected instruction tells the agent to retrieve another client's file, the credential the agent holds does not open that file through the Guardian-controlled boundary. The attack fails at the architecture layer, even when the prompt is compromised.
V1 runs on a single device — typically the principal's or compliance officer's laptop. This fits firms operating up to a few dozen agents. Larger and always-on deployments are V2 work, in development for late 2026.
Customer matter data does not transit our servers. The architecture is built so it cannot — your credentials, your keys, and your audit logs stay on your firm's infrastructure.
Microsoft Copilot, SharePoint agents, and similar platforms enforce access through the signed-in user's existing permissions. If one professional has access to Matter A and Matter B, the platform provides strong governance at the tenant, site, and policy level. What current Microsoft documentation does not clearly show is a separate runtime boundary that confines one agent task to Matter A only, even when the same human can also access Matter B.
MahaGuardian is designed for that narrower problem. We issue time-bounded, matter-scoped capability tokens to long-lived agents. The agent identity persists for the duration of the engagement; the authority it holds for any given task is bounded to one matter and expires. An agent assigned to Matter A receives a token that does not open Matter B — regardless of what its operator can otherwise reach.
Microsoft governance, DLP, and compliance controls remain in place and continue to do what they do well. MahaGuardian is designed to add a tighter runtime layer inside that broader control environment, not to replace it.
This differentiation rests on real runtime enforcement, not policy alone. The Security Status page maps every claim to a specific test in the open-source repository.
MahaGuardian is moving from architecture and enforcement primitives into a real operator product. We are hiring for three focused roles across runtime, console UX, and security review.
Work on agent execution flow, runtime grounding, deployment-related backend logic, and the boundary between agents, operators, and external actions.
View role details →Improve conversation flow, artifact history, deployment UX, and operator-facing state management in the early MahaGuardian Console.
View role details →Review scoped credentials, trust boundaries, audit integrity, and local control surfaces in a real early codebase for governed AI agents.
View role details →We work directly with our first 100 design partners — firms that want cryptographic information barriers around their AI agents and are willing to shape V2 with us. No cost during the design-partner phase. Direct line to the founder.
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