Security infrastructure for autonomous AI agents.
The crisis of the future is not intelligence. It is self-authorization.
Prevent unauthorized agent actions, enforce policy before execution, and produce audit-ready evidence.
Every governed action leaves evidence behind
ProvnAI replaces blind trust with inspectable records: what the agent tried to do, who authorized it, which policy applied, and how the evidence was preserved.
Policy enforced
The action is checked outside the model before it reaches privileged tools.
Identity bound
The agent, session, and approving principal are attached to the record.
Tamper evident
Each event is linked into a witness trail that can be verified later.
Security
dimensions
Start with McpVanguard for immediate protection, or design long-term trust architecture with VEX Protocol. Two approaches to the same problem.
McpVanguard
An open-source security layer for AI agent tool calls. Block injection, unauthorized access, and unsafe behavior before agents reach production systems.
VEX Protocol
Review and approve high-risk agent actions before they execute. Every decision is recorded with tamper-evident evidence your auditors can verify independently.
Why governed
execution matters
AI agents now execute code, call APIs, and make decisions. The infrastructure granting them this power was built for humans. The security model was not updated.
Execution must be inspectable.
If an AI agent can modify files, call APIs, or execute code, then its actions must be observable and reviewable. Not just in logs, but in a format that security and compliance teams can reason about.
Controls must sit outside the model.
Relying on the model to enforce its own constraints is a single point of failure. Policy should be enforced in a layer the model cannot rewrite, negotiate, or bypass through prompt engineering.
Identity should not be implicit.
When agents take action on behalf of users or systems, the chain of attribution must be explicit and verifiable. Identity should be explicit, and where needed can be rooted in hardware-backed or protocol-level trust, not inferred from session context.
Governance is infrastructure, not an afterthought.
Evidence, audit trails, and enforcement boundaries should be built into the architecture from the beginning. Retrofitting them onto systems designed for speed alone is expensive and usually incomplete.
Choose the path that matches your rollout
Start with practical security controls, threat analysis, or governed evidence depending on where your agent program is today.
For AI Developers
Block unsafe tool calls before they execute. Ship agent features faster without security review bottlenecks.
For Security Teams
Catch injection, exfiltration, and unauthorized access before agents reach production. Reduce incident response time from days to minutes.
For Governance Teams
Generate tamper-evident audit trails automatically. Pass compliance reviews without manual evidence collection or spreadsheet archaeology.
Secure your
autonomous AI agents
We are accepting a limited number of design partners and pilot deployments for secure agent execution. Tell us what you are building.
Enterprise & Startup Pilots
Deploy controlled execution for your agent stack. We work directly with your security and platform teams.
Public-Sector & Consortium
Standard-setting conversations for secure AI in regulated environments. EU AI Act, DORA, SOC2 alignment.
Architecture Collaboration
Formal verification, ZK proof systems, and protocol-level trust. We support open academic and institutional collaboration.
We read every submission and reply within two business days.