Kastra
Kastra is the authorization layer for AI systems, deciding what agents, models, and AI tools are allowed to do before they do it. It sits in the execution path of every prompt, tool call, shell command, database operation, and API request, evaluates each action against deterministic, attribute-based policy, and returns an allow, deny, redact, or escalate decision in under a millisecond. Unlike monitoring products that observe AI after it acts, Kastra blocks unauthorized behavior before it reaches a tool, API, database, or production system. Its unified control plane combines a policy engine, edge decision points, integrations, and a tamper-evident evidence vault that signs every decision for audit and replay. Kastra Edge brings local enforcement to developer machines, protecting Claude Code, Cursor, Codex CLI, and other coding agents from destructive commands, secret exfiltration, unsafe file writes, and unauthorized tool use.
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Trase
Trase is a governed AI platform for healthcare, government, and enterprise environments where trust, security, sovereignty, and predictability are non-negotiable. It provides a powerful foundation for deploying AI agents across real workflows, with hundreds of specialized agents ready to run in production and the infrastructure needed to keep every workflow, decision, and escalation under control. Trase Origin is the operating system agents run on, built to orchestrate, secure, and govern agents across cloud, on-premises, VPC, and edge environments while keeping data where it lives. Trase and third-party agents operate under one control plane with shared policy enforcement, monitoring, cost controls, escalation paths, and a full, immutable audit trail. It supports HIPAA- and SOC2-compliant deployment, data residency, privacy, model flexibility, and no vendor lock-in.
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Preloop
Preloop is the open source AI agent control plane for agents that take real actions. It combines an MCP firewall for tool access, an AI model gateway for cost, safety, and attribution, policy-as-code with human approvals, runtime session observability, and audit trails in a single self-hostable platform. AI agents can deploy code, change infrastructure, move money, touch production data, and burn model spend in seconds, so Preloop helps teams control what agents can do, how much they spend, and which actions require human approval. It works with OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any MCP-compatible agent or managed runtime. Access rules can inspect arguments and context, not just tool names, with CEL expressions for fine-grained conditions. Teams can start with observability, then layer in approvals and deny rules without SDKs or invasive app changes.
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Barndoor.ai
Barndoor is a data and access management layer designed to secure how artificial intelligence systems interact with enterprise data and infrastructure. It acts as a centralized control plane that governs AI agents and applications, allowing organizations to define policies, enforce access rules automatically, and maintain full visibility over how AI tools operate across business systems. Instead of relying only on traditional identity-based permissions, Barndoor introduces context-aware governance, enabling administrators to control what actions an AI agent can perform based on factors such as the user operating the agent, the system being accessed, the type of data involved, and the specific task being attempted. It evaluates every AI request in real time and enforces policies before an action is executed, preventing unsafe or unauthorized operations from reaching internal systems or modifying sensitive information.
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