AI-agent security and control infrastructure for enterprises
Protect enterprise systems from unknown cyber attackers and autonomous AI agents by combining perimeter controls, anomaly detection, identity management, permissions, monitoring, and intervention mechanisms.
online B2B Deep Tech / Infrastructure
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Problem: AI models can find vulnerabilities, exploit misconfigurations, access connected data, and change core code faster than traditional security teams can respond. Agents may also take actions without clearly defined identities, permissions, or safeguards.
For: Enterprises deploying AI agents, LLMs, open-source software, and connected cloud or infrastructure systems.
Products from this video
Claude Fable orchestrator Fireworks Google Drive Model Context Protocol (MCP)
Examples
- Palo Alto Networks: analyzes enterprise data for anomalous behavior, ingests 19 petabytes per day, and had 1,200 customers that had bought and deployed the described capability.
Other takes on AI security infrastructure
- Agentic consent governance layer for autonomous AI systems
- Enterprise agentic last-mile identity and access control layer
- Identity-based security infrastructure for multi-agent AI systems
- A guardrail tool that enforces strict test-driven development and other engineering rules for AI coding agents
- AI-powered scam-interception and threat-intelligence service
- Secure compiler for AI-agent skills