AI agent security

Codenotary Expands Free Linux Security to Debian and Ubuntu

Codenotary Expands Free Linux Security to Debian and Ubuntu

Codenotary has expanded its permanent, unrestricted free Linux security tier to Debian and Ubuntu, following 12,000 AlmaLinux registrations in two months. The platform covers vulnerability monitoring, patch management, CIS compliance, and AI agent runtime security. ECI Research data suggests the zero-cost entry point directly addresses procurement barriers that slow security tool adoption across enterprise and public sector environments.

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Codenotary AgentMon 3: Adaptive AI Agent Runtime Security

Codenotary AgentMon 3: Adaptive AI Agent Runtime Security

Codenotary has launched AgentMon 3, an enterprise AI runtime security platform that replaces static allow-lists with adaptive behavioral policies. The platform monitors more than 5 million AI agent interactions daily and is now available on AWS Marketplace. ECI Research analyst commentary explains why behavioral runtime monitoring is becoming a baseline enterprise requirement.

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Detection Latency Budget: Why Blast Radius Is an Integral

Detection Latency Budget: Why Blast Radius Is an Integral

Most enterprises have invested in fast detection sensors while leaving batch correlation and manual runbooks as dominant latency stages. Armo’s five-stage pipeline framework reframes blast radius as a latency integral, not a threshold, with direct implications for how security and platform engineering teams should share ownership of time-to-containment. ECI Research finds the same structural gap appearing across AI operations and FinOps maturity models.

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AI Agent Security: Tetrate and Ory Target Runtime Policy Gaps

AI Agent Security: Tetrate and Ory Target Runtime Policy Gaps

Tetrate and Ory have announced a joint solution that enforces dynamic, granular authorization on AI agent tool calls at the parameter level, built on an Envoy-based gateway. ECI Research examines the identity and governance gap the partnership addresses, and why parameter-level MCP control is becoming a production requirement for enterprise AI deployments.

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