Which AI observability metrics matter for security?
Beyond latency and cost, security teams should track tool calls, guardrail blocks, permission denials, retrieval sources and model versions. A starter set.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Beyond latency and cost, security teams should track tool calls, guardrail blocks, permission denials, retrieval sources and model versions. A starter set.
A coding agent that uses your login inherits every permission you have. See the risks, why logs go blind, and how to give agents their own scoped identity.
People vibe code to get working software fast, but speed shifts the review burden. Here is a checklist drawn from NIST SSDF, CISA and OWASP guidance.
The OWASP AI Exchange is an open guide to AI security threats and controls. Use it to threat model AI systems and to pick controls by lifecycle stage.
An AI agent's attack surface is its inputs, tools, memory and credentials. Map each one, cut permissions to the task, and log every tool call.
MLSecOps adds security checks to the machine learning lifecycle: data, training, model files and serving. See NIST's attack types and a practical start.
MITRE ATLAS is a public knowledge base of adversary tactics and techniques against AI systems, modeled on ATT&CK. How to read the matrix and apply it.
Dark AI is a loose term for AI used or built for harm. Government assessments say it mostly speeds up known attacks. Here is what that means for defenders.
An agent gateway is a control point between AI agents and their tools. It handles identity, permissions, limits and logs. Here is how to design one.
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