What do AI and machine learning actually do inside a SIEM?
In a SIEM, ML mostly scores unusual behaviour and groups related alerts, while language models summarize them. What each does well, and where it misleads.
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.
In a SIEM, ML mostly scores unusual behaviour and groups related alerts, while language models summarize them. What each does well, and where it misleads.
AI agents act with real permissions on text they cannot reliably separate from instructions. See the OWASP root causes and the controls that limit damage.
Give each AI agent its own identity, only the tools it needs, short lived credentials and human approval for risky actions. A checklist for engineers.
Ten ways AI agents get misused, taken from the OWASP agentic list, with a review question for each. Use it to find which paths your agents leave open.
AI agents now run on laptops and servers with real permissions. See what agentic endpoint security covers, the main risks and the controls to set first.
An agentic browser lets an AI model read pages and act for you. The core risk is prompt injection from web content, so limit access and require approval.
Generative AI produces content when asked. Agentic AI plans and acts through tools. That moves the risk from bad text to bad actions. Know the controls.
Agents act, not just answer, so security shifts to identity, tool access and approvals. Here are the controls to ask any agentic AI security product about.
A2A is an open standard for AI agents from different vendors to find each other and share tasks. Learn Agent Cards, auth rules and what to verify.
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