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.
Regulated teams can use AI coding agents if they control where code goes, who approves each action, what gets recorded and which models are allowed.
AI coding agents add dependencies, run commands, send code to models and read untrusted text. Approvals, scans, policies and MCP limits keep that in check.
Approve AI coding agents at three points: risky actions in the session, the diff in review by someone other than the requester, and the merge gate in CI.
Hand an AI agent one browser tab, keep approvals on for forms, sign-ins and downloads, allow only trusted sites, and keep a record of every step it takes.
Assistants and search summaries can reproduce code from public docs, including unsafe samples. How to write examples that stay safe when quoted.
A model can rank and explain findings, but release decisions need rules that behave the same every time. How to pair the two without confusing them.
Models learn from text frozen at a cutoff date. Fresh advisories are missing unless you supply them. How to feed current data into a security prompt.
CVSS measures severity, not risk, and the environment part is yours to fill in. Here is why asking an LLM for one number hides the missing context.
Claude custom connectors call out from the provider's cloud, so the server must be public or allowlist its IP ranges. What that means for buyers.
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