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.
Enforce access on the retrieval path, not just at the app. Filter by tenant and permission at query time, separate indexes for sensitive data and log what was retrieved.
Structured outputs make an agent's reply parseable, but a valid shape can still carry a harmful value. Validate content and limit tool permissions too.
Set retention for AI chat prompts and replies by purpose, check vendor terms, and make deletion work end to end. A practical checklist for engineers.
Context engineering is choosing what a model sees each turn. Every item in context can also steer the model, so curate sources and limit agent permissions.
MCP servers that pass tool arguments to a shell can let a prompt or a crafted input run commands. Learn the pattern, the risk and defensive coding fixes.
AI chat assistants speed up coding but can produce insecure code and leak data. See what research shows, which OWASP risks apply and how to review output.
Chat memory manipulation plants false or hostile notes in an assistant's saved memory so they steer later answers. Learn how it works and how to limit it.
Yes, if an app runs model-written SQL unchecked. Use parameterized queries, a read-only database role, allowlists, and treat model output as untrusted.
Yes. Text a person cannot see can still be read by a model and change its output. Learn how indirect prompt injection works and how to harden pipelines.
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