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.
Auto-instrumentation adds tracing to AI apps without code changes. Learn what OpenTelemetry records for model calls and why prompt content needs opt-in.
A compound AI system chains models with retrievers, tools and filters. Secure it by treating each hand-off as a trust boundary and limiting tool access.
When AI tools let anyone ship software, security must move from training each author to guardrails: secure defaults, review gates and clear ownership.
Agentic AI acts without per-step approval, so permissions, identity and blast radius matter more than prompts. Here is how security teams should respond.
A package hallucination is an AI suggestion for a dependency that does not exist. Verify each new package before install, since attackers can register it.
AI evals are repeatable tests that score a model or agent on tasks you care about. Build them from real cases, score them, rerun them on every change.
Vibe coding speeds up prototypes but ships code nobody read. Research shows AI assistants can raise insecure output and false confidence. Add guardrails.
Yes, if you treat report text as untrusted input, limit what the model can do, keep a human on decisions and log everything. A checklist for engineering teams.
Generative AI can speed triage, review and fixes in DevSecOps, but its output is untrusted input. Add human review, least privilege and tests first.
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