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.
How attackers hide malicious instructions inside webpages, documents, and retrieved content to hijack AI systems — and why RAG pipelines are especially exposed.
A vendor-by-vendor comparison of LLM firewall and AI guardrail platform options for enterprise deployment, with real strengths and limitations for each.
RAG poisoning attacks corrupt the external knowledge base an LLM retrieves from, turning trusted documents into vectors for misinformation and data leaks.
Vector databases now hold copies of your most sensitive data with weaker controls than the systems they came from. Here's what to fix before your next RAG deployment.
Model extraction attacks let adversaries clone proprietary AI models through ordinary API queries alone. Here's how the attacks work, why they evade detection, and how to stop them.
Membership inference attacks let adversaries confirm if your data trained a model, exposing privacy leakage in ML models and training data inference risks.
Shadow AI risk is spreading faster than governance can keep up. Here's what unsanctioned AI use looks like inside real enterprises and how to discover it before data leaks.
A glossary of AI trust risk security management concepts: the Gartner AI TRiSM framework, its four pillars, AI risk taxonomy, and adversarial threats.
A step-by-step guide to building an AI incident response plan — covering scoping, escalation, detection, containment, and post-incident review for LLM and agent failures.
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