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.
Watermarking and provenance are the two most confused terms in AI security. A practical breakdown of what each actually does, where the 2025 techniques break, and what to ship in the meantime.
Fine-tuning corpora are supply chain artifacts. We cover the provenance signals, attestations, and drift controls enterprises need before pushing weights to prod.
Retrieval-augmented generation is the most common LLM deployment pattern in the enterprise and the most commonly poisoned. A senior security engineer's playbook for defences that hold up in production.
Embedding models are the silent dependency under every RAG system. We cover poisoning, deprecation, and provenance gaps that break retrieval in production.
Persistent memory makes AI agents more useful and more dangerous. A security engineer's walkthrough of how agent memory gets poisoned, exfiltrated, and weaponised, with concrete 2025 examples.
Vector stores hold derivatives of your most sensitive text. We cover the access, isolation, and integrity controls production deployments of Pinecone and Weaviate need.
We field-tested five GenAI code review tools against 240 seeded security defects to see which catch real issues and which hallucinate findings.
AI and ML pipelines introduce unique supply chain risks -- from poisoned training data to compromised model registries. Here is what attackers are targeting and how to defend.
Running an open-weight model inside an enterprise perimeter seems safer than calling a hosted API. It is, and it isn't. The sandboxing patterns that actually produce the safety properties.
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