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.
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.
The Coalition for Secure AI published two operational frameworks in November 2025: Signing ML Artifacts and AI Incident Response. We unpack what each contains and how to adopt them.
Indirect prompt injection arrives through your retrieval corpus, not your chat box. We cover the detection strategies that survive when attackers write your RAG content.
Anthropic released Claude Opus 4.5 on November 24, 2025 with the most detailed safety section of any system card to date. We pull out what enterprise defenders should change.
vLLM 0.10.2-0.11.0 deserialized user-supplied PyTorch tensors via torch.load() in the Completions API. Memory corruption, potential RCE.
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.
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