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.
Hacking AI is not science fiction; it is a growing set of concrete techniques that exploit how models learn, process input, and produce output. Here is how to think about defending against them.
A container security assessment reviews your images, registries, orchestration, and runtime against known weaknesses so you can fix them before an attacker finds them.
Claude and GPT both write vulnerable code by default in coding harnesses. Comparing model behavior, then Safeguard's approach versus Endor Labs' reachability-first SCA.
AI based cybersecurity tools range from genuinely useful triage assistants to thin wrappers around a generic model, and the difference is usually visible in how the tool handles context, not in its marketing.
The Model Context Protocol connects AI models to tools and data through a client-server design. Understanding that architecture is the first step to securing it.
Container security on AWS spans the image, the registry, the runtime, and IAM. Get those four layers right and you close the gaps that cause almost every incident.
An AI security app uses machine learning to detect, prioritize, or remediate security issues. Here is what the category actually delivers and how to judge one.
An AI cybersecurity company uses machine learning to detect, prioritize, and remediate threats faster than rules alone. Here is how to evaluate one honestly.
A container image audit inspects every layer of an image - base OS packages, application dependencies, secrets, and configuration - to find what an attacker could exploit before you deploy.
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