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.
Container security scanning tools find vulnerabilities in your images, layers, and dependencies before they reach production. Here is how the categories compare and what to look for.
AI explainability is the ability to understand why a model produced a given output. In security, it is the difference between an alert you can act on and one you cannot.
AI agents can now open pull requests that patch vulnerabilities on their own. Without guardrails — scoped permissions, test gates, human merge approval — they can also break builds and introduce new flaws at machine speed.
AI accelerators are the specialized chips that make model training and inference fast, and they bring their own attack surface: memory leakage, driver stacks, and firmware you did not write. Here is what to secure.
The OWASP AI Top 10 is the community list of the most critical security risks in LLM applications, led by prompt injection. Here is what each entry means and how to defend against it.
AI-powered cybersecurity means using machine learning and language models to detect, triage, and remediate threats faster than rule-based tooling alone. Here is where it genuinely helps and where the hype outruns reality.
ReversingLabs disclosed two malicious Hugging Face models that evaded Picklescan by using broken 7z-packed PyTorch archives. We unpack the technique.
Open-weight models give you total deployment control. They also give you a new supply chain to secure. The tradeoff is worth being explicit about.
The honest answer to "when does this pay back?" is where sales decks and procurement reality diverge. Griffin AI and Mythos-class tools have different ROI shapes.
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