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.
The top AI cybersecurity companies of 2026 span three distinct plays — AI-assisted detection, AI-native SOC automation, and AI-augmented AppSec — and the distinction matters when you're evaluating vendors.
Running a docker mcp server puts an AI agent's tool access inside a container boundary, which helps containment but doesn't remove the need to scope credentials and network access carefully.
AI/ML pipelines introduce unique supply chain risks from training data to model distribution. Most organizations have zero visibility into this attack surface.
Griffin AI reports 98-100% hold rate against adversarial probes. Most Mythos-class tools have never published an adversarial number at all.
MCP, Skills, hooks, and rules extend AI coding agents in very different ways — two execute code, two only steer behavior. Here's how each one breaks.
The explosion of AI tools like ChatGPT is reshaping how developers write code — and introducing new supply chain risks that most teams aren't thinking about.
Auto-remediation uses AI to generate and PR real fixes for vulnerabilities — not just flag them. Here's how it decides what's safe to auto-fix.
Unsafe deserialization looks obvious on a slide and impossible on a real codebase. Sinks are language-specific, gadgets live in third-party libraries, and the tainted byte can arrive wrapped in six layers of framework ceremony. Griffin's engine-plus-LLM design handles each of those concerns separately; Mythos-style pure-LLM scanners blur them into pattern-matching.
AI agents are consuming APIs, installing packages, and executing code autonomously. The security implications are massive and largely unaddressed.
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