AI risk management best practices: a lifecycle framework
NIST's AI RMF has four functions and MITRE ATLAS now tracks 84 adversarial techniques — most AI risk programs still only cover one lifecycle stage.
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.
NIST's AI RMF has four functions and MITRE ATLAS now tracks 84 adversarial techniques — most AI risk programs still only cover one lifecycle stage.
GitHub's Copilot Autofix cuts median fix time from 1.5 hours to 28 minutes — but a 2025 Replit agent incident shows why autonomy needs hard limits.
AI agents already research, compare, and recommend software — but the moment they hit a paywall, they stall and hand the job back to a human. Here's why that gap is expensive, and how agent-native purchasing closes it.
The OWASP LLM Top 10 is the closest thing the field has to a shared checklist for AI security. Here is what each of the ten risks actually means, in plain language, with the defenses that matter.
MCP is the USB-C of AI integrations — one open standard for connecting models to tools and data. It also standardizes a fresh attack surface, so understanding both halves matters.
You cannot secure an LLM application by reading its code alone. You have to attack it the way an adversary will — with language, with poisoned content, and with the goal of making it do something it should not. Here is how to run an AI red team.
The biggest MCP protocol news of 2026 is a major specification overhaul that fixes old flaws and shifts a load of new security responsibility onto developers.
AI agent skill marketplaces run installed code with your full permissions and no sandboxing — VS Code's 2025 extension attacks show exactly how that gets abused.
Copilot suggested 2,702 hardcoded secrets from just 900 prompts in one study, and at least 200 were live credentials — adoption without policy is a leak waiting to happen.
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