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.
AI coding assistants are now standard developer tooling. The incident data from 2025 and early 2026 shows a recurring pattern of source code, credential, and customer data leaking through them.
Most AI observability stacks log prompts and completions. The actual security signal is in the tool calls. Here is how to capture it.
Fixing a transitive dependency is rarely a single bump. It is a cascade. Here is how to manage those cascades without flooding reviewers or breaking builds.
Anthropic's Model Context Protocol standardizes how AI models interact with external tools. The security implications for software supply chains are significant.
Multi-repo security reasoning is a graph problem, not a retrieval problem. How Griffin AI's engine scales where pure-LLM products flatten into guesswork.
Patterns for managing MCP servers through development, staging, rollout, and deprecation — with an eye on the security gaps that appear at each transition.
A senior engineer's review of academic research on fine-tune backdoor insertion, from BadNets to sleeper agents, and how the findings translate to production ML.
AI cybersecurity companies split into three distinct groups doing very different work, and confusing them is the fastest way to buy the wrong tool.
The difference between an engine-plus-LLM bug hunter and a pure-LLM one is not a tuning detail. It is a structural divide that determines whether the findings are usable.
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