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 NIST AI RMF is voluntary guidance, not a certification. A VP of engineering should decide who owns AI risk, which systems are in scope and what evidence to keep.
The NIST AI RMF is voluntary guidance with four functions. A DevOps lead can wire them into pipelines: inventory, eval gates, monitoring and rollback.
The NIST AI RMF is a voluntary guide, not an audit. A first security hire can use it to start an AI register, set owners and write down which risks matter.
A neural network is layers of weighted sums that learn from data. Attackers can poison data, fool inputs or extract the model, so plan defences for each.
Design MCP tool permissions so a confused or hijacked agent can do little harm. Split tools by risk, scope tokens, require approval and log every call.
A practical MCP server threat model: confused deputy, token passthrough, SSRF, session hijacking and local server risk, plus a checklist to review each.
MCP, the Model Context Protocol, lets AI apps call tools and read data through servers. Answers to common questions, plus the security mistakes to avoid.
Enterprise MCP adoption stalls on identity, tool permissions, visibility and prompt injection. Here are the hard problems and practical controls for each.
MCP connects AI apps to tools through hosts, clients and servers. See how one host runs many one-to-one connections, and where to put security checks.
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