What is observability in AI models and what should you log?
AI observability means recording prompts, retrieval, tool calls, versions and costs so you can explain and audit behavior. Log traces with care.
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 observability means recording prompts, retrieval, tool calls, versions and costs so you can explain and audit behavior. Log traces with care.
Expose read-only data to an LLM through MCP resources: pick URIs, declare the capability, validate every read, and keep scopes narrow. Steps for Node.js.
The NIST AI RMF is voluntary guidance: Govern, Map, Measure, Manage. Product managers decide scope, owners, risk thresholds and release gates.
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.
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