AIDR metrics that matter, and the ones that mislead
Which metrics tell you whether AI detection and response works, and which ones flatter you, such as blocked prompt counts. Measure outcomes, not activity.
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.
Which metrics tell you whether AI detection and response works, and which ones flatter you, such as blocked prompt counts. Measure outcomes, not activity.
A practical maturity model for AI detection and response, from an inventory of AI systems to tested, human approved response, with metrics at each stage.
What drives AI detection and response cost, which numbers to collect and what to ask vendors, so you can budget from your own volumes, not invented prices.
Six mistakes teams make choosing how a security tool uses models, from treating Zero AI as second class to skipping provider terms, plus a decision list.
Choose the AI mode by what data the model sees, who runs it and what it may do. A checklist for tools that offer Zero AI, your own model or full AI.
When AI agents direct warehouse robots, a software flaw can become a physical event. A sourced look at what changes in the threat model and where to start.
OWASP LLM10 covers runaway LLM cost, denial of service and model extraction. A checklist of limits, quotas, monitoring and access controls to put in place.
A defensive plan to test OWASP LLM10 unbounded consumption: limits on input, output, rate, cost and queues, plus alerts and graceful degradation.
A defensive test plan for OWASP LLM01 prompt injection: map what the model can touch, test direct and indirect paths, and check least privilege.
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