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.
LLM02 covers PII, secrets and business data leaking through model output. See how each mitigation lines up with privacy and security frameworks.
OWASP says no fool-proof prevention is known. Limit what the model can do, validate output and require approval for risky actions. Prompts alone fail.
Fifteen yes or no questions on data origin, access, testing and rollback for engineering leads to answer before shipping a model, based on OWASP LLM04.
Test an AI code security tool on code with known flaws, repeat runs for consistency, compare to a deterministic scanner and check where your code goes.
Guardrails shape what an agent tries, and sandboxes limit what it can do. A checklist for using both, based on OWASP's excessive agency guidance.
AIDR watches AI systems for misuse, manipulation and odd behaviour, then helps you respond. What it covers, what it does not and how to judge a product.
A day by day plan for a two week trial of an AI detection and response tool, with scenarios, success criteria and the evidence to collect before you buy.
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.
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