Are embeddings sensitive data? Embedding inversion explained
Treat embeddings like the data they came from. OWASP warns that attackers can reverse embeddings to recover source information. What that means for you.
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.
Treat embeddings like the data they came from. OWASP warns that attackers can reverse embeddings to recover source information. What that means for you.
Twelve RFP questions that separate real AI capability from marketing: models used, data sent, accuracy evidence, human review, and cost behavior at scale.
Remote approval prompts for AI agents are only a control if you can read them. What the MCP spec and OWASP say and a checklist for small screens.
A short training outline for staff using AI assistants: approved tools, what data stays out, how to check output, and reporting mistakes without blame.
How to run an AI risk assessment on a single system using the NIST AI RMF: context, categories, impacts, tests, treatment and residual risk.
A step by step workflow for using an LLM on employee-reported phishing: what code does, what the model drafts and what a person must decide.
LLMs write smooth incident summaries that can include wrong times, invented causes and missing steps. Use this checklist to verify one before it goes out.
Your incident plan probably misses AI failures. What to add: definitions, intake fields, a kill switch, communication, third-party cases and review.
Who owns what in AI governance? A simple RACI for executive, system owner, security, legal, engineering and reviewers, based on NIST AI RMF accountability.
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