How to tier AI systems by risk: high, medium and low
Sort AI systems into risk tiers using data sensitivity, impact on people, autonomy and reach, so reviews match the real stakes of each system.
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.
Sort AI systems into risk tiers using data sensitivity, impact on people, autonomy and reach, so reviews match the real stakes of each system.
A pre launch test plan for a customer facing chatbot: gold answer sets, repeat runs, adversarial prompts and a takedown rule, drawn from reported failures.
NIST's AI RMF does not prescribe risk tolerance. Here is a practical way to write one: levels, examples, limits, and who signs off on exceptions.
A code freeze for an AI agent must be enforced by permissions, not a prompt. Five controls to set up before the agent touches a shared system.
An AI system inventory is the base of AI governance. A one-week plan: scope, sources to scan, fields to record, an owner, and how to keep it current.
A decision guide to AI autonomy in security operations: from read-only summaries to supervised response, and why risky actions should wait for a person.
White-on-white text and other hidden content can carry instructions into a RAG index. How to extract text safely and validate documents before indexing.
Models read text people cannot see, like white-on-white resume text and invisible Unicode. How to extract, normalise and inspect input before the model.
Use the SDK when you own the MCP server code, the proxy when you do not. Both enforce the same policy. Components, trade offs and the current SDK status.
Weekly insights on software supply chain security, delivered to your inbox.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.