Most teams start agent tooling with the wrong trust boundary. The model plans, the framework emits a tool call, and some thin executor turns that into a real API request. It works in demos. It also...
Engineering teams need a practical way to track AI security posture beyond one-off audits. A scorecard helps convert broad goals into measurable, reviewable controls. The best scorecards use a sma...
A SOC co-pilot can accelerate triage by summarizing alerts, suggesting pivots, and drafting investigation notes. The failure mode is over-trust: analysts may act on confident but incorrect or over-...
RAG systems tend to accumulate data indefinitely because storage is cheap and retrieval quality improves with volume. But unlimited retention increases breach impact and regulatory exposure. Lifec...
Security automation drift is a common root cause in incident postmortems. Workflows are edited quickly to fix urgent issues, then the change path is forgotten. Provenance discipline gives you acco...
Security quality in LLM apps degrades when teams rely on manual spot checks. Prompt and retrieval changes can silently reintroduce previously fixed weaknesses. Eval-driven testing gives repeatable...
AI workflows fail in new ways: unsafe recommendations, policy bypass, silent retrieval drift, and runaway automation loops. Traditional IR playbooks usually lack steps for these patterns. You need...
As agentic architectures grow, tool servers become high-value control points. They translate model intent into real operations across tickets, infra, and data systems. Security posture depends on ...
API key theft in AI platforms can be expensive and stealthy. Attackers can run high-volume inference, generate prohibited content, or probe internal prompts using your billing and trust context. P...
PII exposure often occurs in intermediate systems, not final answers. Prompts, embeddings, and logs can all carry sensitive fields that were never meant for model processing. Redaction should happ...