RAG teams often ship before adversarial testing because they assume retrieval limits risk. In practice, retrieval creates new abuse paths that classic web app tests do not cover. A home-lab red te...

Phishing queues are ideal automation candidates: repetitive, high volume, and rich in semi-structured text. n8n plus an LLM can classify, enrich, and prioritize reports quickly. The key is guardra...

Self-hosted AI gives control, but it also shifts supply chain risk onto your team. Model weights, tokenizer files, runtime libraries, and serving containers all become trust decisions. Treat model...

Role-based controls are often too coarse for retrieval systems. Two users with the same role may still require different document visibility based on region, project, or data classification. Attri...

Prompt text and automation logic now carry business risk similar to application code. Yet many teams still manage them through ad hoc edits in UI tools with minimal review. Treating prompts and n8...

RAG exfiltration often looks like normal usage until it is too late. Attackers can ask repeated reformulated questions that gradually reconstruct sensitive data. Canary markers give you early warn...

Most AI incidents are hard to investigate because telemetry is fragmented. Teams log prompts but not retrieval IDs, or they store tool outputs without caller identity. After an event, there is no c...

Tool-enabled agents can execute real actions: create users, close tickets, rotate credentials, or query sensitive systems. That is powerful, and it turns prompt quality into an authorization proble...

Automation should remove toil, not remove judgment. In security workflows, fully autonomous actions can lock out executives, delete evidence, or block production services if detection quality drops...

Multi-tenant RAG architectures often fail at one point: retrieval isolation. Teams enforce auth on the API layer but forget that nearest-neighbor search can still surface embeddings from another te...