Production LLM Security for CISOs (September 2026)

LLM applications create security challenges that differ from traditional software because their behavior is shaped at inference time by prompts, retrieved content, model behavior, and user input, making attack surfaces dynamic and difficult to fully define in advance. The discussion identifies prompt injection, especially indirect injection through retrieval-augmented generation (RAG) content, as a leading threat, alongside PII disclosure, poisoned knowledge bases, vector-store exposure, excessive agent autonomy, unauthorized tool calls, misinformation, and uncontrolled resource use. It recommends using the OWASP LLM Top 10 and complementary agentic guidance for threat modeling, while applying layered controls across ingestion, retrieval, generation, tool invocation, and API boundaries. It argues that logging and alerting provide evidence after an incident but do not prevent harmful outputs or actions, whereas real-time blocking, redaction, allowlists, and human-review escalation are more appropriate for systems handling regulated data or agentic write access. The piece also emphasizes that incomplete AI inventories and shadow AI can undermine every other control, advocating centralized gateways, traffic discovery, per-request authorization, detailed audit records, cross-functional governance, and continuous behavioral evaluation. It connects these practices to EU AI Act obligations, NIST AI RMF, and ISO 42001, and presents Openlayer as a platform offering runtime enforcement, asset discovery, automated evaluations, and audit-ready compliance evidence.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.