Koreshield has launched a runtime security and evidence proxy to protect AI support agents from prompt injections, untrusted RAG retrievals, and unauthorized tool calls.
Koreshield is a runtime trust and security gateway designed specifically for AI-powered customer support workflows. Because autonomous support agents routinely process inputs from untrusted sources—including user messages, external knowledge base documents, and proposed tool calls—Koreshield inspects all three boundaries before actions become trusted model execution. Integrated via a single API call or Python SDK, it screens against data leaks, hidden instructions in help documents, policy drift, and unsafe agent actions while retaining an evidence trail for every decision. The platform emphasizes a detect-first posture, allowing teams to monitor live traffic and tune false positives before enforcing active policy blocks.
AI support agents with tool-calling capabilities represent high-risk attack surfaces where indirect prompt injections can easily lead to data exfiltration or unauthorized system changes.
- –Multi-boundary inspection: Securing inbound customer messages, retrieved RAG context, and proposed tool invocations addresses the full agent attack surface rather than relying solely on fragile system prompt instructions.
- –Detect-before-enforce posture: Allowing operators to run in passive detection mode alongside live traffic prevents unexpected support disruptions while building confidence in policy rules.
- –Auditability and evidence retention: Retaining decision logs and traces for every inspection satisfies enterprise compliance needs and aids debugging when agents behave unexpectedly.
- –Pragmatic boundary limitations: The system clearly delineates its scope, serving as a runtime safety layer rather than a replacement for backend IAM systems or complex file/image scanners.
DISCOVERED
2h ago
2026-09-23
PUBLISHED
8h ago
2026-09-23
RELEVANCE
AUTHOR
[REDACTED]