Why we built Koreshield
The product changed, but the core concern did not: untrusted language should not quietly become trusted behaviour.
Natural language became an execution surface
Traditional application security still matters, but AI workflows add a different failure mode. A syntactically valid document, message, or tool result can change model behaviour without exploiting memory corruption or bypassing a login screen.
The problem is most visible in support systems: they combine customer messages, internal knowledge, account data, and actions such as refunds or account changes. The workflow is useful because those systems meet. It is risky for the same reason.
What changed in the product
The first Koreshield framing was a transparent firewall proxy in front of model providers. That made the idea easy to describe, but it blurred the actual security boundaries and encouraged claims broader than the implementation could support.
The rebuilt product is narrower and more explicit. It protects three transitions in an AI support workflow: customer input before model context, retrieved content before prompt assembly, and a proposed tool action before execution. Each transition has its own API contract, evidence, and fallback.
What we believe
Security controls should be inspectable. A team should see why a request was flagged, replay the evidence, and choose when to move from observation to enforcement. It should also be clear what Koreshield does not own: identity, tenant isolation, business authorization, and human accountability remain application responsibilities.
Our job is not to claim that prompt injection has been solved. It is to make dangerous trust transitions visible and enforceable enough that teams can operate them deliberately.