Security in the traffic path
AI security should sit where decisions happen: between the application, retrieved context, tools, and model providers.
We are building the security layer for production AI.
Koreshield protects AI applications at runtime: prompts, retrieved context, provider calls, policy decisions, and the evidence teams need when security questions become business questions.
Built inUnited Kingdom
FocusProduction AI security
TeamProduct, engineering, GRC
ModelRuntime boundary
Operating thesis
AI security has to move from policy documents into the request path.
LLM applications do not fail like normal software. A model can obey a malicious instruction hidden in a document, leak sensitive data through a helpful answer, or call a tool under false context.
Koreshield is built as a runtime layer because the most important security decisions happen while traffic is moving, not after an incident review.
Privacy by default
Prompts, responses, and evidence should be handled with minimisation and clear customer-controlled retention choices.
Evidence over promises
Teams need audit logs, policy decisions, and repeatable checks they can show to security, compliance, and legal reviewers.
Team
A small team with clear ownership.
Product, engineering, and AI governance ownership in one working team.
Teslim O. Kazeem
CEO & Product LeadLeads product direction, customer discovery, and the commercial path for Koreshield.
LinkedIn
Isaac Emmanuel
CTO & Engineering LeadArchitects the platform, SDKs, proxy layer, and operational infrastructure behind Koreshield.
LinkedIn
Uwagba Obinna
AI Risk & GRC LeadShapes governance, risk, and compliance workflows so AI security evidence is usable by real organisations.
LinkedInCommercially focused. Technically serious.
Contact Koreshield