Koreshield

Security and evidence for AI support agents

Developer ToolsArtificial IntelligenceSecurity

▲ 0 votes4 commentsLaunched Sep 23, 2026

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Daily #16Weekly #84

Every AI support agent takes input from someone it should not trust: the customer message, the documents it retrieves, and the tool calls it proposes. Koreshield screens all three before they become trusted model behavior or application execution. Data leaks, hidden instructions in help articles, policy drift and unsafe agent actions are checked before the model acts, and every decision is recorded. One call to integrate.

AI Analysis

📝 Summary

Koreshield secures AI support agents by screening customer messages, retrieved documents, and proposed tool calls before they influence model behavior or execution. It detects and blocks data leaks, hidden instructions, policy violations, and unsafe actions, while logging every decision for evidence and compliance. Key USP is its comprehensive pre-trust validation across three input types with one-call integration. It solves critical pain points of untrusted inputs leading to breaches or drift in AI customer support systems, delivering safe, auditable AI operations and reducing security risks.

📈 Market Timing

2025-2026 sees explosive growth in AI agents for customer service amid rising AI regulations (e.g. EU AI Act), increasing enterprise adoption, and high-profile AI security incidents. User demand for trustworthy AI is surging while foundational LLM tech has matured enough for specialized security layers. This is a strong window before the market consolidates. Excellent Timing.

✅ Feasibility

Technical implementation involves prompt/document scanning and tool validation using detection models, which is challenging but achievable with current LLM guardrail techniques. One-call integration lowers adoption barriers. Operational costs involve inference but can be optimized. Compliance risks exist around data privacy but are manageable in security-focused product. Strong scalability via API. Overall rating: High, supported by narrow scope and clear developer value.

🎯 Target Market

Primary users: Developers and engineering teams at SaaS companies and enterprises deploying AI customer support agents (e.g. chatbots, helpdesk automation). Industries: Software, B2B services, e-commerce. Geographic focus: North America and Europe tech hubs. TAM for AI security ~$5-10B by 2026; SAM for agent-specific tools several hundred million. Pain points center on regulatory compliance and breach prevention. High willingness to pay for enterprise-grade security preventing costly incidents.

⚔️ Competition

Medium. Direct competitors: 1. Lakera Guard (lakera.ai), 2. Guardrails AI (guardrailsai.com), 3. NeMo Guardrails (nvidia.com), 4. Prompt Security (prompt.security), 5. LangChain/LlamaIndex safety modules. Advantages: Targeted at support agents with unified screening of messages/docs/tools plus decision evidence logging; simpler one-call integration. Disadvantages: Newer entrant with potentially less ecosystem maturity and brand trust compared to established players; may require more custom tuning.

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