Build 'ShadowGuard AI'—a $25K/year SaaS + hardware platform that combines edge AI and federated learning to detect AI-driven cyber threats. The platform would: 1) Deploy 'ShadowNodes' (Raspberry Pi clusters) with edge AI to monitor network traffic for anomalies (e.g., 'IP #XYZ: 95% AI-driven attack—isolate'); 2) Provide a 'Threat Dashboard' showing real-time attack vectors (e.g., 'LLM phishing: 30% increase this week'); 3) Integrate with SIEM tools (e.g., Splunk) and government APIs (e.g., CISA) to auto-block threats; 4) Offer a 'Risk Exposure Score' showing potential fines/IP loss (e.g., 'GDPR fine: $2.1M—mitigate with ShadowGuard').

Mid-market companies (e.g., regional banks, healthcare providers) lack affordable tools to detect and mitigate AI-driven cyber threats. Current solutions (e.g., Darktrace, CrowdStrike) cost $100K+/year and focus on signature-based detection, not AI adversaries. The cost of inaction includes breaches ($4.45M avg. per incident), regulatory fines (e.g., GDPR: 4% of global revenue), and reputational damage. These companies cannot afford Anthropic’s enterprise-grade security tools.