Local AI

The rise of powerful, locally executable AI models like Google’s Gemma 4 challenges traditional enterprise security. These models can run on edge devices, bypassing traditional perimeter defenses and making network traffic monitoring ineffective for offline AI processing. This creates blind spots for security, especially in regulated industries requiring auditability. CISOs must shift focus from blocking models to controlling intent and system access, as identity platforms become the new firewall. Enterprises need new endpoint detection tools to monitor local AI inference and adapt security policies to acknowledge that compute execution is no longer solely cloud-based.

AI is crucial in programmatic advertising, but using third-party AI raises data security concerns. A growing trend is embedded, or local, AI, where models operate within an organization’s infrastructure, keeping sensitive data secure. This approach offers control, transparency, and auditability. Local AI enhances data governance, enables auditable model behavior, and supports applications like bidstream enrichment, pricing optimization, and fraud detection, all while complying with regulations. This balances performance with data stewardship and transparency.