Unlocking Secure AI: Small Language Models for Regulated Environments
Overview
As large language models (LLMs) like GPT-5 dominate headlines, organizations operating in regulated environments—such as government agencies, legal entities, and healthcare providers—are rightly cautious. Data privacy, compliance with evolving regulations, and sustainability are critical concerns that make cloud-based AI adoption risky or even infeasible.
This session explores Small Language Models (SLMs) as a practical, secure, and efficient alternative for AI adoption in privacy-sensitive domains.
What You'll Learn
🔐 Privacy-First AI Deployments
SLMs can run entirely on local infrastructure—no data ever leaves your organization.
⚙️ Efficiency Without Compromise
With significantly lower compute requirements, SLMs are lightweight, environmentally friendly, and cost-effective.
📄 Real-World Application: Redaction with NER
We’ll demonstrate a document redaction pipeline powered by open-source models. From document ingestion to Named Entity Recognition (NER) and annotation, everything runs locally—ideal for compliance-heavy use cases.
Who Should Attend?
This event is ideal for:
IT decision-makers and CIOs
Public sector and government technologists
Legal tech and compliance officers
Data governance professionals
AI engineers and developers prioritizing secure deployment