Security & Data Privacy

Most people treat AI like a calculator that processes information locally. That mental model is wrong in ways that matter for security. We map exactly what happens when your team hits "send" on a prompt: data in transit, data at rest, retention policies, subprocessors, and the gap between what vendor marketing says and what the DPA actually commits to.

The same AI model can have dramatically different data handling depending on the product tier. Most employees don't know this distinction exists, which is how sensitive business data ends up in consumer-tier tools that use it for model training. We break down the three tiers and draw clear lines for what data belongs where.

Your employees are already using AI tools you don't know about. It's not malicious; it's a policy gap. Banning AI drives usage underground and trades visible risk for invisible risk. We teach a structured discovery process and a governance framework that channels AI usage toward approved tools instead of fighting a prohibition culture.

Most AI security conversations focus on data leaving your organization. Prompt injection is the reverse problem: attackers crafting inputs that manipulate AI systems you've deployed. We cover direct and indirect injection, real-world attack scenarios, and the defense-in-depth strategies we use when building AI-powered features for our own clients.

Your organization already has compliance requirements that apply to AI data handling. You just haven't mapped them yet. We walk through GDPR, CCPA, HIPAA, SOC 2, and the incoming EU AI Act, and help you build a pre-deployment compliance checklist specific to your regulatory landscape.

Map the data that flows through your most critical AI use cases. Identify exposure points, classify risk levels by data type, and leave the session with a complete data flow diagram ready for security review. Most teams discover gaps they didn't know existed.