As enterprises deploy agentic AI, everyone's building capabilities—but who's building the guardrails? When an autonomous agent generates a prompt containing AWS credentials, or a compromised data source injects malicious instructions, what stops that payload from reaching the LLM?
This session reveals how I built SecurePrompt, a pre-flight security scanner that intercepts prompts before they're sent to any AI model—addressing the critical blind spot at the boundary of autonomous AI systems.

You'll learn:

1. Real-world scenarios where credentials leak, prompt injections propagate, and PII compliance fails
2. Why I chose Go and rules-based detection for sub-10ms latency
3. Parallel scanning architecture for secrets, injection attacks, PII, and data exfiltration
4. Policy-as-code profiles for enterprise risk tolerances
5. HMAC-signed audit logs with causal traceability
6. Evolving from deterministic rules to LLM-powered semantic analysis

Leave with practical patterns for implementing security at the prompt boundary—the layer nobody else is building.