SecurePrompt: Building a Pre-Flight Security Layer for Agentic AI

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.