Engineering deep-dives, threat analysis, and product updates.
Guardrails tell the AI "don't do that." DLP prevents the data from ever reaching the AI in the first place. When your .env file contains production database credentials, the difference between "please don't read this" and "you physically cannot read this" is the difference between a suggestion and a security control. We built SecureMind around the second approach.
Prompt injection changes what the AI does. Data exfiltration steals what the AI sees. Most security tools only address one. Here's how we handle both.
Regex for speed, Pydantic rules for structure, local LLM for nuance. Early-return optimization means most prompts are classified in <5ms.
If your AI agent can read patient records, you have a HIPAA problem. We built breach classification for 13 fintech and healthcare breach types.
Base64 encoding, Unicode evasion, multi-step exfiltration, semantic smuggling. Our adversarial red-team suite tests every bypass technique we could think of.
How sitecustomize.py monkey-patches OpenAI, Anthropic, and LangChain SDKs at import time — monitoring every LLM call without touching your code.
Simon Willison's threat model, implemented. When all three conditions are met simultaneously, we block MCP tool calls with network access.