Cloud DLP proxies were built for a slower era of AI — batch scoring, buffered responses, and tolerance for 200–400ms of added round-trip latency. Modern engineering teams shipping streaming copilots and agent workflows do not have that luxury.
The cloud proxy tax
When every prompt hops to a third-party inspection cluster before reaching Anthropic, OpenAI, or Gemini, three things break:
- Streaming UX — tokens stall while the proxy buffers enough context to classify.
- Data residency — sensitive payloads leave the perimeter “for security,” which is the opposite of what compliance teams want.
- Developer trust — once latency is noticeable, teams bypass the proxy with personal API keys.
Local regex-driven tokenization
PromptGate evaluates prompts on the desktop client or edge engine before the bytes leave the machine. Regional PII patterns (Israeli Teudat Zehut, US SSN, EU national IDs) and secret detectors run in microseconds, then rewrite the outbound stream:
# Pseudocode — local masking before provider hop
prompt = inspect(raw_prompt)
if prompt.hits_block_policy:
raise PolicyDenied("sensitive data blocked")
outbound = redact(prompt, token="<PROMPTGATE_PII_REDACTED>")
stream_to_llm(outbound) # native token streaming, near-zero lag
Why this scales with agents
Autonomous tools amplify leak surface area. Edge redaction applies the same policy to every tool call — no per-framework SDK fork required. Point LangChain, CrewAI, or raw HTTPS at the gateway and the perimeter stays intact.
What “good” looks like
Security teams get audit counters and policy hits without content logs. Engineering keeps ChatGPT-class streaming. Neither side waits on a distant cloud hop. That is the bar for enterprise LLM governance in 2026.