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AI Gateway Best Practices — Research 2026-08-10

Source: Enterprise AI governance chat analysis + industry research

Key Findings

1. Gateway Architecture Patterns

Pattern Pros Cons Best For
Envoy + ext_proc High performance, Go/Python plugins Complex setup Large enterprises
Kong API Gateway Plugin ecosystem, Lua/Go Higher latency Mid-size teams
NGINX + Lua Lightweight, fast Limited AI-specific features Small teams
Custom Go proxy Full control Maintenance burden Specialized needs

2. DLP Pipeline Components

Component Technology Purpose
Secret Scanner TruffleHog, detect-secrets Block API keys, tokens
PII Detector Presidio, custom regex Tokenize ФИО, паспорта
Code Detector AST parser (Semgrep) Route code to local LLM
Prompt Injection NeMo Guardrails, DeBERTa Block jailbreaks
License Checker Scancode, FOSSology Prevent GPL contamination

3. Data Classification (Russian Framework)

Level Description AI Access
Д-0 Public data Any provider
Д-1 Internal data Tokenized external OK
Д-2 Confidential Local LLM only
Д-3 Commercial secret (98-ФЗ) Local LLM + audit
Д-4 Personal data (152-ФЗ) Tokenized + audit
Д-5 State secret Absolute prohibition

4. Compliance Checklist

  • 152-ФЗ: PII tokenization before external send
  • 98-ФЗ: Commercial secrets never leave perimeter
  • Kaspersky: Follow corporate AI security guide
  • Audit: Log all prompts/responses to SIEM
  • DLP: Block secrets, PII, code in prompts
  • Anti-jailbreak: NeMo Guardrails or equivalent
  • License: SCA scan on generated code
  • Quotas: Per-department token budgets

Based on the analysis, the recommended enterprise stack is:

  1. Gateway: Envoy Proxy with ext_proc filter (Go plugin)
  2. DLP: Presidio (PII) + TruffleHog (secrets) + Semgrep (code)
  3. Anti-Jailbreak: NVIDIA NeMo Guardrails
  4. Audit: Langfuse or Arize Phoenix
  5. SIEM: MaxPatrol SIEM or RuSIEM
  6. Auth: Keycloak (OIDC) + Active Directory (Kerberos)
  7. RAG: Qdrant with ACL-aware indexing